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Smid A, Dominguez-Vega ZT, van Laar T, Oterdoom DLM, Absalom AR, van Egmond ME, Drost G, van Dijk JMC. Objective clinical registration of tremor, bradykinesia, and rigidity during awake stereotactic neurosurgery: a scoping review. Neurosurg Rev 2024; 47:81. [PMID: 38355824 PMCID: PMC10866747 DOI: 10.1007/s10143-024-02312-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 01/19/2024] [Accepted: 01/28/2024] [Indexed: 02/16/2024]
Abstract
Tremor, bradykinesia, and rigidity are incapacitating motor symptoms that can be suppressed with stereotactic neurosurgical treatment like deep brain stimulation (DBS) and ablative surgery (e.g., thalamotomy, pallidotomy). Traditionally, clinicians rely on clinical rating scales for intraoperative evaluation of these motor symptoms during awake stereotactic neurosurgery. However, these clinical scales have a relatively high inter-rater variability and rely on experienced raters. Therefore, objective registration (e.g., using movement sensors) is a reasonable extension for intraoperative assessment of tremor, bradykinesia, and rigidity. The main goal of this scoping review is to provide an overview of electronic motor measurements during awake stereotactic neurosurgery. The protocol was based on the PRISMA extension for scoping reviews. After a systematic database search (PubMed, Embase, and Web of Science), articles were screened for relevance. Hundred-and-three articles were subject to detailed screening. Key clinical and technical information was extracted. The inclusion criteria encompassed use of electronic motor measurements during stereotactic neurosurgery performed under local anesthesia. Twenty-three articles were included. These studies had various objectives, including correlating sensor-based outcome measures to clinical scores, identifying optimal DBS electrode positions, and translating clinical assessments to objective assessments. The studies were highly heterogeneous in device choice, sensor location, measurement protocol, design, outcome measures, and data analysis. This review shows that intraoperative quantification of motor symptoms is still limited by variable signal analysis techniques and lacking standardized measurement protocols. However, electronic motor measurements can complement visual evaluations and provide objective confirmation of correct placement of the DBS electrode and/or lesioning. On the long term, this might benefit patient outcomes and provide reliable outcome measures in scientific research.
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Affiliation(s)
- Annemarie Smid
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1 HPC AB71, 9713 GZ, Groningen, Netherlands.
| | - Zeus T Dominguez-Vega
- Department of Neurology, University Medical Center Groningen, University of Groningen, Hanzeplein 1 HPC AB71, 9713 GZ, Groningen, Netherlands
| | - Teus van Laar
- Department of Neurology, University Medical Center Groningen, University of Groningen, Hanzeplein 1 HPC AB71, 9713 GZ, Groningen, Netherlands
| | - D L Marinus Oterdoom
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1 HPC AB71, 9713 GZ, Groningen, Netherlands
| | - Anthony R Absalom
- Department of Anesthesiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1 HPC AB71, 9713 GZ, Groningen, Netherlands
| | - Martje E van Egmond
- Department of Neurology, University Medical Center Groningen, University of Groningen, Hanzeplein 1 HPC AB71, 9713 GZ, Groningen, Netherlands
| | - Gea Drost
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1 HPC AB71, 9713 GZ, Groningen, Netherlands
- Department of Neurology, University Medical Center Groningen, University of Groningen, Hanzeplein 1 HPC AB71, 9713 GZ, Groningen, Netherlands
| | - J Marc C van Dijk
- Department of Neurosurgery, University Medical Center Groningen, University of Groningen, Hanzeplein 1 HPC AB71, 9713 GZ, Groningen, Netherlands
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Guerra A, D'Onofrio V, Ferreri F, Bologna M, Antonini A. Objective measurement versus clinician-based assessment for Parkinson's disease. Expert Rev Neurother 2023; 23:689-702. [PMID: 37366316 DOI: 10.1080/14737175.2023.2229954] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 06/18/2023] [Accepted: 06/22/2023] [Indexed: 06/28/2023]
Abstract
INTRODUCTION Although clinician-based assessment through standardized clinical rating scales is currently the gold standard for quantifying motor impairment in Parkinson's disease (PD), it is not without limitations, including intra- and inter-rater variability and a degree of approximation. There is increasing evidence supporting the use of objective motion analyses to complement clinician-based assessment. Objective measurement tools hold significant potential for improving the accuracy of clinical and research-based evaluations of patients. AREAS COVERED The authors provide several examples from the literature demonstrating how different motion measurement tools, including optoelectronics, contactless and wearable systems allow for both the objective quantification and monitoring of key motor symptoms (such as bradykinesia, rigidity, tremor, and gait disturbances), and the identification of motor fluctuations in PD patients. Furthermore, they discuss how, from a clinician's perspective, objective measurements can help in various stages of PD management. EXPERT OPINION In our opinion, sufficient evidence supports the assertion that objective monitoring systems enable accurate evaluation of motor symptoms and complications in PD. A range of devices can be utilized not only to support diagnosis but also to monitor motor symptom during the disease progression and can become relevant in the therapeutic decision-making process.
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Affiliation(s)
- Andrea Guerra
- Parkinson and Movement Disorder Unit, Study Center on Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy
| | | | - Florinda Ferreri
- Unit of Neurology, Unit of Clinical Neurophysiology, Study Center of Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy
- Department of Clinical Neurophysiology, Kuopio University Hospital, University of Eastern Finland, Kuopio, Finland
| | - Matteo Bologna
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
- IRCCS Neuromed, Pozzilli, Italy
| | - Angelo Antonini
- Parkinson and Movement Disorder Unit, Study Center on Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy
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Sánchez-Fernández LP, Sánchez-Pérez LA, Concha-Gómez PD, Shaout A. Kinetic tremor analysis using wearable sensors and fuzzy inference systems in Parkinson's disease. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/27/2023]
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Antonini A, Reichmann H, Gentile G, Garon M, Tedesco C, Frank A, Falkenburger B, Konitsiotis S, Tsamis K, Rigas G, Kostikis N, Ntanis A, Pattichis C. Toward objective monitoring of Parkinson's disease motor symptoms using a wearable device: wearability and performance evaluation of PDMonitor ®. Front Neurol 2023; 14:1080752. [PMID: 37260606 PMCID: PMC10228366 DOI: 10.3389/fneur.2023.1080752] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 02/27/2023] [Indexed: 06/02/2023] Open
Abstract
Parkinson's disease (PD) is characterized by a variety of motor and non-motor symptoms. As disease progresses, fluctuations in the response to levodopa treatment may develop, along with emergence of freezing of gait (FoG) and levodopa induced dyskinesia (LiD). The optimal management of the motor symptoms and their complications, depends, principally, on the consistent detection of their course, leading to improved treatment decisions. During the last few years, wearable devices have started to be used in the clinical practice for monitoring patients' PD-related motor symptoms, during their daily activities. This work describes the results of 2 multi-site clinical studies (PDNST001 and PDNST002) designed to validate the performance and the wearability of a new wearable monitoring device, the PDMonitor®, in the detection of PD-related motor symptoms. For the studies, 65 patients with Parkinson's disease and 28 healthy individuals (controls) were recruited. Specifically, during the Phase I of the first study, participants used the monitoring device for 2-6 h in a clinic while neurologists assessed the exhibited parkinsonian symptoms every half hour using the Unified Parkinson's Disease Rating Scale (UPDRS) Part III, as well as the Abnormal Involuntary Movement Scale (AIMS) for dyskinesia severity assessment. The goal of Phase I was data gathering. On the other hand, during the Phase II of the first study, as well as during the second study (PDNST002), day-to-day variability was evaluated, with patients in the former and with control subjects in the latter. In both cases, the device was used for a number of days, with the subjects being unsupervised and free to perform any kind of daily activities. The monitoring device produced estimations of the severity of the majority of PD-related motor symptoms and their fluctuations. Statistical analysis demonstrated that the accuracy in the detection of symptoms and the correlation between their severity and the expert evaluations were high. As a result, the studies confirmed the effectiveness of the system as a continuous telemonitoring solution, easy to be used to facilitate decision-making for the treatment of patients with Parkinson's disease.
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Affiliation(s)
- Angelo Antonini
- Parkinson and Movement Disorders Unit, Study Center for Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy
| | - Heinz Reichmann
- Department of Neurology, University Hospital Carl Gustav Carus and Carl Gustav Carus Faculty of Medicine, Technische Universitat Dresden, Dresden, Germany
| | - Giovanni Gentile
- Parkinson and Movement Disorders Unit, Study Center for Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy
| | - Michela Garon
- Parkinson and Movement Disorders Unit, Study Center for Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy
| | - Chiara Tedesco
- Parkinson and Movement Disorders Unit, Study Center for Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy
| | - Anika Frank
- Department of Neurology, University Hospital Carl Gustav Carus and Carl Gustav Carus Faculty of Medicine, Technische Universitat Dresden, Dresden, Germany
- German Center for Neurodegenerative Diseases (DZNE), Dresden, Germany
| | - Bjoern Falkenburger
- Department of Neurology, University Hospital Carl Gustav Carus and Carl Gustav Carus Faculty of Medicine, Technische Universitat Dresden, Dresden, Germany
- German Center for Neurodegenerative Diseases (DZNE), Dresden, Germany
| | - Spyridon Konitsiotis
- Department of Neurology, University Hospital of Ioannina and Faculty of Medicine, School of Health Sciences, University of Ioannina, Ioannina, Greece
| | - Konstantinos Tsamis
- Department of Neurology, University Hospital of Ioannina and Faculty of Medicine, School of Health Sciences, University of Ioannina, Ioannina, Greece
| | | | | | | | - Constantinos Pattichis
- Department of Computer Science and Biomedical Engineering Research Centre, University of Cyprus, Nicosia, Cyprus
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Ravichandran V, Sadhu S, Convey D, Guerrier S, Chomal S, Dupre AM, Akbar U, Solanki D, Mankodiya K. iTex Gloves: Design and In-Home Evaluation of an E-Textile Glove System for Tele-Assessment of Parkinson's Disease. SENSORS (BASEL, SWITZERLAND) 2023; 23:2877. [PMID: 36991587 PMCID: PMC10054833 DOI: 10.3390/s23062877] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Revised: 02/23/2023] [Accepted: 02/27/2023] [Indexed: 06/19/2023]
Abstract
Parkinson's disease (PD) is a neurological progressive movement disorder, affecting more than 10 million people globally. PD demands a longitudinal assessment of symptoms to monitor the disease progression and manage the treatments. Existing assessment methods require patients with PD (PwPD) to visit a clinic every 3-6 months to perform movement assessments conducted by trained clinicians. However, periodic visits pose barriers as PwPDs have limited mobility, and healthcare cost increases. Hence, there is a strong demand for using telemedicine technologies for assessing PwPDs in remote settings. In this work, we present an in-home telemedicine kit, named iTex (intelligent Textile), which is a patient-centered design to carry out accessible tele-assessments of movement symptoms in people with PD. iTex is composed of a pair of smart textile gloves connected to a customized embedded tablet. iTex gloves are integrated with flex sensors on the fingers and inertial measurement unit (IMU) and have an onboard microcontroller unit with IoT (Internet of Things) capabilities including data storage and wireless communication. The gloves acquire the sensor data wirelessly to monitor various hand movements such as finger tapping, hand opening and closing, and other movement tasks. The gloves are connected to a customized tablet computer acting as an IoT device, configured to host a wireless access point, and host an MQTT broker and a time-series database server. The tablet also employs a patient-centered interface to guide PwPDs through the movement exam protocol. The system was deployed in four PwPDs who used iTex at home independently for a week. They performed the test independently before and after medication intake. Later, we performed data analysis of the in-home study and created a feature set. The study findings reported that the iTex gloves were capable to collect movement-related data and distinguish between pre-medication and post-medication cases in a majority of the participants. The IoT infrastructure demonstrated robust performance in home settings and offered minimum barriers for the assessment exams and the data communication with a remote server. In the post-study survey, all four participants expressed that the system was easy to use and poses a minimum barrier to performing the test independently. The present findings indicate that the iTex glove system has the potential for periodic and objective assessment of PD motor symptoms in remote settings.
