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Paredes-Acuna N, Utpadel-Fischler D, Ding K, Thakor NV, Cheng G. Upper limb intention tremor assessment: opportunities and challenges in wearable technology. J Neuroeng Rehabil 2024; 21:8. [PMID: 38218890 PMCID: PMC10787996 DOI: 10.1186/s12984-023-01302-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 12/26/2023] [Indexed: 01/15/2024] Open
Abstract
BACKGROUND Tremors are involuntary rhythmic movements commonly present in neurological diseases such as Parkinson's disease, essential tremor, and multiple sclerosis. Intention tremor is a subtype associated with lesions in the cerebellum and its connected pathways, and it is a common symptom in diseases associated with cerebellar pathology. While clinicians traditionally use tests to identify tremor type and severity, recent advancements in wearable technology have provided quantifiable ways to measure movement and tremor using motion capture systems, app-based tasks and tools, and physiology-based measurements. However, quantifying intention tremor remains challenging due to its changing nature. METHODOLOGY & RESULTS This review examines the current state of upper limb tremor assessment technology and discusses potential directions to further develop new and existing algorithms and sensors to better quantify tremor, specifically intention tremor. A comprehensive search using PubMed and Scopus was performed using keywords related to technologies for tremor assessment. Afterward, screened results were filtered for relevance and eligibility and further classified into technology type. A total of 243 publications were selected for this review and classified according to their type: body function level: movement-based, activity level: task and tool-based, and physiology-based. Furthermore, each publication's methods, purpose, and technology are summarized in the appendix table. CONCLUSIONS Our survey suggests a need for more targeted tasks to evaluate intention tremors, including digitized tasks related to intentional movements, neurological and physiological measurements targeting the cerebellum and its pathways, and signal processing techniques that differentiate voluntary from involuntary movement in motion capture systems.
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Affiliation(s)
- Natalia Paredes-Acuna
- Institute for Cognitive Systems, Technical University of Munich, Arcisstraße 21, 80333, Munich, Germany.
| | - Daniel Utpadel-Fischler
- Department of Neurology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Keqin Ding
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Nitish V Thakor
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Gordon Cheng
- Institute for Cognitive Systems, Technical University of Munich, Arcisstraße 21, 80333, Munich, Germany
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2
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Ricci M, Lazzaro GD, Errico V, Pisani A, Giannini F, Saggio G. The impact of wearable electronics in assessing the effectiveness of levodopa treatment in Parkinsons disease. IEEE J Biomed Health Inform 2022; 26:2920-2928. [PMID: 35316198 DOI: 10.1109/jbhi.2022.3160103] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE In order to evaluate Parkinson disease patients response to therapeutic interventions, sources of information are mainly patient reports and clinicians assessment of motor functions. However, these sources can suffer from patients subjectivity and from inter/intra raters score variability. Our work aimed at determining the impact of wearable electronics and data analysis in objectifying the effectiveness of levodopa treatment. METHODS Seven motor tasks performed by thirty-six patients were measured by wearable electronics and related data were analyzed. This was at the time of therapy initiation (T0), and repeated after six (T1) and 12 months (T2). Wearable electronics consisted of inertial measurement units each equipped with 3-axis accelerometer and 3-axis gyroscope, while data analysis of ANOVA and Pearson correlation algorithms, in addition to a support vector machine (SVM) classification. RESULTS According to our findings, levodopa-based therapy alters the patients conditions in general, ameliorating something (e.g. bradykinesia), leaving unchanged others (e.g. tremor), but with poor correlation to the levodopa dose. CONCLUSION A technology-based approach can objectively assess levodopa-based therapy effectiveness. SIGNIFICANCE Novel devices can improve the accuracy of the assessment of motor function, by integrating the clinical evaluation and patient reports.
