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      A Framework to Automate Assessment of Upper-Limb Motor Function Impairment: A Feasibility Study

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          Abstract

          Standard upper-limb motor function impairment assessments, such as the Fugl-Meyer Assessment (FMA), are a critical aspect of rehabilitation after neurological disorders. These assessments typically take a long time (about 30 min for the FMA) for a clinician to perform on a patient, which is a severe burden in a clinical environment. In this paper, we propose a framework for automating upper-limb motor assessments that uses low-cost sensors to collect movement data. The sensor data is then processed through a machine learning algorithm to determine a score for a patient’s upper-limb functionality. To demonstrate the feasibility of the proposed approach, we implemented a system based on the proposed framework that can automate most of the FMA. Our experiment shows that the system provides similar FMA scores to clinician scores, and reduces the time spent evaluating each patient by 82%. Moreover, the proposed framework can be used to implement customized tests or tests specified in other existing standard assessment methods.

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          Most cited references34

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          A performance test for assessment of upper limb function in physical rehabilitation treatment and research.

          R C Lyle (1981)
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            Fugl-Meyer assessment of sensorimotor function after stroke: standardized training procedure for clinical practice and clinical trials.

            Outcome measurement fidelity within and between sites of multi-site, randomized, clinical trials is an essential element to meaningful trial outcomes. As important are the methods developed for randomized, clinical trials that can have practical utility for clinical practice. A standardized measurement method and rater training program were developed for the total Fugl-Meyer motor and sensory assessments; inter-rater reliability was used to test program effectiveness. Fifteen individuals with hemiparetic stroke, 17 trained physical therapists across 5 regional clinical sites, and an expert rater participated in an inter-rater reliability study of the Fugl-Meyer motor (total, upper extremity, and lower extremity subscores) and sensory (total, light touch, and proprioception subscores) assessments. Intra-rater reliability for the expert rater was high for the motor and sensory scores (range, 0.95-1.0). Inter-rater agreement (intraclass correlation coefficient, 2, 1) between expert and therapist raters was high for the motor scores (total, 0.98; upper extremity, 0.99; lower extremity, 0.91) and sensory scores (total, 0.93; light touch, 0.87; proprioception, 0.96). Standardized measurement methods and training of therapist assessors for a multi-site, rehabilitation, randomized, clinical trial resulted in high inter-rater reliability for the Fugl-Meyer motor and sensory assessments. Poststroke sensorimotor impairment severity can be reliably assessed for clinical practice or rehabilitation research with these methods.
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              Accuracy of the Microsoft Kinect sensor for measuring movement in people with Parkinson's disease.

              The Microsoft Kinect sensor (Kinect) is potentially a low-cost solution for clinical and home-based assessment of movement symptoms in people with Parkinson's disease (PD). The purpose of this study was to establish the accuracy of the Kinect in measuring clinically relevant movements in people with PD. Nine people with PD and 10 controls performed a series of movements which were measured concurrently with a Vicon three-dimensional motion analysis system (gold-standard) and the Kinect. The movements included quiet standing, multidirectional reaching and stepping and walking on the spot, and the following items from the Unified Parkinson's Disease Rating Scale: hand clasping, finger tapping, foot, leg agility, chair rising and hand pronation. Outcomes included mean timing and range of motion across movement repetitions. The Kinect measured timing of movement repetitions very accurately (low bias, 95% limits of agreement 0.9 and Pearson's r>0.9). However, the Kinect had varied success measuring spatial characteristics, ranging from excellent for gross movements such as sit-to-stand (ICC=.989) to very poor for fine movement such as hand clasping (ICC=.012). Despite this, results from the Kinect related strongly to those obtained with the Vicon system (Pearson's r>0.8) for most movements. The Kinect can accurately measure timing and gross spatial characteristics of clinically relevant movements but not with the same spatial accuracy for smaller movements, such as hand clasping. Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                14 August 2015
                August 2015
                : 15
                : 8
                : 20097-20114
                Affiliations
                [1 ]Epic Systems Corporation, 1979 Milky Way, Verona, WI 53705, USA; E-Mail: pco890@ 123456gmail.com
                [2 ]Department of Robotics Engineering, DGIST, 333 Techno jungang-daero, Hyeonpung-myeon, Dalseong-gun, Daegu 42988, Korea
                [3 ]Department of Information and Communication Engineering, DGIST, 333 Techno jungang-daero, Hyeonpung-myeon, Dalseong-gun, Daegu 42988, Korea
                Author notes
                [†]

                These authors contributed equally to this work.

                [* ]Authors to whom correspondence should be addressed; E-Mails: jhkim@ 123456dgist.ac.kr (J.K.); son@ 123456dgist.ac.kr (S.H.S.); Tel.: +82-53-785-6211 (J.K.); Fax: +82-53-785-6209 (J.K.).
                Article
                sensors-15-20097
                10.3390/s150820097
                4570412
                26287206
                9148b870-acc5-4df9-b691-56a9afb1de81
                © 2015 by the authors; licensee MDPI, Basel, Switzerland.

                This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 30 June 2015
                : 07 August 2015
                Categories
                Article

                Biomedical engineering
                automated upper-limb assessment,fugl-meyer assessment,low-cost sensors,machine learning,upper-limb motor impairment

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