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Affiliation(s)
- Vignesh Ravichandran
- Department of Electrical, Computer and Biomedical Engineering, University of Rhode Island, Kingston, RI 02881, USA
| | - Shehjar Sadhu
- Department of Electrical, Computer and Biomedical Engineering, University of Rhode Island, Kingston, RI 02881, USA
| | - Daniel Convey
- Department of Electrical, Computer and Biomedical Engineering, University of Rhode Island, Kingston, RI 02881, USA
| | - Sebastien Guerrier
- Department of Electrical, Computer and Biomedical Engineering, University of Rhode Island, Kingston, RI 02881, USA
| | - Shubham Chomal
- Department of Electrical, Computer and Biomedical Engineering, University of Rhode Island, Kingston, RI 02881, USA
| | - Anne-Marie Dupre
- Department of Physical Therapy, University of Rhode Island, Kingston, RI 02881, USA
| | - Umer Akbar
- Department of Neurology, Brown University, Rhode Island Hospital, Providence, RI 02903, USA
| | - Dhaval Solanki
- Department of Electrical, Computer and Biomedical Engineering, University of Rhode Island, Kingston, RI 02881, USA
| | - Kunal Mankodiya
- Department of Electrical, Computer and Biomedical Engineering, University of Rhode Island, Kingston, RI 02881, USA
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Vanmechelen I, Haberfehlner H, De Vleeschhauwer J, Van Wonterghem E, Feys H, Desloovere K, Aerts JM, Monbaliu E. Assessment of movement disorders using wearable sensors during upper limb tasks: A scoping review. Front Robot AI 2023; 9:1068413. [PMID: 36714804 PMCID: PMC9879015 DOI: 10.3389/frobt.2022.1068413] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 11/30/2022] [Indexed: 01/10/2023] Open
Abstract
Background: Studies aiming to objectively quantify movement disorders during upper limb tasks using wearable sensors have recently increased, but there is a wide variety in described measurement and analyzing methods, hampering standardization of methods in research and clinics. Therefore, the primary objective of this review was to provide an overview of sensor set-up and type, included tasks, sensor features and methods used to quantify movement disorders during upper limb tasks in multiple pathological populations. The secondary objective was to identify the most sensitive sensor features for the detection and quantification of movement disorders on the one hand and to describe the clinical application of the proposed methods on the other hand. Methods: A literature search using Scopus, Web of Science, and PubMed was performed. Articles needed to meet following criteria: 1) participants were adults/children with a neurological disease, 2) (at least) one sensor was placed on the upper limb for evaluation of movement disorders during upper limb tasks, 3) comparisons between: groups with/without movement disorders, sensor features before/after intervention, or sensor features with a clinical scale for assessment of the movement disorder. 4) Outcome measures included sensor features from acceleration/angular velocity signals. Results: A total of 101 articles were included, of which 56 researched Parkinson's Disease. Wrist(s), hand(s) and index finger(s) were the most popular sensor locations. Most frequent tasks were: finger tapping, wrist pro/supination, keeping the arms extended in front of the body and finger-to-nose. Most frequently calculated sensor features were mean, standard deviation, root-mean-square, ranges, skewness, kurtosis/entropy of acceleration and/or angular velocity, in combination with dominant frequencies/power of acceleration signals. Examples of clinical applications were automatization of a clinical scale or discrimination between a patient/control group or different patient groups. Conclusion: Current overview can support clinicians and researchers in selecting the most sensitive pathology-dependent sensor features and methodologies for detection and quantification of upper limb movement disorders and objective evaluations of treatment effects. Insights from Parkinson's Disease studies can accelerate the development of wearable sensors protocols in the remaining pathologies, provided that there is sufficient attention for the standardisation of protocols, tasks, feasibility and data analysis methods.
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Affiliation(s)
- Inti Vanmechelen
- Research Group for Neurorehabilitation (eNRGy), KU Leuven Bruges, Department of Rehabilitation Sciences, Bruges, Belgium,*Correspondence: Inti Vanmechelen,
| | - Helga Haberfehlner
- Research Group for Neurorehabilitation (eNRGy), KU Leuven Bruges, Department of Rehabilitation Sciences, Bruges, Belgium,Amsterdam Movement Sciences, Amsterdam UMC, Department of Rehabilitation Medicine, Amsterdam, Netherlands
| | - Joni De Vleeschhauwer
- Research Group for Neurorehabilitation (eNRGy), KU Leuven, Department of Rehabilitation Sciences, Leuven, Belgium
| | - Ellen Van Wonterghem
- Research Group for Neurorehabilitation (eNRGy), KU Leuven Bruges, Department of Rehabilitation Sciences, Bruges, Belgium
| | - Hilde Feys
- Research Group for Neurorehabilitation (eNRGy), KU Leuven, Department of Rehabilitation Sciences, Leuven, Belgium
| | - Kaat Desloovere
- Research Group for Neurorehabilitation (eNRGy), KU Leuven, Department of Rehabilitation Sciences, Pellenberg, Belgium
| | - Jean-Marie Aerts
- Division of Animal and Human Health Engineering, KU Leuven, Department of Biosystems, Measure, Model and Manage Bioresponses (M3-BIORES), Leuven, Belgium
| | - Elegast Monbaliu
- Research Group for Neurorehabilitation (eNRGy), KU Leuven Bruges, Department of Rehabilitation Sciences, Bruges, Belgium
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A new scheme for the development of IMU-based activity recognition systems for telerehabilitation. Med Eng Phys 2022; 108:103876. [DOI: 10.1016/j.medengphy.2022.103876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 08/10/2022] [Accepted: 08/21/2022] [Indexed: 11/24/2022]
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Home-Based Measurements of Dystonia in Cerebral Palsy Using Smartphone-Coupled Inertial Sensor Technology and Machine Learning: A Proof-of-Concept Study. SENSORS 2022; 22:s22124386. [PMID: 35746168 PMCID: PMC9231145 DOI: 10.3390/s22124386] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 05/30/2022] [Accepted: 06/07/2022] [Indexed: 02/06/2023]
Abstract
Accurate and reliable measurement of the severity of dystonia is essential for the indication, evaluation, monitoring and fine-tuning of treatments. Assessment of dystonia in children and adolescents with dyskinetic cerebral palsy (CP) is now commonly performed by visual evaluation either directly in the doctor’s office or from video recordings using standardized scales. Both methods lack objectivity and require much time and effort of clinical experts. Only a snapshot of the severity of dyskinetic movements (i.e., choreoathetosis and dystonia) is captured, and they are known to fluctuate over time and can increase with fatigue, pain, stress or emotions, which likely happens in a clinical environment. The goal of this study was to investigate whether it is feasible to use home-based measurements to assess and evaluate the severity of dystonia using smartphone-coupled inertial sensors and machine learning. Video and sensor data during both active and rest situations from 12 patients were collected outside a clinical setting. Three clinicians analyzed the videos and clinically scored the dystonia of the extremities on a 0–4 scale, following the definition of amplitude of the Dyskinesia Impairment Scale. The clinical scores and the sensor data were coupled to train different machine learning models using cross-validation. The average F1 scores (0.67 ± 0.19 for lower extremities and 0.68 ± 0.14 for upper extremities) in independent test datasets indicate that it is possible to detected dystonia automatically using individually trained models. The predictions could complement standard dyskinetic CP measures by providing frequent, objective, real-world assessments that could enhance clinical care. A generalized model, trained with data from other subjects, shows lower F1 scores (0.45 for lower extremities and 0.34 for upper extremities), likely due to a lack of training data and dissimilarities between subjects. However, the generalized model is reasonably able to distinguish between high and lower scores. Future research should focus on gathering more high-quality data and study how the models perform over the whole day.
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Rodríguez-Martín D, Cabestany J, Pérez-López C, Pie M, Calvet J, Samà A, Capra C, Català A, Rodríguez-Molinero A. A New Paradigm in Parkinson's Disease Evaluation With Wearable Medical Devices: A Review of STAT-ON TM. Front Neurol 2022; 13:912343. [PMID: 35720090 PMCID: PMC9202426 DOI: 10.3389/fneur.2022.912343] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 04/22/2022] [Indexed: 11/13/2022] Open
Abstract
In the past decade, the use of wearable medical devices has been a great breakthrough in clinical practice, trials, and research. In the Parkinson's disease field, clinical evaluation is time limited, and healthcare professionals need to rely on retrospective data collected through patients' self-filled diaries and administered questionnaires. As this often leads to inaccurate evaluations, a more objective system for symptom monitoring in a patient's daily life is claimed. In this regard, the use of wearable medical devices is crucial. This study aims at presenting a review on STAT-ONTM, a wearable medical device Class IIa, which provides objective information on the distribution and severity of PD motor symptoms in home environments. The sensor analyzes inertial signals, with a set of validated machine learning algorithms running in real time. The device was developed for 12 years, and this review aims at gathering all the results achieved within this time frame. First, a compendium of the complete journey of STAT-ONTM since 2009 is presented, encompassing different studies and developments in funded European and Spanish national projects. Subsequently, the methodology of database construction and machine learning algorithms design and development is described. Finally, clinical validation and external studies of STAT-ONTM are presented.
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Affiliation(s)
| | - Joan Cabestany
- Technical Research Centre for Dependency Care and Autonomous Living, Universitat Politecnica de Catalunya, Barcelona, Spain
| | - Carlos Pérez-López
- Department of Investigation, Consorci Sanitari Alt Penedès - Garraf, Vilanova i la Geltrú, Spain
| | - Marti Pie
- Sense4Care S.L., Cornellà de Llobregat, Spain
| | - Joan Calvet
- Sense4Care S.L., Cornellà de Llobregat, Spain
| | - Albert Samà
- Sense4Care S.L., Cornellà de Llobregat, Spain
| | | | - Andreu Català
- Technical Research Centre for Dependency Care and Autonomous Living, Universitat Politecnica de Catalunya, Barcelona, Spain
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Chandrabhatla AS, Pomeraniec IJ, Ksendzovsky A. Co-evolution of machine learning and digital technologies to improve monitoring of Parkinson's disease motor symptoms. NPJ Digit Med 2022; 5:32. [PMID: 35304579 PMCID: PMC8933519 DOI: 10.1038/s41746-022-00568-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 01/21/2022] [Indexed: 11/09/2022] Open
Abstract
Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor impairments such as tremor, bradykinesia, dyskinesia, and gait abnormalities. Current protocols assess PD symptoms during clinic visits and can be subjective. Patient diaries can help clinicians evaluate at-home symptoms, but can be incomplete or inaccurate. Therefore, researchers have developed in-home automated methods to monitor PD symptoms to enable data-driven PD diagnosis and management. We queried the US National Library of Medicine PubMed database to analyze the progression of the technologies and computational/machine learning methods used to monitor common motor PD symptoms. A sub-set of roughly 12,000 papers was reviewed that best characterized the machine learning and technology timelines that manifested from reviewing the literature. The technology used to monitor PD motor symptoms has advanced significantly in the past five decades. Early monitoring began with in-lab devices such as needle-based EMG, transitioned to in-lab accelerometers/gyroscopes, then to wearable accelerometers/gyroscopes, and finally to phone and mobile & web application-based in-home monitoring. Significant progress has also been made with respect to the use of machine learning algorithms to classify PD patients. Using data from different devices (e.g., video cameras, phone-based accelerometers), researchers have designed neural network and non-neural network-based machine learning algorithms to categorize PD patients across tremor, gait, bradykinesia, and dyskinesia. The five-decade co-evolution of technology and computational techniques used to monitor PD motor symptoms has driven significant progress that is enabling the shift from in-lab/clinic to in-home monitoring of PD symptoms.
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Affiliation(s)
- Anirudha S Chandrabhatla
- School of Medicine, University of Virginia Health Sciences Center, Charlottesville, VA, 22903, USA
| | - I Jonathan Pomeraniec
- Surgical Neurology Branch, National Institutes of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA. .,Department of Neurosurgery, University of Virginia Health Sciences Center, Charlottesville, VA, 22903, USA.
| | - Alexander Ksendzovsky
- Department of Neurosurgery, University of Maryland Medical System, Baltimore, MD, 21201, USA
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Ferreira GA, Teixeira JLS, Rosso ALZ, de Sá AMFM. On the classification of tremor signals into dyskinesia, Parkinsonian tremor, and Essential tremor by using machine learning techniques. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Hssayeni MD, Jimenez-Shahed J, Burack MA, Ghoraani B. Dyskinesia estimation during activities of daily living using wearable motion sensors and deep recurrent networks. Sci Rep 2021; 11:7865. [PMID: 33846387 PMCID: PMC8041801 DOI: 10.1038/s41598-021-86705-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Accepted: 03/09/2021] [Indexed: 02/01/2023] Open
Abstract
Levodopa-induced dyskinesias are abnormal involuntary movements experienced by the majority of persons with Parkinson's disease (PwP) at some point over the course of the disease. Choreiform as the most common phenomenology of levodopa-induced dyskinesias can be managed by adjusting the dose/frequency of PD medication(s) based on a PwP's motor fluctuations over a typical day. We developed a sensor-based assessment system to provide such information. We used movement data collected from the upper and lower extremities of 15 PwPs along with a deep recurrent model to estimate dyskinesia severity as they perform different activities of daily living (ADL). Subjects performed a variety of ADLs during a 4-h period while their dyskinesia severity was rated by the movement disorder experts. The estimated dyskinesia severity scores from our model correlated highly with the expert-rated scores (r = 0.87 (p < 0.001)), which was higher than the performance of linear regression that is commonly used for dyskinesia estimation (r = 0.81 (p < 0.001)). Our model provided consistent performance at different ADLs with minimum r = 0.70 (during walking) to maximum r = 0.84 (drinking) in comparison to linear regression with r = 0.00 (walking) to r = 0.76 (cutting food). These findings suggest that when our model is applied to at-home sensor data, it can provide an accurate picture of changes of dyskinesia severity facilitating effective medication adjustments.
