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      Technology in Parkinson's disease: Challenges and opportunities : Technology in PD

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          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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          Multifunctional wearable devices for diagnosis and therapy of movement disorders.

          Wearable systems that monitor muscle activity, store data and deliver feedback therapy are the next frontier in personalized medicine and healthcare. However, technical challenges, such as the fabrication of high-performance, energy-efficient sensors and memory modules that are in intimate mechanical contact with soft tissues, in conjunction with controlled delivery of therapeutic agents, limit the wide-scale adoption of such systems. Here, we describe materials, mechanics and designs for multifunctional, wearable-on-the-skin systems that address these challenges via monolithic integration of nanomembranes fabricated with a top-down approach, nanoparticles assembled by bottom-up methods, and stretchable electronics on a tissue-like polymeric substrate. Representative examples of such systems include physiological sensors, non-volatile memory and drug-release actuators. Quantitative analyses of the electronics, mechanics, heat-transfer and drug-diffusion characteristics validate the operation of individual components, thereby enabling system-level multifunctionalities.
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            Is Open Access

            Unobtrusive Sensing and Wearable Devices for Health Informatics

            The aging population, prevalence of chronic diseases, and outbreaks of infectious diseases are some of the major challenges of our present-day society. To address these unmet healthcare needs, especially for the early prediction and treatment of major diseases, health informatics, which deals with the acquisition, transmission, processing, storage, retrieval, and use of health information, has emerged as an active area of interdisciplinary research. In particular, acquisition of health-related information by unobtrusive sensing and wearable technologies is considered as a cornerstone in health informatics. Sensors can be weaved or integrated into clothing, accessories, and the living environment, such that health information can be acquired seamlessly and pervasively in daily living. Sensors can even be designed as stick-on electronic tattoos or directly printed onto human skin to enable long-term health monitoring. This paper aims to provide an overview of four emerging unobtrusive and wearable technologies, which are essential to the realization of pervasive health information acquisition, including: 1) unobtrusive sensing methods, 2) smart textile technology, 3) flexible-stretchable-printable electronics, and 4) sensor fusion, and then to identify some future directions of research.
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              Monitoring motor fluctuations in patients with Parkinson's disease using wearable sensors.

              This paper presents the results of a pilot study to assess the feasibility of using accelerometer data to estimate the severity of symptoms and motor complications in patients with Parkinson's disease. A support vector machine (SVM) classifier was implemented to estimate the severity of tremor, bradykinesia and dyskinesia from accelerometer data features. SVM-based estimates were compared with clinical scores derived via visual inspection of video recordings taken while patients performed a series of standardized motor tasks. The analysis of the video recordings was performed by clinicians trained in the use of scales for the assessment of the severity of Parkinsonian symptoms and motor complications. Results derived from the accelerometer time series were analyzed to assess the effect on the estimation of clinical scores of the duration of the window utilized to derive segments (to eventually compute data features) from the accelerometer data, the use of different SVM kernels and misclassification cost values, and the use of data features derived from different motor tasks. Results were also analyzed to assess which combinations of data features carried enough information to reliably assess the severity of symptoms and motor complications. Combinations of data features were compared taking into consideration the computational cost associated with estimating each data feature on the nodes of a body sensor network and the effect of using such data features on the reliability of SVM-based estimates of the severity of Parkinsonian symptoms and motor complications.

                Author and article information

                Movement Disorders
                Mov Disord.
                September 2016
                September 2016
                April 29 2016
                : 31
                : 9
                : 1272-1282
                [1 ]James J. and Joan A. Gardner Family Center for Parkinson's disease and Movement Disorders; University of Cincinnati; Cincinnati Ohio USA
                [2 ]Department of Physical Medicine and Rehabilitation; Harvard Medical School; Boston Massachusetts USA
                [3 ]Department of Neurosciences; University of California San Diego; La Jolla CA USA
                [4 ]Department of Neurodegeneration, Hertie Institute for Clinical Brain Research (HIH); University of Tuebingen; Tübingen Germany
                [5 ]DZNE; German Center for Neurodegenerative Diseases; Tübingen Germany
                [6 ]Davis Phinney Foundation for Parkinson's; Boulder Colorado USA
                [7 ]Department of Molecular Neurology, University Hospital Erlangen; Friedrich-Alexander University Erlangen-Nürnberg; Erlangen Germany
                [8 ]Digital Sports Group; Friedrich-Alexander University Erlangen-Nürnberg; Erlangen Germany
                [9 ]Department of Neuroscience “Rita Levi Montalcini”; Città della salute e della scienza di Torino; Torino Italy
                [10 ]Department of Neurology, Oregon Health & Science University; Portland VA Medical System; Portland Oregon
                [11 ]APDM, Inc.; Portland Oregon USA
                [12 ]Morton and Gloria Movement Disorders Clinic and the Edmond J. Safra Program in Parkinson's Disease; Toronto Western Hospital; Toronto Canada
                [13 ]George-Huntington-Institute; Muenster Germany
                [14 ]Department of Radiology; University of Muenster; Muenster Germany
                [15 ]Department of Neurodegenerative Diseases and Hertie-Institute for Clinical Brain Research; University of Tuebingen; Tuebingen Germany
                [16 ]Great Lakes NeuroTechnologies; Cleveland Ohio USA
                [17 ]Neuromotor Rehabilitation Research Group, Department of Rehabilitation Sciences; KU Leuven; Leuven Belgium
                [18 ]Global Kinetics Corporation & Florey Institute for Neuroscience and Mental Health; University of Melbourne; Parkville Victoria Australia
                [19 ]Department of Mathematics; Aston University; Birmingham UK
                [20 ]Media Lab; Massachusetts Institute of Technology; Cambridge Massachusetts USA
                [21 ]Department of Neurology, Feinberg School of Medicine; Northwestern University; Chicago Illinois USA
                [22 ]Department of Neurology; University of Rochester Medical Center; Rochester New York USA
                [23 ]Michael J Fox Foundation for Parkinson's Research; New York City New York USA
                [24 ]Department of Neurology; National Institute of Clinical Neurosciences; Budapest Hungary
                [25 ]Center of Interdisciplinary Research Egas Moniz (CiiEM); Instituto Superior de Ciências da Saúde Egas Moniz; Monte de Caparica Portugal
                [26 ]Apptomics LLC; Wellesley Massachusetts USA
                [27 ]Medtronic Neuromodulation; Minneapolis Minnesota USA
                [28 ]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
                [29 ]Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology; Nijmegen the Netherlands
                [30 ]Massachusetts General Hospital; Boston Massachusetts USA
                © 2016




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