A web-based system for home monitoring of patients with Parkinson's disease using wearable sensors

Relevance of Biochemical Deep Phenotyping for a Personalised Approach to Parkinson's Disease

Parkinson's disease (PD) is a multifactorial neurodegenerative disorder characterised by the progressive loss of dopaminergic neurons in the nigrostriatal tract. The identification of disease-modifying therapies is the Holy Grail of PD research, but to date no drug has been approved as such a therapy. A possible reason is the remarkable phenotypic heterogeneity of PD patients, which can generate confusion in the interpretation of results or even mask the efficacy of a therapeutic intervention. This heterogeneity should be taken into account in clinical trials, stratifying patients by their expected response to drugs designed to engage selected molecular targets. In this setting, stratification methods (clinical and genetic) should be supported by biochemical phenotyping of PD patients, in line with the deep phenotyping concept. Collection, from single patients, of a range of biological samples would streamline the generation of these profiles. Several studies have proposed biochemical characterisations of patient cohorts based on analysis of blood, cerebrospinal fluid, urine, stool, saliva and skin biopsy samples, with extracellular vesicles attracting increasing interest as a source of biomarkers. In this review we report and critically discuss major studies that used a biochemical approach to stratify their PD cohorts. The analyte most studied is α-synuclein, while other studies have focused on neurofilament light chain, lysosomal proteins, inflammasome-related proteins, LRRK2 and the urinary proteome. At present, stratification of PD patients, while promising, is still a nascent approach. Deep phenotyping of patients will allow clinical researchers to identify homogeneous subgroups for the investigation of tailored disease-modifying therapies, enhancing the chances of therapeutic success.

A Low-Cost System Using a Big-Data Deep-Learning Framework for Assessing Physical Telerehabilitation: A Proof-of-Concept

The consolidation of telerehabilitation for the treatment of many diseases over the last decades is a consequence of its cost-effective results and its ability to offer access to rehabilitation in remote areas. Telerehabilitation operates over a distance, so vulnerable patients are never exposed to unnecessary risks. Despite its low cost, the need for a professional to assess therapeutic exercises and proper corporal movements online should also be mentioned. The focus of this paper is on a telerehabilitation system for patients suffering from Parkinson's disease in remote villages and other less accessible locations. A full-stack is presented using big data frameworks that facilitate communication between the patient and the occupational therapist, the recording of each session, and real-time skeleton identification using artificial intelligence techniques. Big data technologies are used to process the numerous videos that are generated during the course of treating simultaneous patients. Moreover, the skeleton of each patient can be estimated using deep neural networks for automated evaluation of corporal exercises, which is of immense help to the therapists in charge of the treatment programs.

Co-evolution of machine learning and digital technologies to improve monitoring of Parkinson's disease motor symptoms

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.

Wearable Sensor for Forearm Motion Detection Using a Carbon-Based Conductive Layer-Polymer Composite Film

In this study, we developed a fabrication method for a bracelet-type wearable sensor to detect four motions of the forearm by using a carbon-based conductive layer-polymer composite film. The integral material used for the composite film is a polyethylene terephthalate polymer film with a conductive layer composed of a carbon paste. It is capable of detecting the resistance variations corresponding to the flexion changes of the surface of the body due to muscle contraction and relaxation. To effectively detect the surface resistance variations of the film, a small sensor module composed of mechanical parts mounted on the film was designed and fabricated. A subject wore the bracelet sensor, consisting of three such sensor modules, on their forearm. The surface resistance of the film varied corresponding to the flexion change of the contact area between the forearm and the sensor modules. The surface resistance variations of the film were converted to voltage signals and used for motion detection. The results demonstrate that the thin bracelet-type wearable sensor, which is comfortable to wear and easily applicable, successfully detected each motion with high accuracy.

Wearables in the home-based assessment of abnormal movements in Parkinson's disease: a systematic review of the literature

At present, the standard practices for home-based assessments of abnormal movements in Parkinson's disease (PD) are based either on subjective tools or on objective measures that often fail to capture day-to-day fluctuations and long-term information in real-life conditions in a way that patient's compliance and privacy are secured. The employment of wearable technologies in PD represents a great paradigm shift in healthcare remote diagnostics and therapeutics monitoring. However, their applicability in everyday clinical practice seems to be still limited. We carried out a systematic search across the Medline Database. In total, 246 publications, published until 1 June 2020, were identified. Among them, 26 reports met the inclusion criteria and were included in the present review. We focused more on clinically relevant aspects of wearables' application including feasibility and efficacy of the assessment, the number, type and body position of the wearable devices, type of PD motor symptom, environment and duration of assessments and validation methodology. The aim of this review is to provide a systematic overview of the current knowledge and state-of-the-art of the home-based assessment of motor symptoms and fluctuations in PD patients using wearable technology, highlighting current problems and laying foundations for future works.

A real-world study of wearable sensors in Parkinson's disease

Most wearable sensor studies in Parkinson's disease have been conducted in the clinic and thus may not be a true representation of everyday symptoms and symptom variation. Our goal was to measure activity, gait, and tremor using wearable sensors inside and outside the clinic. In this observational study, we assessed motor features using wearable sensors developed by MC10, Inc. Participants wore five sensors, one on each limb and on the trunk, during an in-person clinic visit and for two days thereafter. Using the accelerometer data from the sensors, activity states (lying, sitting, standing, walking) were determined and steps per day were also computed by aggregating over 2 s walking intervals. For non-walking periods, tremor durations were identified that had a characteristic frequency between 3 and 10 Hz. We analyzed data from 17 individuals with Parkinson's disease and 17 age-matched controls over an average 45.4 h of sensor wear. Individuals with Parkinson's walked significantly less (median [inter-quartile range]: 4980 [2835-7163] steps/day) than controls (7367 [5106-8928] steps/day; P = 0.04). Tremor was present for 1.6 [0.4-5.9] hours (median [range]) per day in most-affected hands (MDS-UPDRS 3.17a or 3.17b = 1-4) of individuals with Parkinson's, which was significantly higher than the 0.5 [0.3-2.3] hours per day in less-affected hands (MDS-UPDRS 3.17a or 3.17b = 0). These results, which require replication in larger cohorts, advance our understanding of the manifestations of Parkinson's in real-world settings.