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Affiliation(s)
- Murtadha D Hssayeni
- Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, 33431, USA
| | | | - Michelle A Burack
- Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Behnaz Ghoraani
- Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, 33431, USA.
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Daneault JF, Vergara-Diaz G, Parisi F, Admati C, Alfonso C, Bertoli M, Bonizzoni E, Carvalho GF, Costante G, Fabara EE, Fixler N, Golabchi FN, Growdon J, Sapienza S, Snyder P, Shpigelman S, Sudarsky L, Daeschler M, Bataille L, Sieberts SK, Omberg L, Moore S, Bonato P. Accelerometer data collected with a minimum set of wearable sensors from subjects with Parkinson's disease. Sci Data 2021; 8:48. [PMID: 33547309 PMCID: PMC7865022 DOI: 10.1038/s41597-021-00830-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Accepted: 11/24/2020] [Indexed: 12/22/2022] Open
Abstract
Parkinson's disease (PD) is a neurodegenerative disorder associated with motor and non-motor symptoms. Current treatments primarily focus on managing motor symptom severity such as tremor, bradykinesia, and rigidity. However, as the disease progresses, treatment side-effects can emerge such as on/off periods and dyskinesia. The objective of the Levodopa Response Study was to identify whether wearable sensor data can be used to objectively quantify symptom severity in individuals with PD exhibiting motor fluctuations. Thirty-one subjects with PD were recruited from 2 sites to participate in a 4-day study. Data was collected using 2 wrist-worn accelerometers and a waist-worn smartphone. During Days 1 and 4, a portion of the data was collected in the laboratory while subjects performed a battery of motor tasks as clinicians rated symptom severity. The remaining of the recordings were performed in the home and community settings. To our knowledge, this is the first dataset collected using wearable accelerometers with specific focus on individuals with PD experiencing motor fluctuations that is made available via an open data repository.
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Affiliation(s)
- Jean-Francois Daneault
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, Massachusetts, USA
- Department of Rehabilitation and Movement Sciences, Rutgers University, Newark, New Jersey, USA
| | - Gloria Vergara-Diaz
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, Massachusetts, USA
| | - Federico Parisi
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, Massachusetts, USA
| | - Chen Admati
- Intel Corporation, IT Advanced Analytics, Bethlehem, Israel
| | - Christina Alfonso
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Matilde Bertoli
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, Massachusetts, USA
| | - Edoardo Bonizzoni
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, Massachusetts, USA
| | - Gabriela Ferreira Carvalho
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, Massachusetts, USA
| | - Gianluca Costante
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, Massachusetts, USA
| | - Eric Eduardo Fabara
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, Massachusetts, USA
| | - Naama Fixler
- Intel Corporation, IT Advanced Analytics, Bethlehem, Israel
| | - Fatemah Noushin Golabchi
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, Massachusetts, USA
| | - John Growdon
- Department of Neurology, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Stefano Sapienza
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, Massachusetts, USA
| | - Phil Snyder
- Sage Bionetworks, Seattle, Washington, 98121, USA
| | | | - Lewis Sudarsky
- Department of Neurology, Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | | | | | | | | | - Steven Moore
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- School of Engineering and Technology, Central Queensland University, Rockhampton, Australia
| | - Paolo Bonato
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Boston, Massachusetts, USA.
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Cambridge, Massachusetts, USA.
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Sato K, Mano T, Iwata A, Toda T. Autocorrelation-based method to identify disordered rhythm in Parkinson's disease tasks: A novel approach applicable to multimodal devices. PLoS One 2020; 15:e0238486. [PMID: 33031372 PMCID: PMC7544077 DOI: 10.1371/journal.pone.0238486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 09/23/2020] [Indexed: 11/18/2022] Open
Abstract
Objective We aim to propose a novel method of evaluating the degree of rhythmic irregularity during repetitive tasks in Parkinson’s disease (PD) by using autocorrelation to extract serial perturbation in the periodicity of body part movements as recorded by objective devices. Methods We used publicly distributed sequential joint movement data recorded during a leg agility task or pronation-supination task. The sequences of body part trajectory were processed to extract their short-time autocorrelation (STACF) matrices; the sequences of single task conducted by participants were then divided into two clusters according to their similarity in terms of their STACF representation. The Unified Parkinson’s Disease Rating Scale sub-score rated for each task was compared with cluster membership to obtain the area under the curve (AUC) to evaluate the discrimination performance of the clustering. We compared the AUC with those obtained from the clustering of the raw sequence or short-time Fourier transform (STFT). Results In classifying the pose estimator-based trajectory data of the knee during the leg agility task, the AUC was the highest when the STACF sequence was used for clustering instead of other types of sequences with up to 0.815, being comparable to the results reported in the original analysis of the data using an approach different from ours. In addition, in classifying another dataset of accelerometer-based trajectory data of the wrist during a pronation-supination task, the AUC was again highest up to 0.785 when clustering was performed using the STACF rather than other types of sequence. Conclusion Our autocorrelation-based method achieved a fair performance in detecting sequences with irregular rhythm, suggesting that it might be used as another evaluation strategy that is potentially widely applicable to qualify the disordered rhythm of PD regardless of the kinds of task or the modality of devices, although further refinement is needed.
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Affiliation(s)
- Kenichiro Sato
- Department of Neurology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
- * E-mail: (KS); (AI)
| | - Tatsuo Mano
- Department of Neurology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Atsushi Iwata
- Department of Neurology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
- Department of Neurology, Tokyo Metropolitan Geriatric Medical Center Hospital, Tokyo, Japan
- * E-mail: (KS); (AI)
| | - Tatsushi Toda
- Department of Neurology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
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15
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Parkinsonian Symptoms, Not Dyskinesia, Negatively Affect Active Life Participation of Dyskinetic Patients with Parkinson's Disease. Tremor Other Hyperkinet Mov (N Y) 2020; 10:20. [PMID: 32775034 PMCID: PMC7394214 DOI: 10.5334/tohm.403] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
Background: The impact of slight-to-moderate levodopa-induced dyskinesia (LID) on the level of participation in active life in patients with Parkinson’s disease (PD) has never been objectively determined. Methods: Levels of LID, tremor and bradykinesia were measured during best-ON state in 121 patients diagnosed with PD and having peak-dose LID using inertial sensors positioned on each body limb. Rigidity and postural instability were assessed using clinical evaluations. Cognition and depression were assessed using the MMSE and the GDS-15. Participation in active life was assessed in patients and in 69 healthy controls using the Activity Card Sort (ACS), which measures levels of activity engagement and activities affected by the symptomatology. Outcome measures were compared between patients and controls using ANCOVA, controlling for age or Wilcoxon-Mann-Whitney tests. Spearman correlations and multivariate analyses were then performed between symptomatology and ACS scores. Results: Patients had significantly lower activity engagement than controls and had significantly affected activities. LID was neither associated with activity engagement nor affected activities. Higher levels of tremor, postural instability, cognitive decline and depression were associated with lower activity engagement and higher affected activities. Multivariate analyses revealed that only tremor, postural instability and depression accounted significantly in the variances of these variables. Discussion: Slight-to-moderate LID had little impact compared to other symptoms on the level of participation in active life, suggesting that other symptoms should remain the treatment priority to maintain the level of participation of patients in an active lifestyle.
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16
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Aghanavesi S, Westin J, Bergquist F, Nyholm D, Askmark H, Aquilonius SM, Constantinescu R, Medvedev A, Spira J, Ohlsson F, Thomas I, Ericsson A, Buvarp DJ, Memedi M. A multiple motion sensors index for motor state quantification in Parkinson's disease. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 189:105309. [PMID: 31982667 DOI: 10.1016/j.cmpb.2019.105309] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 12/22/2019] [Accepted: 12/29/2019] [Indexed: 06/10/2023]
Abstract
AIM To construct a Treatment Response Index from Multiple Sensors (TRIMS) for quantification of motor state in patients with Parkinson's disease (PD) during a single levodopa dose. Another aim was to compare TRIMS to sensor indexes derived from individual motor tasks. METHOD Nineteen PD patients performed three motor tests including leg agility, pronation-supination movement of hands, and walking in a clinic while wearing inertial measurement unit sensors on their wrists and ankles. They performed the tests repeatedly before and after taking 150% of their individual oral levodopa-carbidopa equivalent morning dose.Three neurologists blinded to treatment status, viewed patients' videos and rated their motor symptoms, dyskinesia, overall motor state based on selected items of Unified PD Rating Scale (UPDRS) part III, Dyskinesia scale, and Treatment Response Scale (TRS). To build TRIMS, out of initially 178 extracted features from upper- and lower-limbs data, 39 features were selected by stepwise regression method and were used as input to support vector machines to be mapped to mean reference TRS scores using 10-fold cross-validation method. Test-retest reliability, responsiveness to medication, and correlation to TRS as well as other UPDRS items were evaluated for TRIMS. RESULTS The correlation of TRIMS with TRS was 0.93. TRIMS had good test-retest reliability (ICC = 0.83). Responsiveness of the TRIMS to medication was good compared to TRS indicating its power in capturing the treatment effects. TRIMS was highly correlated to dyskinesia (R = 0.85), bradykinesia (R = 0.84) and gait (R = 0.79) UPDRS items. Correlation of sensor index from the upper-limb to TRS was 0.89. CONCLUSION Using the fusion of upper- and lower-limbs sensor data to construct TRIMS provided accurate PD motor states estimation and responsive to treatment. In addition, quantification of upper-limb sensor data during walking test provided strong results.
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Affiliation(s)
| | - Jerker Westin
- Department of Computer Engineering, Dalarna University, Falun, Sweden.
| | - Filip Bergquist
- Department of Pharmacology at Institute of Neuroscience and Physiology, University of Gothenburg, Gothenburg, Sweden.
| | - Dag Nyholm
- Department of Neuroscience, Neurology at Uppsala University, Uppsala, Sweden.
| | - Håkan Askmark
- Department of Neuroscience, Neurology at Uppsala University, Uppsala, Sweden.
| | | | - Radu Constantinescu
- Department of Clinical Neuroscience, University of Gothenburg, Gothenburg, Sweden.
| | - Alexander Medvedev
- Department of Information Technology, at Uppsala University, Uppsala, Sweden.
| | | | | | - Ilias Thomas
- Department of Statistics, Dalarna University, Falun, Sweden.
| | | | - Dongni Johansson Buvarp
- Department of Clinical Neuroscience and Rehabilitation, University of Gothenburg, Gothenburg, Sweden.
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17
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Assessment of the Status of Patients with Parkinson's Disease Using Neural Networks and Mobile Phone Sensors. Diagnostics (Basel) 2020; 10:diagnostics10040214. [PMID: 32290633 PMCID: PMC7235735 DOI: 10.3390/diagnostics10040214] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2020] [Revised: 04/09/2020] [Accepted: 04/11/2020] [Indexed: 11/16/2022] Open
Abstract
Parkinson's disease (PD) is one of the most common chronic neurological diseases and one of the significant causes of disability for middle-aged and elderly people. Monitoring the patient's condition and its compliance is the key to the success of the correction of the main clinical manifestations of PD, including the almost inevitable modification of the clinical picture of the disease against the background of prolonged dopaminergic therapy. In this article, we proposed an approach to assessing the condition of patients with PD using deep recurrent neural networks, trained on data measured using mobile phones. The data was received in two modes: background (data from the phone's sensors) and interactive (data directly entered by the user). For the classification of the patient's condition, we built various models of the neural network. Testing of these models showed that the most efficient was a recurrent network with two layers. The results of the experiment show that with a sufficient amount of the training sample, it is possible to build a neural network that determines the condition of the patient according to the data from the mobile phone sensors with a high probability.
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18
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Erb MK, Karlin DR, Ho BK, Thomas KC, Parisi F, Vergara-Diaz GP, Daneault JF, Wacnik PW, Zhang H, Kangarloo T, Demanuele C, Brooks CR, Detheridge CN, Shaafi Kabiri N, Bhangu JS, Bonato P. mHealth and wearable technology should replace motor diaries to track motor fluctuations in Parkinson's disease. NPJ Digit Med 2020; 3:6. [PMID: 31970291 PMCID: PMC6969057 DOI: 10.1038/s41746-019-0214-x] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2019] [Accepted: 12/05/2019] [Indexed: 11/18/2022] Open
Abstract
Accurately monitoring motor and non-motor symptoms as well as complications in people with Parkinson's disease (PD) is a major challenge, both during clinical management and when conducting clinical trials investigating new treatments. A variety of strategies have been relied upon including questionnaires, motor diaries, and the serial administration of structured clinical exams like part III of the MDS-UPDRS. To evaluate the potential use of mobile and wearable technologies in clinical trials of new pharmacotherapies targeting PD symptoms, we carried out a project (project BlueSky) encompassing four clinical studies, in which 60 healthy volunteers (aged 23-69; 33 females) and 95 people with PD (aged 42-80; 37 females; years since diagnosis 1-24 years; Hoehn and Yahr 1-3) participated and were monitored in either a laboratory environment, a simulated apartment, or at home and in the community. In this paper, we investigated (i) the utility and reliability of self-reports for describing motor fluctuations; (ii) the agreement between participants and clinical raters on the presence of motor complications; (iii) the ability of video raters to accurately assess motor symptoms, and (iv) the dynamics of tremor, dyskinesia, and bradykinesia as they evolve over the medication cycle. Future papers will explore methods for estimating symptom severity based on sensor data. We found that 38% of participants who were asked to complete an electronic motor diary at home missed ~25% of total possible entries and otherwise made entries with an average delay of >4 h. During clinical evaluations by PD specialists, self-reports of dyskinesia were marked by ~35% false negatives and 15% false positives. Compared with live evaluation, the video evaluation of part III of the MDS-UPDRS significantly underestimated the subtle features of tremor and extremity bradykinesia, suggesting that these aspects of the disease may be underappreciated during remote assessments. On the other hand, live and video raters agreed on aspects of postural instability and gait. Our results highlight the significant opportunity for objective, high-resolution, continuous monitoring afforded by wearable technology to improve upon the monitoring of PD symptoms.
