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      How Wearable Sensors Can Support Parkinson's Disease Diagnosis and Treatment: A Systematic Review

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          Abstract

          Background: Parkinson's disease (PD) is a common and disabling pathology that is characterized by both motor and non-motor symptoms and affects millions of people worldwide. The disease significantly affects quality of life of those affected. Many works in literature discuss the effects of the disease. The most promising trends involve sensor devices, which are low cost, low power, unobtrusive, and accurate in the measurements, for monitoring and managing the pathology. Objectives: This review focuses on wearable devices for PD applications and identifies five main fields: early diagnosis, tremor, body motion analysis, motor fluctuations (ON–OFF phases), and home and long-term monitoring. The concept is to obtain an overview of the pathology at each stage of development, from the beginning of the disease to consider early symptoms, during disease progression with analysis of the most common disorders, and including management of the most complicated situations (i.e., motor fluctuations and long-term remote monitoring). Data sources: The research was conducted within three databases: IEEE Xplore®, Science Direct®, and PubMed Central®, between January 2006 and December 2016. Study eligibility criteria: Since 1,429 articles were found, accurate definition of the exclusion criteria and selection strategy allowed identification of the most relevant papers. Results: Finally, 136 papers were fully evaluated and included in this review, allowing a wide overview of wearable devices for the management of Parkinson's disease.

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          Most cited references155

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          iTUG, a sensitive and reliable measure of mobility.

          Timed Up and Go (TUG) test is a widely used clinical paradigm to evaluate balance and mobility. Although TUG includes several complex subcomponents, namely: sit-to-stand, gait, 180 degree turn, and turn-to-sit; the only outcome is the total time to perform the task. We have proposed an instrumented TUG, called iTUG, using portable inertial sensors to improve TUG in several ways: automatic detection and separation of subcomponents, detailed analysis of each one of them and a higher sensitivity than TUG. Twelve subjects in early stages of Parkinson's disease (PD) and 12 age matched control subjects were enrolled. Stopwatch measurements did not show a significant difference between the two groups. The iTUG, however, showed a significant difference in cadence between early PD and control subjects (111.1 +/- 6.2 versus 120.4 +/- 7.6 step/min, p < 0.006) as well as in angular velocity of arm-swing (123 +/- 32.0 versus 174.0+/-50.4 degrees/s, p < 0.005), turning duration (2.18 +/- 0.43 versus 1.79 +/- 0.27 s, p < 0.023), and time to perform turn-to-sits (2.96 +/- 0.68 versus 2.40 +/- 0.33 s, p < 0.023). By repeating the tests for a second time, the test-retest reliability of iTUG was also evaluated. Among the subcomponents of iTUG, gait, turning, and turn-to-sit were the most reliable and sit-to-stand was the least reliable.
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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.
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              Rivastigmine for gait stability in patients with Parkinson's disease (ReSPonD): a randomised, double-blind, placebo-controlled, phase 2 trial.

              Falls are a frequent and serious complication of Parkinson's disease and are related partly to an underlying cholinergic deficit that contributes to gait and cognitive dysfunction in these patients. Gait dysfunction can lead to an increased variability of gait from one step to another, raising the likelihood of falls. In the ReSPonD trial we aimed to assess whether ameliorating this cholinergic deficit with the acetylcholinesterase inhibitor rivastigmine would reduce gait variability.
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                Author and article information

                Contributors
                Journal
                Front Neurosci
                Front Neurosci
                Front. Neurosci.
                Frontiers in Neuroscience
                Frontiers Media S.A.
                1662-4548
                1662-453X
                06 October 2017
                2017
                : 11
                : 555
                Affiliations
                [1] 1The BioRobotics Institute, Scuola Superiore Sant'Anna , Pontedera, Italy
                [2] 2U.O. Neurologia, Ospedale delle Apuane (AUSL Toscana Nord Ovest) , Massa, Italy
                Author notes

                Edited by: Giovanni Mirabella, Sapienza Università di Roma, Italy

                Reviewed by: Wei Peng Teo, Deakin University, Australia; Pietro Cipresso, Istituto Auxologico Italiano (IRCCS), Italy; Elisa Pedroli, Istituto Auxologico Italiano (IRCCS), Italy; Giovanni Albani, Istituto Auxologico Italiano (IRCCS), Italy

                *Correspondence: Filippo Cavallo filippo.cavallo@ 123456santannapisa.it

                This article was submitted to Neural Technology, a section of the journal Frontiers in Neuroscience

                Article
                10.3389/fnins.2017.00555
                5635326
                28174515
                5dfb26af-580f-47e3-b94d-11501474fdcf
                Copyright © 2017 Rovini, Maremmani and Cavallo.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 20 June 2017
                : 21 September 2017
                Page count
                Figures: 2, Tables: 10, Equations: 0, References: 169, Pages: 41, Words: 30021
                Categories
                Neuroscience
                Review

                Neurosciences
                parkinson's disease,wearable sensors,motion analysis,early diagnosis,tremor,motor fluctuations,monitoring,telemedicine

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