Wearable Assistive Robotics: A Perspective on Current Challenges and Future Trends

Wearable assistive robotics is an emerging technology with the potential to assist humans with sensorimotor impairments to perform daily activities. This assistance enables individuals to be physically and socially active, perform activities independently, and recover quality of life. These benefits to society have motivated the study of several robotic approaches, developing systems ranging from rigid to soft robots with single and multimodal sensing, heuristics and machine learning methods, and from manual to autonomous control for assistance of the upper and lower limbs. This type of wearable robotic technology, being in direct contact and interaction with the body, needs to comply with a variety of requirements to make the system and assistance efficient, safe and usable on a daily basis by the individual. This paper presents a brief review of the progress achieved in recent years, the current challenges and trends for the design and deployment of wearable assistive robotics including the clinical and user need, material and sensing technology, machine learning methods for perception and control, adaptability and acceptability, datasets and standards, and translation from lab to the real world.

Technology-based therapy-response and prognostic biomarkers in a prospective study of a de novo Parkinson's disease cohort

Early noninvasive reliable biomarkers are among the major unmet needs in Parkinson's disease (PD) to monitor therapy response and disease progression. Objective measures of motor performances could allow phenotyping of subtle, undetectable, early stage motor impairments of PD patients. This work aims at identifying prognostic biomarkers in newly diagnosed PD patients and quantifying therapy-response. Forty de novo PD patients underwent clinical and technology-based kinematic assessments performing motor tasks (MDS-UPDRS part III) to assess tremor, bradykinesia, gait, and postural stability (T0). A visit after 6 months (T1) and a clinical and kinematic assessment after 12 months (T2) where scheduled. A clinical follow-up was provided between 30 and 36 months after the diagnosis (T3). We performed an ANOVA for repeated measures to compare patients' kinematic features at baseline and at T2 to assess therapy response. Pearson correlation test was run between baseline kinematic features and UPDRS III score variation between T0 and T3, to select candidate kinematic prognostic biomarkers. A multiple linear regression model was created to predict the long-term motor outcome using T0 kinematic measures. All motor tasks significantly improved after the dopamine replacement therapy. A significant correlation was found between UPDRS scores variation and some baseline bradykinesia (toe tapping amplitude decrement, p = 0.009) and gait features (velocity of arms and legs, sit-to-stand time, p = 0.007; p = 0.009; p = 0.01, respectively). A linear regression model including four baseline kinematic features could significantly predict the motor outcome (p = 0.000214). Technology-based objective measures represent possible early and reproducible therapy-response and prognostic biomarkers.

Does gait bout definition influence the ability to discriminate gait quality between people with and without multiple sclerosis during daily life?

BACKGROUND

There is currently no consensus about standardized gait bout definitions when passively monitoring walking during normal daily life activities. It is also not known how different definitions of a gait bout in daily life monitoring affects the ability to distinguish pathological gait quality. Specifically, how many seconds of a pause with no walking indicates an end to one gait bout and the start of another bout? In this study, we investigated the effect of 3 gait bout definitions on the discriminative ability to distinguish quality of walking in people with multiple sclerosis (MS) from healthy control subjects (HC) during a week of daily living.

METHODS

15 subjects with MS and 16 HC wore instrumented socks on each foot and one Opal sensor over the lower lumbar area for a week of daily activities for at least 8 h/day. Three gait bout definitions were based on the length of the pause between the end of one gait bout and start of another bout (1.25 s, 2.50 s, and 5.0 s pause). Area under the curve (AUC) was used to compare gait quality measures in MS versus HC.

RESULTS

Total number of gait bouts over the week were statistically significantly different across bout definitions, as expected. However, AUCs of gait quality measures (such as gait speed, stride length, stride time) discriminating people with MS from HC were not different despite the 3 bout definitions.

SIGNIFICANCE

Quality of gait measures that discriminate MS from HC during daily life are not influenced by the length of a gait bout, despite large differences in quantity of gait across bout definitions. Thus, gait quality measures in people with MS versus controls can be compared across studies using different gait bout definitions with pause lengths ≤5 s.

Sensors and Systems for Physical Rehabilitation and Health Monitoring-A Review

The use of wearable equipment and sensing devices to monitor physical activities, whether for well-being, sports monitoring, or medical rehabilitation, has expanded rapidly due to the evolution of sensing techniques, cheaper integrated circuits, and the development of connectivity technologies. In this scenario, this paper presents a state-of-the-art review of sensors and systems for rehabilitation and health monitoring. Although we know the increasing importance of data processing techniques, our focus was on analyzing the implementation of sensors and biomedical applications. Although many themes overlap, we organized this review based on three groups: Sensors in Healthcare, Home Medical Assistance, and Continuous Health Monitoring; Systems and Sensors in Physical Rehabilitation; and Assistive Systems.

Marker-Based Movement Analysis of Human Body Parts in Therapeutic Procedure

Movement analysis of human body parts is momentous in several applications including clinical diagnosis and rehabilitation programs. The objective of this research is to present a low-cost 3D visual tracking system to analyze the movement of various body parts during therapeutic procedures. Specifically, a marker based motion tracking system is proposed in this paper to capture the movement information in home-based rehabilitation. Different color markers are attached to the desired joints' locations and they are detected and tracked in the video to encode their motion information. The availability of this motion information of different body parts during the therapy can be exploited to achieve more accurate results with better clinical insight, which in turn can help improve the therapeutic decision making. The proposed framework is an automated and inexpensive motion tracking system with execution speed close to real time. The performance of the proposed method is evaluated on a dataset of 10 patients using two challenging matrices that measure the average accuracy by estimating the joints' locations and rotations. The experimental evaluation and its comparison with the existing state-of-the-art techniques reveals the efficiency of the proposed method.

A Heterogeneous Sensing Suite for Multisymptom Quantification of Parkinson's Disease

Parkinson's disease (PD) is the second most common neurodegenerative disease affecting millions worldwide. Bespoke subject-specific treatment (medication or deep brain stimulation (DBS)) is critical for management, yet depends on precise assessment cardinal PD symptoms - bradykinesia, rigidity and tremor. Clinician diagnosis is the basis of treatment, yet it allows only a cross-sectional assessment of symptoms which can vary on an hourly basis and is liable to inter- and intra-rater subjectivity across human examiners. Automated symptomatic assessment has attracted significant interest to optimise treatment regimens between clinician visits, however, no wearable has the capacity to simultaneously assess all three cardinal symptoms. Challenges in the measurement of rigidity, mapping muscle activity out-of-clinic and sensor fusion have inhibited translation. In this study, we address all through a novel wearable sensor system and machine learning algorithms. The sensor system is composed of a force-sensor, three inertial measurement units (IMUs) and four custom mechanomyography (MMG) sensors. The system was tested in its capacity to predict Unified Parkinson's Disease Rating Scale (UPDRS) scores based on quantitative assessment of bradykinesia, rigidity and tremor in PD patients. 23 PD patients were tested with the sensor system in parallel with exams conducted by treating clinicians and 10 healthy subjects were recruited as a comparison control group. Results prove the system accurately predicts UPDRS scores for all symptoms (85.4% match on average with physician assessment) and discriminates between healthy subjects and PD patients (96.6% on average). MMG features can also be used for remote monitoring of severity and fluctuations in PD symptoms out-of-clinic. This closed-loop feedback system enables individually tailored and regularly updated treatment, facilitating better outcomes for a very large patient population.

mHealth and wearable technology should replace motor diaries to track motor fluctuations in Parkinson's disease

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.