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Affiliation(s)
- M. Kelley Erb
- Early Clinical Development, Pfizer, Inc, Cambridge, MA USA
| | - Daniel R. Karlin
- Early Clinical Development, Pfizer, Inc, Cambridge, MA USA
- Department of Psychiatry, Tufts University School of Medicine, Boston, MA USA
| | - Bryan K. Ho
- Department of Neurology, Tufts University School of Medicine, Boston, MA USA
| | - Kevin C. Thomas
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA USA
| | - Federico Parisi
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Charlestown, MA USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA USA
| | - Gloria P. Vergara-Diaz
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Charlestown, MA USA
| | - Jean-Francois Daneault
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Charlestown, MA USA
| | - Paul W. Wacnik
- Early Clinical Development, Pfizer, Inc, Cambridge, MA USA
| | - Hao Zhang
- Early Clinical Development, Pfizer, Inc, Cambridge, MA USA
| | | | | | - Chris R. Brooks
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA USA
| | - Craig N. Detheridge
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA USA
| | - Nina Shaafi Kabiri
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA USA
| | - Jaspreet S. Bhangu
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA USA
| | - Paolo Bonato
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Spaulding Rehabilitation Hospital, Charlestown, MA USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA USA
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19
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Owens AP, Ballard C, Beigi M, Kalafatis C, Brooker H, Lavelle G, Brønnick KK, Sauer J, Boddington S, Velayudhan L, Aarsland D. Implementing Remote Memory Clinics to Enhance Clinical Care During and After COVID-19. Front Psychiatry 2020; 11:579934. [PMID: 33061927 PMCID: PMC7530252 DOI: 10.3389/fpsyt.2020.579934] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Accepted: 08/31/2020] [Indexed: 12/15/2022] Open
Abstract
Social isolation is likely to be recommended for older adults due to COVID-19, with ongoing reduced clinical contact suggested for this population. This has increased the need for remote memory clinics, we therefore review the literature, current practices and guidelines on organizing such remote memory clinics, focusing on assessment of cognition, function and other relevant measurements, proposing a novel pathway based on three levels of complexity: simple telephone or video-based interviews and testing using available tests (Level 1), digitized and validated methods based on standard pen-and-paper tests and scales (Level 2), and finally fully digitized cognitive batteries and remote measurement technologies (RMTs, Level 3). Pros and cons of these strategies are discussed. Remotely collected data negates the need for frail patients or carers to commute to clinic and offers valuable insights into progression over time, as well as treatment responses to therapeutic interventions, providing a more realistic and contextualized environment for data-collection. Notwithstanding several challenges related to internet access, computer skills, limited evidence base and regulatory and data protection issues, digital biomarkers collected remotely have significant potential for diagnosis and symptom management in older adults and we propose a framework and pathway for how technologies can be implemented to support remote memory clinics. These platforms are also well-placed for administration of digital cognitive training and other interventions. The individual, societal and public/private costs of COVID-19 are high and will continue to rise for some time but the challenges the pandemic has placed on memory services also provides an opportunity to embrace novel approaches. Remote memory clinics' financial, logistical, clinical and practical benefits have been highlighted by COVID-19, supporting their use to not only be maintained when social distancing legislation is lifted but to be devoted extra resources and attention to fully potentiate this valuable arm of clinical assessment and care.
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Affiliation(s)
- Andrew P Owens
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Clive Ballard
- The University of Exeter Medical School, The University of Exeter, Exeter, United Kingdom
| | - Mazda Beigi
- Psychological Medicine and Older Adults, South London & Maudsley NHS Foundation Trust, London, United Kingdom
| | - Chris Kalafatis
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,Psychological Medicine and Older Adults, South London & Maudsley NHS Foundation Trust, London, United Kingdom
| | - Helen Brooker
- The University of Exeter Medical School, The University of Exeter, Exeter, United Kingdom.,Ecog Pro Ltd, Bristol, United Kingdom
| | - Grace Lavelle
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Kolbjørn K Brønnick
- SESAM-Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway.,Department of Public Health, Faculty of Health Sciences, University of Stavanger, Stavanger, Norway
| | - Justin Sauer
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,Psychological Medicine and Older Adults, South London & Maudsley NHS Foundation Trust, London, United Kingdom
| | - Steve Boddington
- Psychological Medicine and Older Adults, South London & Maudsley NHS Foundation Trust, London, United Kingdom
| | - Latha Velayudhan
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Dag Aarsland
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,Psychological Medicine and Older Adults, South London & Maudsley NHS Foundation Trust, London, United Kingdom.,SESAM-Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway
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20
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Analysis and Classification of Motor Dysfunctions in Arm Swing in Parkinson’s Disease. ELECTRONICS 2019. [DOI: 10.3390/electronics8121471] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Due to increasing life expectancy, the number of age-related diseases with motor dysfunctions (MD) such as Parkinson’s disease (PD) is also increasing. The assessment of MD is visual and therefore subjective. For this reason, many researchers are working on an objective evaluation. Most of the research on gait analysis deals with the analysis of leg movement. The analysis of arm movement is also important for the assessment of gait disorders. This work deals with the analysis of the arm swing by using wearable inertial sensors. A total of 250 records of 39 different subjects were used for this task. Fifteen subjects of this group had motor dysfunctions (MD). The subjects had to perform the standardized Timed Up and Go (TUG) test to ensure that the recordings were comparable. The data were classified by using the wavelet transformation, a convolutional neural network (CNN), and weight voting. During the classification, single signals, as well as signal combinations were observed. We were able to detect MD with an accuracy of 93.4% by using the wavelet transformation and a three-layer CNN architecture.
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21
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Xia Y, Yao Z, Ye Q, Cheng N. A Dual-Modal Attention-Enhanced Deep Learning Network for Quantification of Parkinson's Disease Characteristics. IEEE Trans Neural Syst Rehabil Eng 2019; 28:42-51. [PMID: 31603824 DOI: 10.1109/tnsre.2019.2946194] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
It is well known that most patients with Parkinson's disease (PD) have different degree of movement disorders, such as shuffling, festination and akinetic episodes, which could degenerate the life quality of PD patients. Therefore, it is very useful to develop a computerized tool to provide an objective evaluation of PD patients' gait. In this study, we implemented a novel gait evaluating approach to provide not only a binary classification of PD gaits and normal walking, but also a quantification of the PD gaits to relate them to the PD severity level. The proposed system is a dual-modal deep-learning-based model, where left and right gait is modeled separately by a convolutional neural network (CNN) followed by an attention-enhanced long short-term memory (LSTM) network. The left and right samples for model training and testing were segmented sequentially from multiple 1D vertical ground reaction force (VGRF) signals according to the detected gait cycle. Experimental results indicate that our model can provide state-of-the-art performance in terms of classification accuracy. It is expected that the proposed model can be a useful gait assistance to provide a quantitative evaluation of PD gaits with high confidence and accuracy if trained suitably.
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22
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Borzì L, Varrecchia M, Olmo G, Artusi CA, Fabbri M, Rizzone MG, Romagnolo A, Zibetti M, Lopiano L. Home monitoring of motor fluctuations in Parkinson’s disease patients. ACTA ACUST UNITED AC 2019. [DOI: 10.1007/s40860-019-00086-x] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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23
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Delamarre A, Tison F, Li Q, Galitzky M, Rascol O, Bezard E, Meissner WG. Assessment of plasma creatine kinase as biomarker for levodopa-induced dyskinesia in Parkinson's disease. J Neural Transm (Vienna) 2019; 126:789-793. [PMID: 31098725 DOI: 10.1007/s00702-019-02015-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Accepted: 05/11/2019] [Indexed: 10/26/2022]
Abstract
We tested in a translational approach the usefulness of plasma creatine kinase (CK) as an objective biomarker for levodopa-induced dyskinesia (LID). Plasma CK levels were measured in five dyskinetic parkinsonian non-human primates (NHP) and in ten PD patients with LID who participated in a treatment trial with simvastatin. Plasma CK levels were increased in dyskinetic NHP and correlated with LID severity while they were not affected by LID severity in PD patients.
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Affiliation(s)
- Anna Delamarre
- Service de Neurologie, Hôpital Pellegrin, CHU de Bordeaux, 33000, Bordeaux, France.,Institut des Maladies Neurodégénératives, Université de Bordeaux, UMR 5293, 146 rue Léo Saignat, 33000, Bordeaux Cedex, France.,CNRS, Institut des Maladies Neurodégénératives, UMR 5293, 33000, Bordeaux, France
| | - François Tison
- Service de Neurologie, Hôpital Pellegrin, CHU de Bordeaux, 33000, Bordeaux, France.,Institut des Maladies Neurodégénératives, Université de Bordeaux, UMR 5293, 146 rue Léo Saignat, 33000, Bordeaux Cedex, France.,CNRS, Institut des Maladies Neurodégénératives, UMR 5293, 33000, Bordeaux, France
| | - Qin Li
- Institute of Laboratory Animal Sciences, China Academy of Medical Sciences, Beijing, China.,Motac Neuroscience, Manchester, UK
| | | | - Olivier Rascol
- CIC Toulouse, Toulouse, France.,Départements de Pharmacologie Clinique et Neurosciences, INSERM CIC9302, CHU de Toulouse, Toulouse, France.,Service de Pharmacologie, Faculté de Médecine, CHU de Toulouse, Université de Toulouse, Toulouse, France
| | - Erwan Bezard
- Service de Neurologie, Hôpital Pellegrin, CHU de Bordeaux, 33000, Bordeaux, France.,Institut des Maladies Neurodégénératives, Université de Bordeaux, UMR 5293, 146 rue Léo Saignat, 33000, Bordeaux Cedex, France.,CNRS, Institut des Maladies Neurodégénératives, UMR 5293, 33000, Bordeaux, France.,Institute of Laboratory Animal Sciences, China Academy of Medical Sciences, Beijing, China.,Motac Neuroscience, Manchester, UK
| | - Wassilios G Meissner
- Service de Neurologie, Hôpital Pellegrin, CHU de Bordeaux, 33000, Bordeaux, France. .,Institut des Maladies Neurodégénératives, Université de Bordeaux, UMR 5293, 146 rue Léo Saignat, 33000, Bordeaux Cedex, France. .,CNRS, Institut des Maladies Neurodégénératives, UMR 5293, 33000, Bordeaux, France. .,Department Medicine, University of Otago, Christchurch, New Zealand. .,New Zealand Brain Research Institute, Christchurch, New Zealand.
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Goubault E, Nguyen HP, Bogard S, Blanchet PJ, Bézard E, Vincent C, Sarna J, Monchi O, Duval C. Remnants of Cardinal Symptoms of Parkinson's Disease, Not Dyskinesia, Are Problematic for Dyskinetic Patients Performing Activities of Daily Living. Front Neurol 2019; 10:256. [PMID: 30967832 PMCID: PMC6440171 DOI: 10.3389/fneur.2019.00256] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Accepted: 02/26/2019] [Indexed: 11/18/2022] Open
Abstract
Introduction: The impact of levodopa-induced dyskinesia (LID) on the daily lives of patients with Parkinson's disease (PD) remains to be determined. Furthermore, evidence suggests that cardinal motor symptoms of PD may coexist with LID, but their impact on activities of daily living (ADL) relative to LID is not known. This cross-sectional study aimed at determining the effect of LID and cardinal motor symptoms of PD on ADL in patients who were experiencing peak-dose choreic-type LID. Method: One hundred and twenty-one patients diagnosed with PD known to experience choreic-type LID were recruited for the study. Patients were asked to perform a set of ADL. Levels of LID, tremor, bradykinesia, and freezing of gait (FoG) were measured using 17 inertial sensors design to capture full body movements, while rigidity, and postural instability were assessed using clinical evaluations. Cognition was also assessed using the mini-mental state examination. Success criteria were set for each ADL using the time needed to perform the task and errors measured in 69 age-gender-matched healthy controls. Binary logistic regressions were used to identify symptoms influencing success or failure for each activity. Receiver operating characteristic curves were computed on each significant symptom, and Youden indexes were calculated to determine the critical level of symptomatology at which the performance significantly changed. Results: Results show that 97.7% of patients who presented with LID during the experiment also presented with at least one cardinal motor symptom. On average, patients took more time and did more errors during ADL. Multivariate analyses revealed that for the great majority of ADL, LID were not associated with worsening of performance; however, postural instability, tremor, rigidity, and cognitive decline significantly decreased the odds of success. Conclusions: Residual symptoms of PD, such as tremor, rigidity, and postural instability still present at peak-dose were more problematic than LID in the performance of ADL for patients experiencing slight-to-moderate LID. We also found that cognitive decline was associated with decreased performance in certain tasks. Therefore, a strategy using lower doses of medication to manage LID may be counterproductive since it would not address most of these symptoms already present in patients.
