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Sensors 2015, 15(8), 20097-20114; doi:10.3390/s150820097

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

1
,
2,†,* and 3,†,*
1
Epic Systems Corporation, 1979 Milky Way, Verona, WI 53705, USA
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
These authors contributed equally to this work.
*
Authors to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 30 June 2015 / Revised: 5 August 2015 / Accepted: 7 August 2015 / Published: 14 August 2015
(This article belongs to the Special Issue Integrated Intelligent Sensory Systems with Self-x Capabilities)
View Full-Text   |   Download PDF [1667 KB, uploaded 14 August 2015]   |  

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. View Full-Text
Keywords: automated upper-limb assessment; Fugl-Meyer Assessment; low-cost sensors; machine learning; upper-limb motor impairment automated upper-limb assessment; Fugl-Meyer Assessment; low-cost sensors; machine learning; upper-limb motor impairment
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Otten, P.; Kim, J.; Son, S.H. A Framework to Automate Assessment of Upper-Limb Motor Function Impairment: A Feasibility Study. Sensors 2015, 15, 20097-20114.

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