Deep Phenotyping of Parkinson's Disease

Phenotype is the set of observable traits of an organism or condition. While advances in genetics, imaging, and molecular biology have improved our understanding of the underlying biology of Parkinson's disease (PD), clinical phenotyping of PD still relies primarily on history and physical examination. These subjective, episodic, categorical assessments are valuable for diagnosis and care but have left gaps in our understanding of the PD phenotype. Sensors can provide objective, continuous, real-world data about the PD clinical phenotype, increase our knowledge of its pathology, enhance evaluation of therapies, and ultimately, improve patient care. In this paper, we explore the concept of deep phenotyping-the comprehensive assessment of a condition using multiple clinical, biological, genetic, imaging, and sensor-based tools-for PD. We discuss the rationale for, outline current approaches to, identify benefits and limitations of, and consider future directions for deep clinical phenotyping.

Innovative Assisted Living Tools, Remote Monitoring Technologies, Artificial Intelligence-Driven Solutions, and Robotic Systems for Aging Societies: Systematic Review

BACKGROUND

The increase in life expectancy and recent advancements in technology and medical science have changed the way we deliver health services to the aging societies. Evidence suggests that home telemonitoring can significantly decrease the number of readmissions, and continuous monitoring of older adults' daily activities and health-related issues might prevent medical emergencies.

OBJECTIVE

The primary objective of this review was to identify advances in assistive technology devices for seniors and aging-in-place technology and to determine the level of evidence for research on remote patient monitoring, smart homes, telecare, and artificially intelligent monitoring systems.

METHODS

A literature review was conducted using Cumulative Index to Nursing and Allied Health Literature Plus, MEDLINE, EMBASE, Institute of Electrical and Electronics Engineers Xplore, ProQuest Central, Scopus, and Science Direct. Publications related to older people's care, independent living, and novel assistive technologies were included in the study.

RESULTS

A total of 91 publications met the inclusion criteria. In total, four themes emerged from the data: technology acceptance and readiness, novel patient monitoring and smart home technologies, intelligent algorithm and software engineering, and robotics technologies. The results revealed that most studies had poor reference standards without an explicit critical appraisal.

CONCLUSIONS

The use of ubiquitous in-home monitoring and smart technologies for aged people's care will increase their independence and the health care services available to them as well as improve frail elderly people's health care outcomes. This review identified four different themes that require different conceptual approaches to solution development. Although the engineering teams were focused on prototype and algorithm development, the medical science teams were concentrated on outcome research. We also identified the need to develop custom technology solutions for different aging societies. The convergence of medicine and informatics could lead to the development of new interdisciplinary research models and new assistive products for the care of older adults.

Long-Term Home-Monitoring Sensor Technology in Patients with Parkinson's Disease-Acceptance and Adherence

Parkinson’s disease (PD) is characterized by a highly individual disease-profile as well as fluctuating symptoms. Consequently, 24-h home monitoring in a real-world environment would be an ideal solution for precise symptom diagnostics. In recent years, small lightweight sensors which have assisted in objective, reliable analysis of motor symptoms have attracted a lot of attention. While technical advances are important, patient acceptance of such new systems is just as crucial to increase long-term adherence. So far, there has been a lack of long-term evaluations of PD-patient sensor adherence and acceptance. In a pilot study of PD patients (N = 4), adherence (wearing time) and acceptance (questionnaires) of a multi-part sensor set was evaluated over a 4-week timespan. The evaluated sensor set consisted of 3 body-worn sensors and 7 at-home installed ambient sensors. After one month of continuous monitoring, the overall system usability scale (SUS)-questionnaire score was 71.5%, with an average acceptance score of 87% for the body-worn sensors and 100% for the ambient sensors. On average, sensors were worn 15 h and 4 min per day. All patients reported strong preferences of the sensor set over manual self-reporting methods. Our results coincide with measured high adherence and acceptance rate of similar short-term studies and extend them to long-term monitoring.

Commercial Postural Devices: A Review

Wearables are devices worn on the human body and are able to measure various health parameters, such as physical activity, energy expenditure and gait. With the advancement of technology, the general population are now spending more hours craning our necks and slouching over smartphones, tablets and computers, et cetera. Bodily posture is representative of physical and mental health. Poor posture can lead to spinal complications and the same can be said vice versa. As the standard of living increases, there is an increase in consumerism and the expectation to maintain such a lifestyle even in the aging population. Therefore, many are able to afford small luxuries in life, such as a piece of technology that could potentially improve their health in the long run. Wearable technology is a promising alternative to laboratory systems for movement and posture analysis. This article reviews commercial wearable devices with a focus on postural analysis. The clinical applicability of posture wearables, particularly in preventing, monitoring and treating spinal and musculoskeletal conditions, along with other purposes in healthcare, will be discussed.

Assessment of Motor Impairments in Early Untreated Parkinson's Disease Patients: The Wearable Electronics Impact

OBJECTIVE

The complex nature of Parkinson's disease (PD) makes difficult to rate its severity, mainly based on the visual inspection of motor impairments. Wearable sensors have been demonstrated to help overcoming such a difficulty, by providing objective measures of motor abnormalities. However, up to now, those sensors have been used on advanced PD patients with evident motor impairment. As a novelty, here we report the impact of wearable sensors in the evaluation of motor abnormalities in newly diagnosed, untreated, namely de novo, patients.

METHODS

A network of wearable sensors was used to measure motor capabilities, in 30 de novo PD patients and 30 healthy subjects, while performing five motor tasks. Measurement data were used to determine motor features useful to highlight impairments and were compared with the corresponding clinical scores. Three classifiers were used to differentiate PD from healthy subjects.

RESULTS

Motor features gathered from wearable sensors showed a high degree of significance in discriminating the early untreated de novo PD patients from the healthy subjects, with 95% accuracy. The rates of severity obtained from the measured features are partially in agreement with the clinical scores, with some highlighted, though justified, exceptions.

CONCLUSION

Our findings support the feasibility of adopting wearable sensors in the detection of motor anomalies in early, untreated, PD patients.