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Affiliation(s)
- Etienne Goubault
- Département des Sciences de l'Activité Physique, Université du Québec à Montréal, Montréal, QC, Canada.,Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, QC, Canada
| | - Hung P Nguyen
- Département des Sciences de l'Activité Physique, Université du Québec à Montréal, Montréal, QC, Canada.,Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, QC, Canada
| | - Sarah Bogard
- Département des Sciences de l'Activité Physique, Université du Québec à Montréal, Montréal, QC, Canada.,Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, QC, Canada
| | - Pierre J Blanchet
- Département de Stomatologie, Faculté de Médecine Dentaire, Université de Montréal, Montréal, QC, Canada.,Département de Médecine, CHU Montréal, Montréal, QC, Canada
| | - Erwan Bézard
- Laboratoire de Neurophysiologie, Université de Bordeaux, Institut des Maladies Neurodégénératives, Bordeaux, France.,Unité Mixte de Recherche 5293, Centre National de la Recherche Scientifique, Institut des Maladies Neurodégénératives, Bordeaux, France
| | - Claude Vincent
- Département de Réadaptation, Faculté de Médecine, Université Laval, Quebec, QC, Canada
| | - Justyna Sarna
- Department of Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Oury Monchi
- Department of Clinical Neurosciences, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Christian Duval
- Département des Sciences de l'Activité Physique, Université du Québec à Montréal, Montréal, QC, Canada.,Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, QC, Canada
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Aghanavesi S, Bergquist F, Nyholm D, Senek M, Memedi M. Motion Sensor-Based Assessment of Parkinson's Disease Motor Symptoms During Leg Agility Tests: Results From Levodopa Challenge. IEEE J Biomed Health Inform 2019; 24:111-119. [PMID: 30763248 DOI: 10.1109/jbhi.2019.2898332] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Parkinson's disease (PD) is a degenerative, progressive disorder of the central nervous system that mainly affects motor control. The aim of this study was to develop data-driven methods and test their clinimetric properties to detect and quantify PD motor states using motion sensor data from leg agility tests. Nineteen PD patients were recruited in a levodopa single dose challenge study. PD patients performed leg agility tasks while wearing motion sensors on their lower extremities. Clinical evaluation of video recordings was performed by three movement disorder specialists who used four items from the motor section of the unified PD rating scale (UPDRS), the treatment response scale (TRS) and a dyskinesia score. Using the sensor data, spatiotemporal features were calculated and relevant features were selected by feature selection. Machine learning methods like support vector machines (SVM), decision trees, and linear regression, using ten-fold cross validation were trained to predict motor states of the patients. SVM showed the best convergence validity with correlation coefficients of 0.81 to TRS, 0.83 to UPDRS #31 (body bradykinesia and hypokinesia), 0.78 to SUMUPDRS (the sum of the UPDRS items: #26-leg agility, #27-arising from chair, and #29-gait), and 0.67 to dyskinesia. Additionally, the SVM-based scores had similar test-retest reliability in relation to clinical ratings. The SVM-based scores were less responsive to treatment effects than the clinical scores, particularly with regards to dyskinesia. In conclusion, the results from this study indicate that using motion sensors during leg agility tests may lead to valid and reliable objective measures of PD motor symptoms.
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Li MH, Mestre TA, Fox SH, Taati B. Vision-based assessment of parkinsonism and levodopa-induced dyskinesia with pose estimation. J Neuroeng Rehabil 2018; 15:97. [PMID: 30400914 PMCID: PMC6219082 DOI: 10.1186/s12984-018-0446-z] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Accepted: 10/18/2018] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Despite the effectiveness of levodopa for treatment of Parkinson's disease (PD), prolonged usage leads to development of motor complications, most notably levodopa-induced dyskinesia (LID). Persons with PD and their physicians must regularly modify treatment regimens and timing for optimal relief of symptoms. While standardized clinical rating scales exist for assessing the severity of PD symptoms, they must be administered by a trained medical professional and are inherently subjective. Computer vision is an attractive, non-contact, potential solution for automated assessment of PD, made possible by recent advances in computational power and deep learning algorithms. The objective of this paper was to evaluate the feasibility of vision-based assessment of parkinsonism and LID using pose estimation. METHODS Nine participants with PD and LID completed a levodopa infusion protocol, where symptoms were assessed at regular intervals using the Unified Dyskinesia Rating Scale (UDysRS) and Unified Parkinson's Disease Rating Scale (UPDRS). Movement trajectories of individual joints were extracted from videos of PD assessment using Convolutional Pose Machines, a pose estimation algorithm built with deep learning. Features of the movement trajectories (e.g. kinematic, frequency) were used to train random forests to detect and estimate the severity of parkinsonism and LID. Communication and drinking tasks were used to assess LID, while leg agility and toe tapping tasks were used to assess parkinsonism. Feature sets from tasks were also combined to predict total UDysRS and UPDRS Part III scores. RESULTS For LID, the communication task yielded the best results (detection: AUC = 0.930, severity estimation: r = 0.661). For parkinsonism, leg agility had better results for severity estimation (r = 0.618), while toe tapping was better for detection (AUC = 0.773). UDysRS and UPDRS Part III scores were predicted with r = 0.741 and 0.530, respectively. CONCLUSION The proposed system provides insight into the potential of computer vision and deep learning for clinical application in PD and demonstrates promising performance for the future translation of deep learning to PD clinical practices. Convenient and objective assessment of PD symptoms will facilitate more frequent touchpoints between patients and clinicians, leading to better tailoring of treatment and quality of care.
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Affiliation(s)
- Michael H. Li
- Toronto Rehabilitation Institute, University Health Network, 550 University Ave, Toronto, ON M5G 2A2 Canada
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, 164 College St, Room 407, Toronto, ON M5S 3G9 Canada
| | - Tiago A. Mestre
- Edmond J. Safra Program in Parkinson’s Disease, Toronto Western Hospital, University Health Network, 399 Bathurst St, Toronto, ON M5T 2S8 Canada
- The Ottawa Hospital Research Institute, 1053 Carling Ave, Ottawa, ON K1Y 4E9 Canada
- Division of Neurology, Department of Medicine, 1053 Carling Ave, Ottawa, ON K1Y 4E9 Canada
- Division of Neurology, University of Toronto, Suite RFE 3-805, 200 Elizabeth St, Toronto, ON M5G 2C4 Canada
| | - Susan H. Fox
- Edmond J. Safra Program in Parkinson’s Disease, Toronto Western Hospital, University Health Network, 399 Bathurst St, Toronto, ON M5T 2S8 Canada
- Division of Neurology, University of Toronto, Suite RFE 3-805, 200 Elizabeth St, Toronto, ON M5G 2C4 Canada
| | - Babak Taati
- Toronto Rehabilitation Institute, University Health Network, 550 University Ave, Toronto, ON M5G 2A2 Canada
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, 164 College St, Room 407, Toronto, ON M5S 3G9 Canada
- Department of Computer Science, University of Toronto, 10 King’s College Road, Room 3302, Toronto, ON M5S 3G4 Canada
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Computer model for leg agility quantification and assessment for Parkinson's disease patients. Med Biol Eng Comput 2018; 57:463-476. [PMID: 30215213 DOI: 10.1007/s11517-018-1894-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2018] [Accepted: 09/04/2018] [Indexed: 10/28/2022]
Abstract
Parkinson's disease (PD) is a progressive disorder that affects motor regulation. The Unified Parkinson's Disease Rating Scale sponsored by the Movement Disorder Society (MDS-UPDRS) quantifies the illness progression based on clinical observations. The leg agility is an item in this scale, yet only a visual detection of the features is used, leading to subjectivity. Overall, 50 patients (85 measurements) with varying motor impairment severity were asked to perform the leg agility item while wearing inertial sensor units on each ankle. We quantified features based on the MDS-UPDRS and designed a fuzzy inference model to capture clinical knowledge for assessment. The model proposed is capable of capturing all details regardless of the task speed, reducing the inherent uncertainty of the examiner observations obtaining a 92.35% of coincidence with at least one expert. In addition, the continuous scale implemented in this work prevents the inherent "floor/ceil" effect of discrete scales. This model proves the feasibility of quantification and assessment of the leg agility through inertial signals. Moreover, it allows a better follow-up of the PD patient state, due to the repeatability of our computer model and the continuous output, which are not objectively achievable through visual examination. Graphical abstract ᅟ.
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Thomas I, Westin J, Alam M, Bergquist F, Nyholm D, Senek M, Memedi M. A Treatment-Response Index From Wearable Sensors for Quantifying Parkinson's Disease Motor States. IEEE J Biomed Health Inform 2018; 22:1341-1349. [DOI: 10.1109/jbhi.2017.2777926] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Talk, Text, Tag? Understanding Self-Annotation of Smart Home Data from a User's Perspective. SENSORS 2018; 18:s18072365. [PMID: 30037046 PMCID: PMC6069125 DOI: 10.3390/s18072365] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Revised: 07/11/2018] [Accepted: 07/12/2018] [Indexed: 02/05/2023]
Abstract
Delivering effortless interactions and appropriate interventions through pervasive systems requires making sense of multiple streams of sensor data. This is particularly challenging when these concern people's natural behaviours in the real world. This paper takes a multidisciplinary perspective of annotation and draws on an exploratory study of 12 people, who were encouraged to use a multi-modal annotation app while living in a prototype smart home. Analysis of the app usage data and of semi-structured interviews with the participants revealed strengths and limitations regarding self-annotation in a naturalistic context. Handing control of the annotation process to research participants enabled them to reason about their own data, while generating accounts that were appropriate and acceptable to them. Self-annotation provided participants an opportunity to reflect on themselves and their routines, but it was also a means to express themselves freely and sometimes even a backchannel to communicate playfully with the researchers. However, self-annotation may not be an effective way to capture accurate start and finish times for activities, or location associated with activity information. This paper offers new insights and recommendations for the design of self-annotation tools for deployment in the real world.
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Schaeffer EL, Liu DY, Guerin J, Ahn M, Lee S, Asaad WF. A low-cost solution for quantification of movement during DBS surgery. J Neurosci Methods 2018; 303:136-145. [PMID: 29605668 DOI: 10.1016/j.jneumeth.2018.03.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2018] [Revised: 03/23/2018] [Accepted: 03/24/2018] [Indexed: 11/17/2022]
Abstract
BACKGROUND During the deep brain stimulation (DBS) electrode implantation operation with microelectrode recordings (MER) in awake patients, somatotopic testing and test stimulation are performed to improve electrode placement and provide the most beneficial symptom reduction possible, while minimizing side effects. As this procedure is commonly used to alleviate abnormal movements associated with Parkinson's disease (PD) and Essential Tremor (ET), intraoperative assessment of a patient's movements is critical to optimizing surgical benefit. However, despite its importance, movement assessment is typically subjective and qualitative. NEW METHOD Here, we present a detailed description of a low-cost, open-source system as a solution. RESULTS The described system measures movements intraoperatively and in synchrony with neurophysiological recordings for both online visualization and offline analysis. COMPARISON WITH EXISTING METHOD(S) Few movement quantification systems are designed to interface with intraoperative neurophysiological recordings; the widespread application of such systems may be limited by their cost and proprietary, closed-source nature. The system presented provides a low-cost, open-source alternative. CONCLUSIONS The system outlined in this work may improve the DBS procedure by adding valuable objectivity in movement quantification.
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Affiliation(s)
- Erin L Schaeffer
- Department of Neuroscience, Brown University, Providence, RI, 02903, United States
| | - Daniel Y Liu
- Department of Neuroscience, Brown University, Providence, RI, 02903, United States
| | - Julie Guerin
- Department of Neuroscience, Brown University, Providence, RI, 02903, United States
| | - Minkyu Ahn
- School of Computer Science and Electrical Engineering, Handong Global University, Pohang, 37554, South Korea
| | - Shane Lee
- Department of Neuroscience, Brown University, Providence, RI, 02903, United States; Brown Institute for Brain Science (BIBS), Brown University, Providence, RI, 02903, United States
| | - Wael F Asaad
- Department of Neuroscience, Brown University, Providence, RI, 02903, United States; Brown Institute for Brain Science (BIBS), Brown University, Providence, RI, 02903, United States; Department of Neurosurgery, The Warren Alpert Medical School, Providence, RI, 02903, United States; Department of Neuosurgry, Rhode Island Hospital, Providence, RI, 02903, United States; Norman Prince Neurosciences Institute, Lifespan, Providence, RI, 02903, United States.