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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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] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Espay AJ, Bonato P, Nahab FB, Maetzler W, Dean JM, Klucken J, Eskofier BM, Merola A, Horak F, Lang AE, Reilmann R, Giuffrida J, Nieuwboer A, Horne M, Little MA, Litvan I, Simuni T, Dorsey ER, Burack MA, Kubota K, Kamondi A, Godinho C, Daneault JF, Mitsi G, Krinke L, Hausdorff JM, Bloem BR, Papapetropoulos S. Technology in Parkinson's disease: Challenges and opportunities. Mov Disord 2016; 31:1272-82. [PMID: 27125836 DOI: 10.1002/mds.26642] [Citation(s) in RCA: 315] [Impact Index Per Article: 39.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2015] [Revised: 03/15/2016] [Accepted: 03/18/2016] [Indexed: 12/21/2022] Open
Abstract
The miniaturization, sophistication, proliferation, and accessibility of technologies are enabling the capture of more and previously inaccessible phenomena in Parkinson's disease (PD). However, more information has not translated into a greater understanding of disease complexity to satisfy diagnostic and therapeutic needs. Challenges include noncompatible technology platforms, the need for wide-scale and long-term deployment of sensor technology (among vulnerable elderly patients in particular), and the gap between the "big data" acquired with sensitive measurement technologies and their limited clinical application. Major opportunities could be realized if new technologies are developed as part of open-source and/or open-hardware platforms that enable multichannel data capture sensitive to the broad range of motor and nonmotor problems that characterize PD and are adaptable into self-adjusting, individualized treatment delivery systems. The International Parkinson and Movement Disorders Society Task Force on Technology is entrusted to convene engineers, clinicians, researchers, and patients to promote the development of integrated measurement and closed-loop therapeutic systems with high patient adherence that also serve to (1) encourage the adoption of clinico-pathophysiologic phenotyping and early detection of critical disease milestones, (2) enhance the tailoring of symptomatic therapy, (3) improve subgroup targeting of patients for future testing of disease-modifying treatments, and (4) identify objective biomarkers to improve the longitudinal tracking of impairments in clinical care and research. This article summarizes the work carried out by the task force toward identifying challenges and opportunities in the development of technologies with potential for improving the clinical management and the quality of life of individuals with PD. © 2016 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Alberto J Espay
- James J. and Joan A. Gardner Family Center for Parkinson's disease and Movement Disorders, University of Cincinnati, Cincinnati, Ohio, USA.
| | - Paolo Bonato
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, Massachusetts, USA
| | - Fatta B Nahab
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
| | - Walter Maetzler
- Department of Neurodegeneration, Hertie Institute for Clinical Brain Research (HIH), University of Tuebingen, Tübingen, Germany.,DZNE, German Center for Neurodegenerative Diseases, Tübingen, Germany
| | - John M Dean
- Davis Phinney Foundation for Parkinson's, Boulder, Colorado, USA
| | - Jochen Klucken
- Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Bjoern M Eskofier
- Digital Sports Group, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany
| | - Aristide Merola
- Department of Neuroscience "Rita Levi Montalcini", Città della salute e della scienza di Torino, Torino, Italy
| | - Fay Horak
- Department of Neurology, Oregon Health & Science University, Portland VA Medical System, Portland, Oregon.,APDM, Inc., Portland, Oregon, USA
| | - Anthony E Lang
- Morton and Gloria Movement Disorders Clinic and the Edmond J. Safra Program in Parkinson's Disease, Toronto Western Hospital, Toronto, Canada
| | - Ralf Reilmann
- George-Huntington-Institute, Muenster, Germany.,Department of Radiology, University of Muenster, Muenster, Germany.,Department of Neurodegenerative Diseases and Hertie-Institute for Clinical Brain Research, University of Tuebingen, Tuebingen, Germany
| | | | - Alice Nieuwboer
- Neuromotor Rehabilitation Research Group, Department of Rehabilitation Sciences, KU Leuven, Leuven, Belgium
| | - Malcolm Horne
- Global Kinetics Corporation & Florey Institute for Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria, Australia
| | - Max A Little
- Department of Mathematics, Aston University, Birmingham, UK.,Media Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Irene Litvan
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
| | - Tanya Simuni
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - E Ray Dorsey
- Department of Neurology, University of Rochester Medical Center, Rochester, New York, USA
| | - Michelle A Burack
- Department of Neurology, University of Rochester Medical Center, Rochester, New York, USA
| | - Ken Kubota
- Michael J Fox Foundation for Parkinson's Research, New York City, New York, USA
| | - Anita Kamondi
- Department of Neurology, National Institute of Clinical Neurosciences, Budapest, Hungary
| | - Catarina Godinho
- Center of Interdisciplinary Research Egas Moniz (CiiEM), Instituto Superior de Ciências da Saúde Egas Moniz, Monte de Caparica, Portugal
| | - Jean-Francois Daneault
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Lothar Krinke
- Medtronic Neuromodulation, Minneapolis, Minnesota, USA
| | - Jeffery M Hausdorff
- Sackler School of Medicine, Tel Aviv University and Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Bastiaan R Bloem
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Nijmegen, the Netherlands
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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. Ann Transl Med 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] [What about the content of this article? (0)] [Affiliation(s)] [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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7
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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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Abstract
The low cost, simple, robust, mobile, and easy to use Mobile Motion Capture (MiMiC) system is presented and the constraints which guided the design of MiMiC are discussed. The MiMiC Android application allows motion data to be captured from kinematic modules such as Shimmer 2r sensors over Bluetooth. MiMiC is cost effective and can be used for an entire day in a person's daily routine without being intrusive. MiMiC is a flexible motion capture system which can be used for many applications including fall detection, detection of fatigue in industry workers, and analysis of individuals' work patterns in various environments.
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