SIGNIFICANCE

This work demonstrates that subtle motor impairments, occurring in de novo patients, can be evidenced by means of wearable sensors, providing clinicians with instrumental tools as suitable supports for early diagnosis, and subsequent management.

A Multi-Layer Gaussian Process for Motor Symptom Estimation in People With Parkinson's Disease

The assessment of Parkinson's disease (PD) poses a significant challenge, as it is influenced by various factors that lead to a complex and fluctuating symptom manifestation. Thus, a frequent and objective PD assessment is highly valuable for effective health management of people with Parkinson's disease (PwP). Here, we propose a method for monitoring PwP by stochastically modeling the relationships between wrist movements during unscripted daily activities and corresponding annotations about clinical displays of movement abnormalities. We approach the estimation of PD motor signs by independently modeling and hierarchically stacking Gaussian process models for three classes of commonly observed movement abnormalities in PwP including tremor, (non-tremulous) bradykinesia, and (non-tremulous) dyskinesia. We use clinically adopted severity measures as annotations for training the models, thus allowing our multi-layer Gaussian process prediction models to estimate not only their presence but also their severities. The experimental validation of our approach demonstrates strong agreement of the model predictions with these PD annotations. Our results show that the proposed method produces promising results in objective monitoring of movement abnormalities of PD in the presence of arbitrary and unknown voluntary motions, and makes an important step toward continuous monitoring of PD in the home environment.

Review on Wearables to Monitor Foot Temperature in Diabetic Patients

One of the diseases that could affect diabetic patients is the diabetic foot problem. Unnoticed minor injuries and subsequent infection can lead to ischemic ulceration, and may end in a foot amputation. Preliminary studies have shown that there is a positive relationship between increased skin temperature and the pre⁻ulceration phase. Hence, we have carried out a review on wearables, medical devices, and sensors used specifically for collecting vital data. In particular, we are interested in the measure of the foot⁻temperature. Since there is a large amount of this type of medical wearables, we will focus on those used to measure temperature and developed in Spain.

Can the Latest Computerized Technologies Revolutionize Conventional Assessment Tools and Therapies for a Neurological Disease? The Example of Parkinson's Disease

Dramatic breakthroughs in the treatment and assessment of neurological diseases are lacking. We believe that conventional methods have several limitations. Computerized technologies, including virtual reality, augmented reality, and robot assistant systems, are advancing at a rapid pace. In this study, we used Parkinson's disease (PD) as an example to elucidate how the latest computerized technologies can improve the diagnosis and treatment of neurological diseases. Dopaminergic medication and deep brain stimulation remain the most effective interventions for treating PD. Subjective scales, such as the Unified Parkinson's Disease Rating Scale and the Hoehn and Yahr stage, are still the most widely used assessments. Wearable sensors, virtual reality, augmented reality, and robot assistant systems are increasingly being used for evaluation of patients with PD. The use of such computerized technologies can result in safe, objective, real-time behavioral assessments. Our experiences and understanding of PD have led us to believe that such technologies can provide real-time assessment, which will revolutionize the traditional assessment and treatment of PD. New technologies are desired that can revolutionize PD treatment and facilitate real-time adjustment of treatment based on motor fluctuations, such as telediagnosis systems and "smart treatment systems." The use of these technologies will substantially improve both the assessment and the treatment of neurological diseases before next-generation treatments, such as stem cell and genetic therapy, and next-generation assessments, can be clinically practiced, although the current level of artificial intelligence cannot replace the role of clinicians.

Activity-Aware Wearable System for Power-Efficient Prediction of Physiological Responses

Wearable health monitoring has emerged as a promising solution to the growing need for remote health assessment and growing demand for personalized preventative care and wellness management. Vital signs can be monitored and alerts can be made when anomalies are detected, potentially improving patient outcomes. One major challenge for the use of wearable health devices is their energy efficiency and battery-lifetime, which motivates the recent efforts towards the development of self-powered wearable devices. This article proposes a method for context aware dynamic sensor selection for power optimized physiological prediction using multi-modal wearable data streams. We first cluster the data by physical activity using the accelerometer data, and then fit a group lasso model to each activity cluster. We find the optimal reduced set of groups of sensor features, in turn reducing power usage by duty cycling these and optimizing prediction accuracy. We show that using activity state-based contextual information increases accuracy while decreasing power usage. We also show that the reduced feature set can be used in other regression models increasing accuracy and decreasing energy burden. We demonstrate the potential reduction in power usage using a custom-designed multi-modal wearable system prototype.

Wearable sensors to improve detection of patient deterioration

INTRODUCTION

Monitoring a patient's vital signs forms a basic component of care, enabling the identification of deteriorating patients and increasing the likelihood of improving patient outcomes. Several paper-based track and trigger warning scores have been developed to allow clinical evaluation of a patient and guidance on escalation protocols and frequency of monitoring. However, evidence suggests that patient deterioration on hospital wards is still missed, and that patients are still falling through the safety net. Wearable sensor technology is currently undergoing huge growth, and the development of new light-weight wireless wearable sensors has enabled multiple vital signs monitoring of ward patients continuously and in real time.

AREAS COVERED

In this paper, we aim to closely examine the benefits of wearable monitoring applications that measure multiple vital signs; in the context of improving healthcare and delivery. A review of the literature was performed.

EXPERT COMMENTARY

Findings suggest that several sensor designs are available with the potential to improve patient safety for both hospital patients and those at home. Larger clinical trials are required to ensure both diagnostic accuracy and usability.

Detection of Infantile Movement Disorders in Video Data Using Deformable Part-Based Model

Movement analysis of infants' body parts is momentous for the early detection of various movement disorders such as cerebral palsy. Most existing techniques are either marker-based or use wearable sensors to analyze the movement disorders. Such techniques work well for adults, however they are not effective for infants as wearing such sensors or markers may cause discomfort to them, affecting their natural movements. This paper presents a method to help the clinicians for the early detection of movement disorders in infants. The proposed method is marker-less and does not use any wearable sensors which makes it ideal for the analysis of body parts movement in infants. The algorithm is based on the deformable part-based model to detect the body parts and track them in the subsequent frames of the video to encode the motion information. The proposed algorithm learns a model using a set of part filters and spatial relations between the body parts. In particular, it forms a mixture of part-filters for each body part to determine its orientation which is used to detect the parts and analyze their movements by tracking them in the temporal direction. The model is represented using a tree-structured graph and the learning process is carried out using the structured support vector machine. The proposed framework will assist the clinicians and the general practitioners in the early detection of infantile movement disorders. The performance evaluation of the proposed method is carried out on a large dataset and the results compared with the existing techniques demonstrate its effectiveness.