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Lee SI, Daneault JF, Golabchi FN, Patel S, Paganoni S, Shih L, Bonato P. A novel method for assessing the severity of levodopa-induced dyskinesia using wearable sensors. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2015:8087-90. [PMID: 26738170 DOI: 10.1109/embc.2015.7320270] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Patients with Parkinson's disease often experience significant changes in the severity of dyskinesia when they undergo titration of their medications. Dyskinesia is marked by involuntary jerking movements that occur randomly in a burst-like fashion. The burst-like nature of such movements makes it difficult to estimate the clinical scores of severity of dyskinesia using wearable sensors. Clinical observations are generally made over intervals of 15-30 s. On the other hand, techniques designed to estimate the severity of dyskinesia based on the analysis of wearable sensor data typically use data segments of approximately 5 s. Consequently, some data segments might include dyskinetic movements, whereas others might not. Herein, we propose a novel method suitable to automatically select data segments from the training dataset that are marked by dyskinetic movements. The proposed method also aggregates results derived from the testing dataset using a machine learning algorithm to estimate the severity of dyskinesia from wearable sensor data. Results obtained from the analysis of sensor data collected from seven subjects with Parkinson's disease showed a marked improvement in the accuracy of the estimation of clinical scores of dyskinesia.
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Li MH, Mestre TA, Fox SH, Taati B. Automated vision-based analysis of levodopa-induced dyskinesia with deep learning. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2017:3377-3380. [PMID: 29060621 DOI: 10.1109/embc.2017.8037580] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Levodopa is the gold standard therapy for Parkinson's disease (PD), but its prolonged usage leads to additional motor complications, namely levodopa-induced dyskinesia (LID). To assess LID and adjust drug regimens for optimal relief, patients attend regular clinic visits. However, the intermittent nature of these visits can fail to capture important changes in a person's condition. With the recent emergence of deep learning achieving impressive results in a wide array of fields including computer vision, there is an opportunity for video analysis to be used for automated assessment of LID. Deep learning for pose estimation was studied as a viable means of extracting body movements from PD assessment videos. Movement features were computed from joint trajectories. Results show that features derived from vision-based analysis have moderate to good correlation with clinician ratings of dyskinesia severity. This study presents the first application of deep learning to video analysis in PD, and demonstrates promise for future development of a non-contact system for objective PD assessment.
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Rovini E, Maremmani C, Cavallo F. How Wearable Sensors Can Support Parkinson's Disease Diagnosis and Treatment: A Systematic Review. Front Neurosci 2017; 11:555. [PMID: 29056899 PMCID: PMC5635326 DOI: 10.3389/fnins.2017.00555] [Citation(s) in RCA: 184] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2017] [Accepted: 09/21/2017] [Indexed: 01/15/2023] Open
Abstract
Background: Parkinson's disease (PD) is a common and disabling pathology that is characterized by both motor and non-motor symptoms and affects millions of people worldwide. The disease significantly affects quality of life of those affected. Many works in literature discuss the effects of the disease. The most promising trends involve sensor devices, which are low cost, low power, unobtrusive, and accurate in the measurements, for monitoring and managing the pathology. OBJECTIVES This review focuses on wearable devices for PD applications and identifies five main fields: early diagnosis, tremor, body motion analysis, motor fluctuations (ON-OFF phases), and home and long-term monitoring. The concept is to obtain an overview of the pathology at each stage of development, from the beginning of the disease to consider early symptoms, during disease progression with analysis of the most common disorders, and including management of the most complicated situations (i.e., motor fluctuations and long-term remote monitoring). DATA SOURCES The research was conducted within three databases: IEEE Xplore®, Science Direct®, and PubMed Central®, between January 2006 and December 2016. STUDY ELIGIBILITY CRITERIA Since 1,429 articles were found, accurate definition of the exclusion criteria and selection strategy allowed identification of the most relevant papers. RESULTS Finally, 136 papers were fully evaluated and included in this review, allowing a wide overview of wearable devices for the management of Parkinson's disease.
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Affiliation(s)
- Erika Rovini
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy
| | - Carlo Maremmani
- U.O. Neurologia, Ospedale delle Apuane (AUSL Toscana Nord Ovest), Massa, Italy
| | - Filippo Cavallo
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy
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Delrobaei M, Baktash N, Gilmore G, McIsaac K, Jog M. Using Wearable Technology to Generate Objective Parkinson’s Disease Dyskinesia Severity Score: Possibilities for Home Monitoring. IEEE Trans Neural Syst Rehabil Eng 2017; 25:1853-1863. [DOI: 10.1109/tnsre.2017.2690578] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Cancela J, Villanueva Mascato S, Gatsios D, Rigas G, Marcante A, Gentile G, Biundo R, Giglio M, Chondrogiorgi M, Vilzmann R, Konitsiotis S, Antonini A, Arredondo MT, Fotiadis DI. Monitoring of motor and non-motor symptoms of Parkinson's disease through a mHealth platform. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2016:663-666. [PMID: 28268415 DOI: 10.1109/embc.2016.7590789] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Parkinson's disease (PD) is a complex, chronic disease that many patients live with for many years. In this work we propose a mHealth approach based on a set of unobtrusive, simple-in-use, off-the-self, co-operative, mobile devices that will be used for motor and non-motor symptoms monitoring and evaluation, as well as for the detection of fluctuations along with their duration through a waking day. Ideally, a multidisciplinary and integrated care approach involving several professionals working together (neurologists, physiotherapists, psychologists and nutritionists) could provide a holistic management of the disease increasing the patient's independence and Quality of Life (QoL). To address these needs we describe also an ecosystem for the management of both motor and non-motor symptoms on PD facilitating the collaboration of health professionals and empowering the patients to self-manage their condition. This would allow not only a better monitoring of PD patients but also a better understanding of the disease progression.
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Pulliam CL, Heldman DA, Brokaw EB, Mera TO, Mari ZK, Burack MA. Continuous Assessment of Levodopa Response in Parkinson's Disease Using Wearable Motion Sensors. IEEE Trans Biomed Eng 2017; 65:159-164. [PMID: 28459677 DOI: 10.1109/tbme.2017.2697764] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
OBJECTIVE Fluctuations in response to levodopa in Parkinson's disease (PD) are difficult to treat as tools to monitor temporal patterns of symptoms are hampered by several challenges. The objective was to use wearable sensors to quantify the dose response of tremor, bradykinesia, and dyskinesia in individuals with PD. METHODS Thirteen individuals with PD and fluctuating motor benefit were instrumented with wrist and ankle motion sensors and recorded by video. Kinematic data were recorded as subjects completed a series of activities in a simulated home environment through transition from off to on medication. Subjects were evaluated using the unified Parkinson disease rating scale motor exam (UPDRS-III) at the start and end of data collection. Algorithms were applied to the kinematic data to score tremor, bradykinesia, and dyskinesia. A blinded clinician rated severity observed on video. Accuracy of algorithms was evaluated by comparing scores with clinician ratings using a receiver operating characteristic (ROC) analysis. RESULTS Algorithm scores for tremor, bradykinesia, and dyskinesia agreed with clinician ratings of video recordings (ROC area > 0.8). Summary metrics extracted from time intervals before and after taking medication provided quantitative measures of therapeutic response (p < 0.01). Radar charts provided intuitive visualization, with graphical features correlated with UPDRS-III scores (R = 0.81). CONCLUSION A system with wrist and ankle motion sensors can provide accurate measures of tremor, bradykinesia, and dyskinesia as patients complete routine activities. SIGNIFICANCE This technology could provide insight on motor fluctuations in the context of daily life to guide clinical management and aid in development of new therapies.
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King RC, Villeneuve E, White RJ, Sherratt RS, Holderbaum W, Harwin WS. Application of data fusion techniques and technologies for wearable health monitoring. Med Eng Phys 2017; 42:1-12. [PMID: 28237714 DOI: 10.1016/j.medengphy.2016.12.011] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2016] [Revised: 12/08/2016] [Accepted: 12/21/2016] [Indexed: 11/26/2022]
Abstract
Technological advances in sensors and communications have enabled discrete integration into everyday objects, both in the home and about the person. Information gathered by monitoring physiological, behavioural, and social aspects of our lives, can be used to achieve a positive impact on quality of life, health, and well-being. Wearable sensors are at the cusp of becoming truly pervasive, and could be woven into the clothes and accessories that we wear such that they become ubiquitous and transparent. To interpret the complex multidimensional information provided by these sensors, data fusion techniques are employed to provide a meaningful representation of the sensor outputs. This paper is intended to provide a short overview of data fusion techniques and algorithms that can be used to interpret wearable sensor data in the context of health monitoring applications. The application of these techniques are then described in the context of healthcare including activity and ambulatory monitoring, gait analysis, fall detection, and biometric monitoring. A snap-shot of current commercially available sensors is also provided, focusing on their sensing capability, and a commentary on the gaps that need to be bridged to bring research to market.
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Affiliation(s)
- Rachel C King
- School of Biological Sciences, Biomedical Engineering, University of Reading, Reading, United Kingdom.
| | - Emma Villeneuve
- University of Exeter, Medical School, Exeter, United Kingdom.
| | - Ruth J White
- School of Biological Sciences, Biomedical Engineering, University of Reading, Reading, United Kingdom.
| | - R Simon Sherratt
- School of Biological Sciences, Biomedical Engineering, University of Reading, Reading, United Kingdom.
| | - William Holderbaum
- School of Biological Sciences, Biomedical Engineering, University of Reading, Reading, United Kingdom.
| | - William S Harwin
- School of Biological Sciences, Biomedical Engineering, University of Reading, Reading, United Kingdom.
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Aghanavesi S, Nyholm D, Senek M, Bergquist F, Memedi M. A smartphone-based system to quantify dexterity in Parkinson's disease patients. INFORMATICS IN MEDICINE UNLOCKED 2017. [DOI: 10.1016/j.imu.2017.05.005] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
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Pérez-López C, Samà A, Rodríguez-Martín D, Català A, Cabestany J, Moreno-Arostegui JM, de Mingo E, Rodríguez-Molinero A. Assessing Motor Fluctuations in Parkinson's Disease Patients Based on a Single Inertial Sensor. SENSORS (BASEL, SWITZERLAND) 2016; 16:E2132. [PMID: 27983675 PMCID: PMC5191112 DOI: 10.3390/s16122132] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2016] [Revised: 11/27/2016] [Accepted: 12/10/2016] [Indexed: 01/23/2023]
Abstract
Altered movement control is typically the first noticeable symptom manifested by Parkinson's disease (PD) patients. Once under treatment, the effect of the medication is very patent and patients often recover correct movement control over several hours. Nonetheless, as the disease advances, patients present motor complications. Obtaining precise information on the long-term evolution of these motor complications and their short-term fluctuations is crucial to provide optimal therapy to PD patients and to properly measure the outcome of clinical trials. This paper presents an algorithm based on the accelerometer signals provided by a waist sensor that has been validated in the automatic assessment of patient's motor fluctuations (ON and OFF motor states) during their activities of daily living. A total of 15 patients have participated in the experiments in ambulatory conditions during 1 to 3 days. The state recognised by the algorithm and the motor state annotated by patients in standard diaries are contrasted. Results show that the average specificity and sensitivity are higher than 90%, while their values are higher than 80% of all patients, thereby showing that PD motor status is able to be monitored through a single sensor during daily life of patients in a precise and objective way.
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Affiliation(s)
- Carlos Pérez-López
- Technical Research Centre for Dependency Care and Autonomous Living, CETPD, Universitat Politècnica de Catalunya, Barcelona Tech., Rambla de l'Exposició 59-69, Vilanova i la Geltrú 08800, Barcelona, Spain.
| | - Albert Samà
- Technical Research Centre for Dependency Care and Autonomous Living, CETPD, Universitat Politècnica de Catalunya, Barcelona Tech., Rambla de l'Exposició 59-69, Vilanova i la Geltrú 08800, Barcelona, Spain.
| | - Daniel Rodríguez-Martín
- Technical Research Centre for Dependency Care and Autonomous Living, CETPD, Universitat Politècnica de Catalunya, Barcelona Tech., Rambla de l'Exposició 59-69, Vilanova i la Geltrú 08800, Barcelona, Spain.
| | - Andreu Català
- Technical Research Centre for Dependency Care and Autonomous Living, CETPD, Universitat Politècnica de Catalunya, Barcelona Tech., Rambla de l'Exposició 59-69, Vilanova i la Geltrú 08800, Barcelona, Spain.
| | - Joan Cabestany
- Technical Research Centre for Dependency Care and Autonomous Living, CETPD, Universitat Politècnica de Catalunya, Barcelona Tech., Rambla de l'Exposició 59-69, Vilanova i la Geltrú 08800, Barcelona, Spain.
| | - Juan Manuel Moreno-Arostegui
- Technical Research Centre for Dependency Care and Autonomous Living, CETPD, Universitat Politècnica de Catalunya, Barcelona Tech., Rambla de l'Exposició 59-69, Vilanova i la Geltrú 08800, Barcelona, Spain.
| | - Eva de Mingo
- Clinical Research Unit, Consorci Sanitari del Garraf (Fundación Sant Antoni Abat ), Carrer de Sant Josep, 21-23, Vilanova i la Geltrú 08800, Barcelona, Spain.
| | - Alejandro Rodríguez-Molinero
- Clinical Research Unit, Consorci Sanitari del Garraf (Fundación Sant Antoni Abat ), Carrer de Sant Josep, 21-23, Vilanova i la Geltrú 08800, Barcelona, Spain.
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Daneault JF, Vergara-Diaz G, Lee SI. Clinical Management of Drug-Induced Dyskinesia in Parkinson’s Disease: Why Current Approaches May Need to Be Changed to Optimise Quality of Life. EUROPEAN MEDICAL JOURNAL 2016. [DOI: 10.33590/emj/10310305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2023] Open
Abstract
Parkinson’s disease is a complex, progressive neurodegenerative disorder associated with both motor and non-motor symptoms. Current treatment strategies mainly target the alleviation of motor symptoms through dopaminergic replacement therapy. Many patients with Parkinson’s disease will eventually experience motor complications associated with their anti-parkinsonian medication. One of those complications is drug-induced dyskinesia. This paper firstly reviews current approaches to the management of drug-induced dyskinesia, from modifications to the titration of medication, to more invasive approaches like deep brain stimulation. Following this we describe a recent proposal suggesting that the treatment of dyskinesia should be based on the impact on daily activities of patients rather than on the mere presence of the condition. Next, we discuss how this approach could improve the quality of life of patients and their caregivers and finally, we suggest possible ways of implementing this approach in practice.
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Affiliation(s)
- Jean-Francois Daneault
- Motion Analysis Laboratory, Spaulding Rehabilitation Hospital; Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, Massachusetts, USA
| | - Gloria Vergara-Diaz
- Motion Analysis Laboratory, Spaulding Rehabilitation Hospital; Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, Massachusetts, USA; Escuela Internacional de Doctorado, Universidad de Sevilla, Sevilla, Spain
| | - Sunghoon Ivan Lee
- Motion Analysis Laboratory, Spaulding Rehabilitation Hospital; Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, Massachusetts, USA; Advanced Human & Health Analytics Laboratory, College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, Massachusetts, USA
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Sebastianutto I, Maslava N, Hopkins CR, Cenci MA. Validation of an improved scale for rating l-DOPA-induced dyskinesia in the mouse and effects of specific dopamine receptor antagonists. Neurobiol Dis 2016; 96:156-170. [PMID: 27597526 DOI: 10.1016/j.nbd.2016.09.001] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2016] [Revised: 08/24/2016] [Accepted: 09/01/2016] [Indexed: 11/17/2022] Open
Abstract
Rodent models of l-DOPA-induced dyskinesia (LID) are essential to investigate pathophysiological mechanisms and treatment options. Ratings of abnormal involuntary movements (AIMs) are used to capture both qualitative and quantitative features of dyskinetic behaviors. Thus far, validated rating scales for the mouse have anchored the definition of severity to the time during which AIMs are present. Here we have asked whether the severity of axial, limb, and orolingual AIMs can be objectively assessed with scores based on movement amplitude. Mice sustained 6-OHDA lesions in the medial forebrain bundle and were treated with l-DOPA (3-6mg/kg/day) until they developed stable AIMs scores. Two independent investigators rated AIM severity using both the validated time-based scale and a novel amplitude scale, evaluating the degree of deviation of dyskinetic body parts relative to their resting position. The amplitude scale yielded a high degree of consistency both within- and between raters. Thus, time-based scores, amplitude scores, and a combination of the two ('global AIM scores') were applied to compare antidyskinetic effects produced by amantadine and by the following subtype-specific DA receptor antagonists: SCH23390 (D1/D5), Raclopride (D2/D3), PG01037 (D3), L-745,870 (D4), and VU6004461 (D4). SCH23390 and Raclopride produced similarly robust reductions in both time-based scores and amplitude scores, while PG01037 and L-745,870 had more partial effects. Interestingly, a novel and highly brain penetrable D4 receptor antagonist (VU6004461) markedly attenuated both time-based and amplitude scores without diminishing the general motor stimulant effect of l-DOPA. In summary, our results show that a dyskinesia scale combining a time dimension with an amplitude dimension ('global AIMs') is more sensitive than unidimensional scales. Moreover, the antidyskinetic effects produced by two chemically distinct D4 antagonists identify the D4 receptor as a potential future target for the treatment of LID.
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Affiliation(s)
- Irene Sebastianutto
- Basal Ganglia Pathophysiology Unit, Dept. Exp. Medical Science, Lund University, BMC, 221 84 Lund, Sweden.
| | - Natallia Maslava
- Basal Ganglia Pathophysiology Unit, Dept. Exp. Medical Science, Lund University, BMC, 221 84 Lund, Sweden
| | - Corey R Hopkins
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Nebraska Medical Center, Omaha, NE 68198-6125, USA
| | - M Angela Cenci
- Basal Ganglia Pathophysiology Unit, Dept. Exp. Medical Science, Lund University, BMC, 221 84 Lund, Sweden.
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Silva de Lima AL, Hahn T, de Vries NM, Cohen E, Bataille L, Little MA, Baldus H, Bloem BR, Faber MJ. Large-Scale Wearable Sensor Deployment in Parkinson's Patients: The Parkinson@Home Study Protocol. JMIR Res Protoc 2016; 5:e172. [PMID: 27565186 PMCID: PMC5018102 DOI: 10.2196/resprot.5990] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2016] [Revised: 06/29/2016] [Accepted: 07/20/2016] [Indexed: 11/18/2022] Open
Abstract
Background Long-term management of Parkinson’s disease does not reach its full potential because we lack knowledge about individual variations in clinical presentation and disease progression. Continuous and longitudinal assessments in real-life (ie, within the patients’ own home environment) might fill this knowledge gap. Objective The primary aim of the Parkinson@Home study is to evaluate the feasibility and compliance of using multiple wearable sensors to collect clinically relevant data. Our second aim is to address the usability of these data for answering clinical research questions. Finally, we aim to build a database for future validation of novel algorithms applied to sensor-derived data from Parkinson’s patients during daily functioning. Methods The Parkinson@Home study is a two-phase observational study involving 1000 Parkinson’s patients and 250 physiotherapists. Disease status is assessed using a short version of the Parkinson's Progression Markers Initiative protocol, performed by certified physiotherapists. Additionally, participants will wear a set of sensors (smartwatch, smartphone, and fall detector), and use these together with a customized smartphone app (Fox Insight), 24/7 for 3 months. The sensors embedded within the smartwatch and fall detector may be used to estimate physical activity, tremor, sleep quality, and falls. Medication intake and fall incidents will be measured via patients’ self-reports in the smartphone app. Phase one will address the feasibility of the study protocol. In phase two, mathematicians will distill relevant summary statistics from the raw sensor signals, which will be compared against the clinical outcomes. Results Recruitment of 300 participants for phase one was concluded in March, 2016, and the follow-up period will end in June, 2016. Phase two will include the remaining participants, and will commence in September, 2016. Conclusions The Parkinson@Home study is expected to generate new insights into the feasibility of integrating self-collected information from wearable sensors into both daily routines and clinical practices for Parkinson’s patients. This study represents an important step towards building a reliable system that translates and integrates real-life information into clinical decisions, with the long-term aim of delivering personalized disease management support. ClinicalTrial ClinicalTrials.gov NCT02474329; https://clinicaltrials.gov/ct2/show/NCT02474329 (Archived at http://www.webcitation.org/6joEc5P1v)
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Affiliation(s)
- Ana Lígia Silva de Lima
- Donders Institute for Brain, Cognition and Behavior, Radboud university medical center, Nijmegen, Netherlands.
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Correlation of Quantitative Motor State Assessment Using a Kinetograph and Patient Diaries in Advanced PD: Data from an Observational Study. PLoS One 2016; 11:e0161559. [PMID: 27556806 PMCID: PMC4996447 DOI: 10.1371/journal.pone.0161559] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2015] [Accepted: 08/08/2016] [Indexed: 01/10/2023] Open
Abstract
Introduction Effective management and development of new treatment strategies for response fluctuations in advanced Parkinson’s disease (PD) largely depends on clinical rating instruments such as the PD home diary. The Parkinson’s kinetigraph (PKG) measures movement accelerations and analyzes the spectral power of the low frequencies of the accelerometer data. New algorithms convert each hour of continuous PKG data into one of the three motor categories used in the PD home diary, namely motor Off state and On state with and without dyskinesia. Objective To compare quantitative motor state assessment in fluctuating PD patients using the PKG with motor state ratings from PD home diaries. Methods Observational cohort study on 24 in-patients with documented motor fluctuations who completed diaries by rating motor Off, On without dyskinesia, On with dyskinesia, and asleep for every hour for 5 consecutive days. Simultaneously collected PKG data (recorded between 6 am and 10 pm) were analyzed and calibrated to the patient’s individual thresholds for Off and dyskinetic state by novel algorithms classifying the continuous accelerometer data into these motor states for every hour between 6 am and 10 pm. Results From a total of 2,040 hours, 1,752 hours (87.4%) were available for analyses from calibrated PKG data (7.5% sleeping time and 5.1% unclassified motor state time were excluded from analyses). Distributions of total motor state hours per day measured by PKG showed moderate-to-strong correlation to those assessed by diaries for the different motor states (Pearson’s correlations coefficients: 0.404–0.658), but inter-rating method agreements on the single-hour-level were only low-to-moderate (Cohen’s κ: 0.215–0.324). Conclusion The PKG has been shown to capture motor fluctuations in patients with advanced PD. The limited correlation of hour-to-hour diary and PKG recordings should be addressed in further studies.
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Hssayeni MD, Burack MA, Ghoraani B. Automatic assessment of medication states of patients with Parkinson's disease using wearable sensors. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2016:6082-6085. [PMID: 28269640 DOI: 10.1109/embc.2016.7592116] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Motor fluctuations are a major focus of clinical managements in patients with mid-stage and advance Parkinson's disease (PD). In this paper, we develop a new patient-specific algorithm that can classify those fluctuations during a variety of activities. We extract a set of temporal and spectral features from the ambulatory signals and then introduce a semi-supervised classification algorithm based on K-means and self-organizing tree map clustering methods. Two different types of cluster labeling are introduced: hard and fuzzy labeling. The developed algorithm is evaluated on a dataset from triaxial gyroscope sensors for 12 PD patients. The average result of using K-means and fuzzy labeling on the trunk and the more affected leg sensors' readings was 75.96%, 70.57%, and 86.93% for accuracy, sensitivity, and specificity, respectively. The accuracy for individual patients varied from 99.95% to 42.53%, which was correlated with dyskinesia severity and the improvement of the PD symptoms with medication.
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Yang K, Xiong WX, Liu FT, Sun YM, Luo S, Ding ZT, Wu JJ, Wang J. Objective and quantitative assessment of motor function in Parkinson's disease-from the perspective of practical applications. ANNALS OF TRANSLATIONAL MEDICINE 2016; 4:90. [PMID: 27047949 DOI: 10.21037/atm.2016.03.09] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Parkinson's disease (PD) is a common neurodegenerative disorder with high morbidity because of the coming aged society. Currently, disease management and the development of new treatment strategies mainly depend on the clinical information derived from rating scales and patients' diaries, which have various limitations with regard to validity, inter-rater variability and continuous monitoring. Recently the prevalence of mobile medical equipment has made it possible to develop an objective, accurate, remote monitoring system for motor function assessment, playing an important role in disease diagnosis, home-monitoring, and severity evaluation. This review discusses the recent development in sensor technology, which may be a promising replacement of the current rating scales in the assessment of motor function of PD.
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Affiliation(s)
- Ke Yang
- Department & Institute of Neurology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Wei-Xi Xiong
- Department & Institute of Neurology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Feng-Tao Liu
- Department & Institute of Neurology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Yi-Min Sun
- Department & Institute of Neurology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Susan Luo
- Department & Institute of Neurology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Zheng-Tong Ding
- Department & Institute of Neurology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Jian-Jun Wu
- Department & Institute of Neurology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Jian Wang
- Department & Institute of Neurology, Huashan Hospital, Fudan University, Shanghai 200040, China
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Smith SL, Lones MA, Bedder M, Alty JE, Cosgrove J, Maguire RJ, Pownall ME, Ivanoiu D, Lyle C, Cording A, Elliott CJH. Computational approaches for understanding the diagnosis and treatment of Parkinson's disease. IET Syst Biol 2016; 9:226-33. [PMID: 26577157 DOI: 10.1049/iet-syb.2015.0030] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
This study describes how the application of evolutionary algorithms (EAs) can be used to study motor function in humans with Parkinson's disease (PD) and in animal models of PD. Human data is obtained using commercially available sensors via a range of non-invasive procedures that follow conventional clinical practice. EAs can then be used to classify human data for a range of uses, including diagnosis and disease monitoring. New results are presented that demonstrate how EAs can also be used to classify fruit flies with and without genetic mutations that cause Parkinson's by using measurements of the proboscis extension reflex. The case is made for a computational approach that can be applied across human and animal studies of PD and lays the way for evaluation of existing and new drug therapies in a truly objective way.