Automated Systems Based on Wearable Sensors for the Management of Parkinson's Disease at Home: A Systematic Review

BACKGROUND

Parkinson's disease is a common neurodegenerative pathology that significantly influences quality of life (QoL) of people affected. The increasing interest and development in telemedicine services and internet of things technologies aim to implement automated smart systems for remote assistance of patients. The wide variability of Parkinson's disease in the clinical expression, as well as in the symptom progression, seems to address the patients' care toward a personalized therapy.

OBJECTIVES

This review addresses automated systems based on wearable/portable devices for the remote treatment and management of Parkinson's disease. The idea is to obtain an overview of the telehealth and automated systems currently developed to address the impairments due to the pathology to allow clinicians to improve the quality of care for Parkinson's disease with benefits for patients in QoL.

DATA SOURCES

The research was conducted within three databases: IEEE Xplore^®, Web of Science^®, and PubMed Central^®, between January 2008 and September 2017.

STUDY ELIGIBILITY CRITERIA

Accurate exclusion criteria and selection strategy were applied to screen the 173 articles found.

RESULTS

Ultimately, 55 articles were fully evaluated and included in this review. Divided into three categories, they were automated systems actually tested at home, implemented mobile applications for Parkinson's disease assessment, or described a telehealth system architecture.

CONCLUSION

This review would provide an exhaustive overview of wearable systems for the remote management and automated assessment of Parkinson's disease, taking into account the reliability and acceptability of the implemented technologies.

App-Based Bradykinesia Tasks for Clinic and Home Assessment in Parkinson's Disease: Reliability and Responsiveness

BACKGROUND

Clinical rating of bradykinesia in Parkinson disease (PD) is challenging as it must combine several movement features into a single score. Additionally, in-clinic assessment cannot capture fluctuations throughout the day.

OBJECTIVE

To evaluate the reliability and responsiveness of a motion sensor-based tablet app for objective bradykinesia assessment in clinic and at home as compared to clinical ratings.

METHODS

Thirty-two PD patients treated with subthalamic deep brain stimulation (DBS) were outfitted with a motion sensor on the index finger of the more affected hand to perform two repetitions of finger-tapping, hand opening-closing, and arm pronation-supination tasks with DBS on and 10, 20, and 30 minutes after turning DBS off. Tasks were videotaped for blinded clinician rating using the Modified Bradykinesia Rating Scale (MBRS). Participants were then sent home with an app-based system to perform two repetitions of the same tasks six times per day spaced two hours apart, three days per week, for two weeks. Intraclass correlation (ICC) and minimal detectable change (MDC) were calculated.

RESULTS

As the effects of DBS wore off, motion sensors detected worsening of amplitude sooner than did clinician-rated MBRS for all three tasks. ICCs were significantly higher and MDCs were significantly lower for motion sensors in the clinic and at home than for clinician ratings (p < 0.01).

CONCLUSIONS

The tablet-based app demonstrated higher reliability and responsiveness in capturing bradykinesia-related tasks in the clinic and at home than did clinician ratings. This tool may enhance the assessment of novel therapies.

Quantification of whole-body bradykinesia in Parkinson's disease participants using multiple inertial sensors

Bradykinesia (slowness of movement) is a common motor symptom of Parkinson's disease (PD) that can severely affect quality of life for those living with the disease. Assessment and treatment of PD motor symptoms largely depends on clinical scales such as the Unified Parkinson's Disease Rating Scale (UPDRS). However, such clinical scales rely on the visual assessment by a human observer, naturally resulting in inter-rater variability. Although previous studies have developed objective means for measuring bradykinesia in PD patients, their evaluation was restricted by the type of movement and number of joints assessed. These studies failed to provide a more comprehensive, whole-body evaluation capable of measuring multiple joints simultaneously. This study utilizes wearable inertial measurement units (IMUs) to quantify whole-body movements, providing novel bradykinesia indices for walking (WBI) and standing up from a chair (sit-to-stand; SBI). The proposed bradykinesia indices include the joint angles at both upper and lower limbs and trunk motion to compute a complete, objective score for whole body bradykinesia. Thirty PD and 11 age-matched healthy control participants were recruited for the study. The participants performed two standard walking tasks that involved multiple body joints in the upper and lower limbs. The WBI and SBI successfully identified differences between control and PD participants. The indices also effectively identified differences within the PD population, distinguishing participants assessed with (ON) and without (OFF) levodopa; the gold-standard of treatment for PD. The goal of this study is to provide health professionals with an objective score for whole body bradykinesia by simultaneously measuring the upper and lower extremities along with truncal movement. This method demonstrates potential to be used in conjunction with current clinical standards for motor symptom assessment, and may also be promising for the remote assessment of PD patients and in cases where experienced clinicians may not be available. In conclusion, the intelligent use of this technology for the measurement of bradykinesia (among other symptoms) has vast implications for optimizing treatment in Parkinson's disease, ultimately leading to an improvement in quality of life.

Wearable Chemosensors: A Review of Recent Progress

In recent years, there has been growing demand for wearable chemosensors for their important potential applications in mobile and electronic healthcare, patient self-assessment, human motion monitoring, and so on. Innovations in wearable chemosensors are revolutionizing the modern lifestyle, especially the involvement of both doctors and patients in the modern healthcare system. The facile interaction of wearable chemosensors with the human body makes them favorable and convenient tools for the detection and long-term monitoring of the chemical, biological, and physical status of the human body at a low cost with high performance. In this Minireview, we give a brief overview of the recent advances and developments in the field of wearable chemosensors, summarize the basic types of wearable chemosensors, and discuss their main functions and fabrication methods. At the end of this paper, the future development direction of wearable chemosensors is prospected. With continued interest and attention to this field, new exciting progress is expected in the development of innovative wearable chemosensors.

Segmentation of Gait Sequences in Sensor-Based Movement Analysis: A Comparison of Methods in Parkinson's Disease

Robust gait segmentation is the basis for mobile gait analysis. A range of methods have been applied and evaluated for gait segmentation of healthy and pathological gait bouts. However, a unified evaluation of gait segmentation methods in Parkinson’s disease (PD) is missing. In this paper, we compare four prevalent gait segmentation methods in order to reveal their strengths and drawbacks in gait processing. We considered peak detection from event-based methods, two variations of dynamic time warping from template matching methods, and hierarchical hidden Markov models (hHMMs) from machine learning methods. To evaluate the methods, we included two supervised and instrumented gait tests that are widely used in the examination of Parkinsonian gait. In the first experiment, a sequence of strides from instructed straight walks was measured from 10 PD patients. In the second experiment, a more heterogeneous assessment paradigm was used from an additional 34 PD patients, including straight walks and turning strides as well as non-stride movements. The goal of the latter experiment was to evaluate the methods in challenging situations including turning strides and non-stride movements. Results showed no significant difference between the methods for the first scenario, in which all methods achieved an almost 100% accuracy in terms of F-score. Hence, we concluded that in the case of a predefined and homogeneous sequence of strides, all methods can be applied equally. However, in the second experiment the difference between methods became evident, with the hHMM obtaining a 96% F-score and significantly outperforming the other methods. The hHMM also proved promising in distinguishing between strides and non-stride movements, which is critical for clinical gait analysis. Our results indicate that both the instrumented test procedure and the required stride segmentation algorithm have to be selected adequately in order to support and complement classical clinical examination by sensor-based movement assessment.

Technologies Assessing Limb Bradykinesia in Parkinson's Disease

Background: The MDS-UPDRS (Movement Disorders Society – Unified Parkinson’s Disease Rating Scale) is the most widely used scale for rating impairment in PD. Subscores measuring bradykinesia have low reliability that can be subject to rater variability. Novel technological tools can be used to overcome such issues.

Objective: To systematically explore and describe the available technologies for measuring limb bradykinesia in PD that were published between 2006 and 2016.

Methods: A systematic literature search using PubMed (MEDLINE), IEEE Xplore, Web of Science, Scopus and Engineering Village (Compendex and Inspec) databases was performed to identify relevant technologies published until 18 October 2016.

Results: 47 technologies assessing bradykinesia in PD were identified, 17 of which offered home and clinic-based assessment whilst 30 provided clinic-based assessment only. Of the eligible studies, 7 were validated in a PD patient population only, whilst 40 were tested in both PD and healthy control groups. 19 of the 47 technologies assessed bradykinesia only, whereas 28 assessed other parkinsonian features as well. 33 technologies have been described in additional PD-related studies, whereas 14 are not known to have been tested beyond the pilot phase.

Conclusion: Technology based tools offer advantages including objective motor assessment and home monitoring of symptoms, and can be used to assess response to intervention in clinical trials or routine care. This review provides an up-to-date repository and synthesis of the current literature regarding technology used for assessing limb bradykinesia in PD. The review also discusses the current trends with regards to technology and discusses future directions in development.

Towards remote monitoring of Parkinson's disease tremor using wearable motion capture systems

The management of movement disorders is shifting from a centralized-clinical assessment towards remote monitoring and individualized therapy. While a variety of treatment options are available, ranging from pharmaceutical drugs to invasive neuromodulation, the clinical effects are inconsistent and often poorly measured. For instance, the lack of remote monitoring has been a major limitation to optimize therapeutic interventions for patients with Parkinson's Disease (PD). In this work, we focus on the assessment of full-body tremor as the most recognized PD symptom. Forty PD and twenty two healthy participants were recruited. The main assessment tool was an inertial measurement unit (IMU)-based motion capture system to quantify full-body tremor and to separate tremor-dominant from non-tremor-dominant PD patients as well as from healthy controls. We developed a new measure and evaluated its clinical utility by correlating the results with the Unified Parkinson's Disease Rating Scale (UPDRS) scores as the gold standard. Significant correlation was observed between the UPDRS and the tremor severity scores for the selected tasks. The results suggest that it is feasible and clinically meaningful to utilize the suggested objective tremor score for the assessment of PD patients. Furthermore, this portable assessment tool could potentially be used in the home environment to monitor PD tremor and facilitate optimizing therapeutic interventions.

Fuzzy inference model evaluating turn for Parkinson's disease patients

Parkinson's disease is a chronic illness that affects motor skills. The Unified Parkinson's Disease Rating Scale sponsored by the Movement Disorder Society (MDS-UPDRS) quantifies the current state of the disease based on clinician's observations. In this scale, turning is part of the gait assessment, yet specific guidelines on which features to observe and rate are still unclear. What is more, only visual impairment detection is used as the main subjective rating tool. In this respect, four biomechanical features are extracted from sensors worn on the lower limbs in this work. Afterwards, a turning assessment score is computed by means of a fuzzy inference model constructed based on the examiners knowledge. Overall, 46 patients with varying motor impairment severity underwent a full MDS-UPDRS motor examination and were monitored using a measurement system that includes inertial sensors on each ankle. Turning rating scores computed are reasonably consistent with examiners opinions. Nevertheless, the model proposed herein will always output the same score given the same inputs; whereas the subjective nature of examiners observations translates into uncertainty and variability in the rating scores. Furthermore, the continuous scale implemented in this work prevents the floor/ceiling effect inherent of discrete scales.

Freezing of gait and fall detection in Parkinson's disease using wearable sensors: a systematic review

Despite the large number of studies that have investigated the use of wearable sensors to detect gait disturbances such as Freezing of gait (FOG) and falls, there is little consensus regarding appropriate methodologies for how to optimally apply such devices. Here, an overview of the use of wearable systems to assess FOG and falls in Parkinson's disease (PD) and validation performance is presented. A systematic search in the PubMed and Web of Science databases was performed using a group of concept key words. The final search was performed in January 2017, and articles were selected based upon a set of eligibility criteria. In total, 27 articles were selected. Of those, 23 related to FOG and 4 to falls. FOG studies were performed in either laboratory or home settings, with sample sizes ranging from 1 PD up to 48 PD presenting Hoehn and Yahr stage from 2 to 4. The shin was the most common sensor location and accelerometer was the most frequently used sensor type. Validity measures ranged from 73-100% for sensitivity and 67-100% for specificity. Falls and fall risk studies were all home-based, including samples sizes of 1 PD up to 107 PD, mostly using one sensor containing accelerometers, worn at various body locations. Despite the promising validation initiatives reported in these studies, they were all performed in relatively small sample sizes, and there was a significant variability in outcomes measured and results reported. Given these limitations, the validation of sensor-derived assessments of PD features would benefit from more focused research efforts, increased collaboration among researchers, aligning data collection protocols, and sharing data sets.