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Affiliation(s)
- Stephen L Smith
- Department of Electronics, University of York, Heslington, York Y010 5DD.
| | - Michael A Lones
- School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh EH14 4AS
| | - Matthew Bedder
- Department of Computer Science, University of York, Heslington, York Y010 5GW
| | - Jane E Alty
- Neurology Department, Leeds Teaching Hospitals NHS Trust, Leeds LS1 3EX
| | - Jeremy Cosgrove
- Neurology Department, Leeds Teaching Hospitals NHS Trust, Leeds LS1 3EX
| | - Richard J Maguire
- Department of Biomedical Science, University of Sheffield, Sheffield S10 2TN
| | | | - Diana Ivanoiu
- Department of Biology, University of York, Heslington, York Y010 5DD
| | - Camille Lyle
- Department of Biology, University of York, Heslington, York Y010 5DD
| | - Amy Cording
- Department of Biology, University of York, Heslington, York Y010 5DD
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Bu LL, Yang K, Xiong WX, Liu FT, Anderson B, Wang Y, Wang J. Toward precision medicine in Parkinson's disease. ANNALS OF TRANSLATIONAL MEDICINE 2016; 4:26. [PMID: 26889479 DOI: 10.3978/j.issn.2305-5839.2016.01.21] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Precision medicine refers to an innovative approach selected for disease prevention and health promotion according to the individual characteristics of each patient. The goal of precision medicine is to formulate prevention and treatment strategies based on each individual with novel physiological and pathological insights into a certain disease. A multidimensional data-driven approach is about to upgrade "precision medicine" to a higher level of greater individualization in healthcare, a shift towards the treatment of individual patients rather than treating a certain disease including Parkinson's disease (PD). As one of the most common neurodegenerative diseases, PD is a lifelong chronic disease with clinical and pathophysiologic complexity, currently it is treatable but neither preventable nor curable. At its advanced stage, PD is associated with devastating chronic complications including both motor dysfunction and non-motor symptoms which impose an immense burden on the life quality of patients. Advances in computational approaches provide opportunity to establish the patient's personalized disease data at the multidimensional levels, which finally meeting the need for the current concept of precision medicine via achieving the minimal side effects and maximal benefits individually. Hence, in this review, we focus on highlighting the perspectives of precision medicine in PD based on multi-dimensional information about OMICS, molecular imaging, deep brain stimulation (DBS) and wearable sensors. Precision medicine in PD is expected to integrate the best evidence-based knowledge to individualize optimal management in future health care for those with PD.
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Affiliation(s)
- Lu-Lu Bu
- 1 Department & Institute of Neurology, Huashan Hospital, Fudan University, Shanghai 200040, China ; 2 School of Computing, National University of Singapore, Singapore
| | - Ke Yang
- 1 Department & Institute of Neurology, Huashan Hospital, Fudan University, Shanghai 200040, China ; 2 School of Computing, National University of Singapore, Singapore
| | - Wei-Xi Xiong
- 1 Department & Institute of Neurology, Huashan Hospital, Fudan University, Shanghai 200040, China ; 2 School of Computing, National University of Singapore, Singapore
| | - Feng-Tao Liu
- 1 Department & Institute of Neurology, Huashan Hospital, Fudan University, Shanghai 200040, China ; 2 School of Computing, National University of Singapore, Singapore
| | - Boyd Anderson
- 1 Department & Institute of Neurology, Huashan Hospital, Fudan University, Shanghai 200040, China ; 2 School of Computing, National University of Singapore, Singapore
| | - Ye Wang
- 1 Department & Institute of Neurology, Huashan Hospital, Fudan University, Shanghai 200040, China ; 2 School of Computing, National University of Singapore, Singapore
| | - Jian Wang
- 1 Department & Institute of Neurology, Huashan Hospital, Fudan University, Shanghai 200040, China ; 2 School of Computing, National University of Singapore, Singapore
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Pérez-López C, Samà A, Rodríguez-Martín D, Moreno-Aróstegui JM, Cabestany J, Bayes A, Mestre B, Alcaine S, Quispe P, Laighin GÓ, Sweeney D, Quinlan LR, Counihan TJ, Browne P, Annicchiarico R, Costa A, Lewy H, Rodríguez-Molinero A. Dopaminergic-induced dyskinesia assessment based on a single belt-worn accelerometer. Artif Intell Med 2016; 67:47-56. [PMID: 26831150 DOI: 10.1016/j.artmed.2016.01.001] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2015] [Revised: 01/01/2016] [Accepted: 01/05/2016] [Indexed: 01/23/2023]
Abstract
BACKGROUND After several years of treatment, patients with Parkinson's disease (PD) tend to have, as a side effect of the medication, dyskinesias. Close monitoring may benefit patients by enabling doctors to tailor a personalised medication regimen. Moreover, dyskinesia monitoring can help neurologists make more informed decisions in patient's care. OBJECTIVE To design and validate an algorithm able to be embedded into a system that PD patients could wear during their activities of daily living with the purpose of registering the occurrence of dyskinesia in real conditions. MATERIALS AND METHODS Data from an accelerometer positioned in the waist are collected at the patient's home and are annotated by experienced clinicians. Data collection is divided into two parts: a main database gathered from 92 patients used to partially train and to evaluate the algorithms based on a leave-one-out approach and, on the other hand, a second database from 10 patients which have been used to also train a part of the detection algorithm. RESULTS Results show that, depending on the severity and location of dyskinesia, specificities and sensitivities higher than 90% are achieved using a leave-one-out methodology. Although mild dyskinesias presented on the limbs are detected with 95% specificity and 39% sensitivity, the most important types of dyskinesia (any strong dyskinesia and trunk mild dyskinesia) are assessed with 95% specificity and 93% sensitivity. CONCLUSION The presented algorithmic method and wearable device have been successfully validated in monitoring the occurrence of strong dyskinesias and mild trunk dyskinesias during activities of daily living.
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Affiliation(s)
- Carlos Pérez-López
- Centro de Estudios para la Dependencia y la vida Autónoma (CETpD), Universitat Politècnica de Catalunya (UPC), Rambla de l'Exposició, 59, 08800 Vilanova i la Geltrú, Barcelona, Spain.
| | - Albert Samà
- Centro de Estudios para la Dependencia y la vida Autónoma (CETpD), Universitat Politècnica de Catalunya (UPC), Rambla de l'Exposició, 59, 08800 Vilanova i la Geltrú, Barcelona, Spain
| | - Daniel Rodríguez-Martín
- Centro de Estudios para la Dependencia y la vida Autónoma (CETpD), Universitat Politècnica de Catalunya (UPC), Rambla de l'Exposició, 59, 08800 Vilanova i la Geltrú, Barcelona, Spain
| | - Juan Manuel Moreno-Aróstegui
- Centro de Estudios para la Dependencia y la vida Autónoma (CETpD), Universitat Politècnica de Catalunya (UPC), Rambla de l'Exposició, 59, 08800 Vilanova i la Geltrú, Barcelona, Spain
| | - Joan Cabestany
- Centro de Estudios para la Dependencia y la vida Autónoma (CETpD), Universitat Politècnica de Catalunya (UPC), Rambla de l'Exposició, 59, 08800 Vilanova i la Geltrú, Barcelona, Spain
| | - Angels Bayes
- UParkinson, Passeig Bonanova 26, Barcelona 08022, Spain
| | - Berta Mestre
- UParkinson, Passeig Bonanova 26, Barcelona 08022, Spain
| | | | - Paola Quispe
- UParkinson, Passeig Bonanova 26, Barcelona 08022, Spain
| | - Gearóid Ó Laighin
- Electrical & Electronic Engineering, School of Engineering & Informatics National University Galway (NUIG), University Rd, Galway, Ireland
| | - Dean Sweeney
- Electrical & Electronic Engineering, School of Engineering & Informatics National University Galway (NUIG), University Rd, Galway, Ireland
| | - Leo R Quinlan
- Physiology, School of Medicine National University Galway (NUIG), University Rd, Galway, Ireland
| | - Timothy J Counihan
- School of Medicine, National University Galway (NUIG), University Rd, Galway, Ireland
| | - Patrick Browne
- School of Medicine, National University Galway (NUIG), University Rd, Galway, Ireland
| | | | - Alberto Costa
- Fondazione Santa Lucia, Via Ardeatina, 306, Rome 00142, Italy; Niccolò Cusano University, via Don Carlo Gnocchi, 3, Rome 00166, Italy
| | - Hadas Lewy
- Maccabi Healthcare Services, Hamered Street 27, Tel-Aviv 68125, Israel
| | - Alejandro Rodríguez-Molinero
- Electrical & Electronic Engineering, School of Engineering & Informatics National University Galway (NUIG), University Rd, Galway, Ireland
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Ossig C, Antonini A, Buhmann C, Classen J, Csoti I, Falkenburger B, Schwarz M, Winkler J, Storch A. Wearable sensor-based objective assessment of motor symptoms in Parkinson’s disease. J Neural Transm (Vienna) 2015; 123:57-64. [PMID: 26253901 DOI: 10.1007/s00702-015-1439-8] [Citation(s) in RCA: 97] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2015] [Accepted: 08/03/2015] [Indexed: 11/29/2022]
Affiliation(s)
- Christiana Ossig
- Department of Neuropsychiatry and Laboratory of Molecular Psychiatry, Charité-Universitätsmedizin Berlin, 10117, Berlin, Germany
| | - Angelo Antonini
- Division of Parkinson Disease and Movement Disorders, Fondazione Ospedale, San Camillo, Venice, Italy
| | - Carsten Buhmann
- Department of Neurology, University Medical Center Hamburg-Eppendorf, 20246, Hamburg, Germany
| | - Joseph Classen
- Department of Neurology, University of Leipzig, 04103, Leipzig, Germany
| | - Ilona Csoti
- Gertrudis-Kliniken im Parkinson Zentrum, Regionalzentrum Biskirchen, 35638, Leun-Biskirchen, Germany
| | | | - Michael Schwarz
- Neurologische Klinik, Klinikum Dortmund, 44137, Dortmund, Germany
| | - Jürgen Winkler
- Division of Molecular Neurology, University Erlangen, 91054, Erlangen, Germany
| | - Alexander Storch
- Division of Neurodegenerative Diseases, Department of Neurology, Technische Universität Dresden, 01307, Dresden, Germany.
- German Centre for Neurodegenerative Diseases (DZNE) Dresden, 01307, Dresden, Germany.
- Department of Neurology, University of Rostock, 18147, Rostock, Germany.
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PERFORM: a system for monitoring, assessment and management of patients with Parkinson's disease. SENSORS 2014; 14:21329-57. [PMID: 25393786 PMCID: PMC4279536 DOI: 10.3390/s141121329] [Citation(s) in RCA: 85] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/22/2014] [Revised: 09/25/2014] [Accepted: 10/20/2014] [Indexed: 01/20/2023]
Abstract
In this paper, we describe the PERFORM system for the continuous remote monitoring and management of Parkinson's disease (PD) patients. The PERFORM system is an intelligent closed-loop system that seamlessly integrates a wide range of wearable sensors constantly monitoring several motor signals of the PD patients. Data acquired are pre-processed by advanced knowledge processing methods, integrated by fusion algorithms to allow health professionals to remotely monitor the overall status of the patients, adjust medication schedules and personalize treatment. The information collected by the sensors (accelerometers and gyroscopes) is processed by several classifiers. As a result, it is possible to evaluate and quantify the PD motor symptoms related to end of dose deterioration (tremor, bradykinesia, freezing of gait (FoG)) as well as those related to over-dose concentration (Levodopa-induced dyskinesia (LID)). Based on this information, together with information derived from tests performed with a virtual reality glove and information about the medication and food intake, a patient specific profile can be built. In addition, the patient specific profile with his evaluation during the last week and last month, is compared to understand whether his status is stable, improving or worsening. Based on that, the system analyses whether a medication change is needed—always under medical supervision—and in this case, information about the medication change proposal is sent to the patient. The performance of the system has been evaluated in real life conditions, the accuracy and acceptability of the system by the PD patients and healthcare professionals has been tested, and a comparison with the standard routine clinical evaluation done by the PD patients' physician has been carried out. The PERFORM system is used by the PD patients and in a simple and safe non-invasive way for long-term record of their motor status, thus offering to the clinician a precise, long-term and objective view of patient's motor status and drug/food intake. Thus, with the PERFORM system the clinician can remotely receive precise information for the PD patient's status on previous days and define the optimal therapeutical treatment.
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