Fuzzy Computing Model of Activity Recognition on WSN Movement Data for Ubiquitous Healthcare Measurement

Ubiquitous health care (UHC) is beneficial for patients to ensure they complete therapeutic exercises by self-management at home. We designed a fuzzy computing model that enables recognizing assigned movements in UHC with privacy. The movements are measured by the self-developed body motion sensor, which combines both accelerometer and gyroscope chips to make an inertial sensing node compliant with a wireless sensor network (WSN). The fuzzy logic process was studied to calculate the sensor signals that would entail necessary features of static postures and dynamic motions. Combinations of the features were studied and the proper feature sets were chosen with compatible fuzzy rules. Then, a fuzzy inference system (FIS) can be generated to recognize the assigned movements based on the rules. We thus implemented both fuzzy and adaptive neuro-fuzzy inference systems in the model to distinguish static and dynamic movements. The proposed model can effectively reach the recognition scope of the assigned activity. Furthermore, two exercises of upper-limb flexion in physical therapy were applied for the model in which the recognition rate can stand for the passing rate of the assigned motions. Finally, a web-based interface was developed to help remotely measure movement in physical therapy for UHC.

Increasing trend of wearables and multimodal interface for human activity monitoring: A review

Activity recognition technology is one of the most important technologies for life-logging and for the care of elderly persons. Elderly people prefer to live in their own houses, within their own locality. If, they are capable to do so, several benefits can follow in terms of society and economy. However, living alone may have high risks. Wearable sensors have been developed to overcome these risks and these sensors are supposed to be ready for medical uses. It can help in monitoring the wellness of elderly persons living alone by unobtrusively monitoring their daily activities. The study aims to review the increasing trends of wearable devices and need of multimodal recognition for continuous or discontinuous monitoring of human activity, biological signals such as Electroencephalogram (EEG), Electrooculogram (EOG), Electromyogram (EMG), Electrocardiogram (ECG) and parameters along with other symptoms. This can provide necessary assistance in times of ominous need, which is crucial for the advancement of disease-diagnosis and treatment. Shared control architecture with multimodal interface can be used for application in more complex environment where more number of commands is to be used to control with better results in terms of controlling.

The study on a real-time remote monitoring system for Parkinson's disease patients with deep brain stimulators

The Deep Brain Stimulation (DBS) has become a well-accepted treatment for Parkinson's disease patients around the world. However, postoperative care of the stimulators usually puts a heavy burden on the patients' families, especially in China. To solve the problem, this study developed a real-time remote monitoring system for deep brain stimulators. Based on Internet technologies, the system offers remote adjustment service so that in vivo stimulators could be programmed at patients' home by clinic caregivers. We tested the system on an experimental condition and the results have proved that this early exploration of remote monitoring deep brain stimulators was successful.

Human behavioral assessments in current research of Parkinson's disease

Parkinson's disease (PD) is traditionally classified as a movement disorder because patients mainly complain about motor symptoms. Recently, non-motor symptoms of PD have been recognized by clinicians and scientists as early signs of PD, and they are detrimental factors in the quality of life in advanced PD patients. It is crucial to comprehensively understand the essence of behavioral assessments, from the simplest measurement of certain symptoms to complex neuropsychological tasks. We have recently reviewed behavioral assessments in PD research with animal models (Asakawa et al., 2016). As a companion volume, this article will systematically review the behavioral assessments of motor and non-motor PD symptoms of human patients in current research. The major aims of this article are: (1) promoting a comparative understanding of various behavioral assessments in terms of the principle and measuring indexes; (2) addressing the major strengths and weaknesses of these behavioral assessments for a better selection of tasks/tests in order to avoid biased conclusions due to inappropriate assessments; and (3) presenting new concepts regarding the development of wearable devices and mobile internet in future assessments. In conclusion we emphasize the importance of improving the assessments for non-motor symptoms because of their complex and unique mechanisms in human PD brains.

Technology in Parkinson's disease: Challenges and opportunities

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.

A Novel Congestion Avoidance Technique for Simultaneous Real-Time Medical Data Transmission

The use of wireless body sensor networks (WBSN) in medical services aims at providing continuous monitoring of patients' physiological data. However, the scarce resources in WBSN nodes limit their capabilities to cope with massive traffic during multiple, simultaneous data transmissions. This will create a high tendency for congestion, causing severe performance degradation. Congestion may lead to high number of packet loss and unbounded delay which are critical and may lead to wrong diagnosis. This paper, therefore, aims at improving this limitation using a novel congestion avoidance technique to avoid losing real-time and life-critical medical data (e.g., electrocardiogram and electroencephalography) which are vital for diagnosis. The main idea is to integrate the existing rate control scheme of relaxation theory (RT) with a method known as max-min fairness (MMF) to achieve better performance. The MMF can be accomplished using a progressive filling algorithm, which cuts-down excessive sending rates that may overwhelme the limited buffer in WBSN. This paper builds upon our prior study, which provides a preliminary analysis of RT technique in single node. Our current technique integrates the MMF phase to enhance RT performance when the transmission rates exceed certain threshold. Performance evaluation on RT-MMF technique shows remarkable performance improvements, while maintaining the desired quality of service.

SME2EM: Smart mobile end-to-end monitoring architecture for life-long diseases

Monitoring life-long diseases requires continuous measurements and recording of physical vital signs. Most of these diseases are manifested through unexpected and non-uniform occurrences and behaviors. It is impractical to keep patients in hospitals, health-care institutions, or even at home for long periods of time. Monitoring solutions based on smartphones combined with mobile sensors and wireless communication technologies are a potential candidate to support complete mobility-freedom, not only for patients, but also for physicians. However, existing monitoring architectures based on smartphones and modern communication technologies are not suitable to address some challenging issues, such as intensive and big data, resource constraints, data integration, and context awareness in an integrated framework. This manuscript provides a novel mobile-based end-to-end architecture for live monitoring and visualization of life-long diseases. The proposed architecture provides smartness features to cope with continuous monitoring, data explosion, dynamic adaptation, unlimited mobility, and constrained devices resources. The integration of the architecture׳s components provides information about diseases׳ recurrences as soon as they occur to expedite taking necessary actions, and thus prevent severe consequences. Our architecture system is formally model-checked to automatically verify its correctness against designers׳ desirable properties at design time. Its components are fully implemented as Web services with respect to the SOA architecture to be easy to deploy and integrate, and supported by Cloud infrastructure and services to allow high scalability, availability of processes and data being stored and exchanged. The architecture׳s applicability is evaluated through concrete experimental scenarios on monitoring and visualizing states of epileptic diseases. The obtained theoretical and experimental results are very promising and efficiently satisfy the proposed architecture׳s objectives, including resource awareness, smart data integration and visualization, cost reduction, and performance guarantee.

Automatic UPDRS Evaluation in the Sit-to-Stand Task of Parkinsonians: Kinematic Analysis and Comparative Outlook on the Leg Agility Task

In this study, we first characterize the sit-to-stand (S2S) task, which contributes to the evaluation of the degree of severity of the Parkinson's disease (PD), through kinematic features, which are then linked to the Unified Parkinson's disease rating scale (UPDRS) scores. We propose to use a single body-worn wireless inertial node placed on the chest of a patient. The experimental investigation is carried out considering 24 PD patients, comparing the obtained results directly with the kinematic characterization of the leg agility (LA) task performed by the same set of patients. We show that i) the S2S and LA tasks are rather unrelated and ii) the UPDRS distributions (for both S2S and LA tasks) across the patients have a direct impact on the observed system performance.

Objective Assessment of Upper-Limb Mobility for Poststroke Rehabilitation

The assessment of the limb mobility of stroke patients is an essential part of poststroke rehabilitation. Conventionally, the assessment is manually performed by clinicians using chart-based ordinal scales, which can be subjective and inefficient. By introducing quantitative evaluation measures, the sensitivity and efficiency of the assessment process can be significantly improved. In this paper, a novel single-index-based assessment approach for quantitative upper-limb mobility evaluation has been proposed for poststroke rehabilitation. Instead of the traditional human-observation-based measures, the proposed assessment system utilizes the kinematic information automatically collected during a regular rehabilitation training exercise using a wearable inertial measurement unit. By calculating a single index, the system can efficiently generate objective and consistent quantitative results that can reflect the stroke patient's upper-limb mobility. In order to verify and validate the proposed assessment system, experiments have been conducted using 145 motion samples collected from 21 stroke patients (12 males, nine females, mean age 58.7±19.3) and eight healthy participants. The results have suggested that the proposed assessment index can not only differentiate the levels of limb function impairment clearly (p < 0.001, two-tailed Welch's t-test), but also strongly correlate with the Brunnstrom stages of recovery (r = 0.86, p < 0.001). The assessment index is also proven to have great potential in automatic Brunnstrom stage classification application with an 82.1% classification accuracy, while using a K-nearest-neighbor classifier.

Technologies for Assessment of Motor Disorders in Parkinson's Disease: A Review

Parkinson’s Disease (PD) is characterized as the commonest neurodegenerative illness that gradually degenerates the central nervous system. The goal of this review is to come out with a summary of the recent progress of numerous forms of sensors and systems that are related to diagnosis of PD in the past decades. The paper reviews the substantial researches on the application of technological tools (objective techniques) in the PD field applying different types of sensors proposed by previous researchers. In addition, this also includes the use of clinical tools (subjective techniques) for PD assessments, for instance, patient self-reports, patient diaries and the international gold standard reference scale, Unified Parkinson Disease Rating Scale (UPDRS). Comparative studies and critical descriptions of these approaches have been highlighted in this paper, giving an insight on the current state of the art. It is followed by explaining the merits of the multiple sensor fusion platform compared to single sensor platform for better monitoring progression of PD, and ends with thoughts about the future direction towards the need of multimodal sensor integration platform for the assessment of PD.

Forward kinematics using IMU on-body sensor network for mobile analysis of human kinematics

The feasibility of large network inertial measurement units (IMUs) are evaluated for purposes requiring feedback. A series of wireless IMUs were attached to a human lower-limb laboratory model outfitted with joint angle encoders. The goal was to discover if large networks of wireless IMUs can give realtime joint orientation data while still maintaining an acceptable degree of accuracy.

An Emerging Era in the Management of Parkinson's Disease: Wearable Technologies and the Internet of Things

Current challenges demand a profound restructuration of the global healthcare system. A more efficient system is required to cope with the growing world population and increased life expectancy, which is associated with a marked prevalence of chronic neurological disorders such as Parkinson's disease (PD). One possible approach to meet this demand is a laterally distributed platform such as the Internet of Things (IoT). Real-time motion metrics in PD could be obtained virtually in any scenario by placing lightweight wearable sensors in the patient's clothes and connecting them to a medical database through mobile devices such as cell phones or tablets. Technologies exist to collect huge amounts of patient data not only during regular medical visits but also at home during activities of daily life. These data could be fed into intelligent algorithms to first discriminate relevant threatening conditions, adjust medications based on online obtained physical deficits, and facilitate strategies to modify disease progression. A major impact of this approach lies in its efficiency, by maximizing resources and drastically improving the patient experience. The patient participates actively in disease management via combined objective device- and self-assessment and by sharing information within both medical and peer groups. Here, we review and discuss the existing wearable technologies and the Internet-of-Things concept applied to PD, with an emphasis on how this technological platform may lead to a shift in paradigm in terms of diagnostics and treatment.

A mobile cloud-based Parkinson's disease assessment system for home-based monitoring

BACKGROUND

Parkinson's disease (PD) is the most prevalent movement disorder of the central nervous system, and affects more than 6.3 million people in the world. The characteristic motor features include tremor, bradykinesia, rigidity, and impaired postural stability. Current therapy based on augmentation or replacement of dopamine is designed to improve patients' motor performance but often leads to levodopa-induced adverse effects, such as dyskinesia and motor fluctuation. Clinicians must regularly monitor patients in order to identify these effects and other declines in motor function as soon as possible. Current clinical assessment for Parkinson's is subjective and mostly conducted by brief observations made during patient visits. Changes in patients' motor function between visits are hard to track and clinicians are not able to make the most informed decisions about the course of therapy without frequent visits. Frequent clinic visits increase the physical and economic burden on patients and their families.

OBJECTIVE

In this project, we sought to design, develop, and evaluate a prototype mobile cloud-based mHealth app, "PD Dr", which collects quantitative and objective information about PD and would enable home-based assessment and monitoring of major PD symptoms.

METHODS

We designed and developed a mobile app on the Android platform to collect PD-related motion data using the smartphone 3D accelerometer and to send the data to a cloud service for storage, data processing, and PD symptoms severity estimation. To evaluate this system, data from the system were collected from 40 patients with PD and compared with experts' rating on standardized rating scales.

RESULTS

The evaluation showed that PD Dr could effectively capture important motion features that differentiate PD severity and identify critical symptoms. For hand resting tremor detection, the sensitivity was .77 and accuracy was .82. For gait difficulty detection, the sensitivity was .89 and accuracy was .81. In PD severity estimation, the captured motion features also demonstrated strong correlation with PD severity stage, hand resting tremor severity, and gait difficulty. The system is simple to use, user friendly, and economically affordable.

CONCLUSIONS

The key contribution of this study was building a mobile PD assessment and monitoring system to extend current PD assessment based in the clinic setting to the home-based environment. The results of this study proved feasibility and a promising future for utilizing mobile technology in PD management.