A Framework to Automate Assessment of Upper-Limb Motor Function Impairment: A Feasibility Study.
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.
Epic Systems Corporation, 1979 Milky Way, Verona, WI 53705, USA. pco890@gmail.com.
Paul
P
Otten
Department of Robotics Engineering, DGIST, 333 Techno jungang-daero, Hyeonpung-myeon, Dalseong-gun, Daegu 42988, Korea. jhkim@dgist.ac.kr.
Jonghyun
J
Kim
Department of Information and Communication Engineering, DGIST, 333 Techno jungang-daero, Hyeonpung-myeon, Dalseong-gun, Daegu 42988, Korea. son@dgist.ac.kr.
Sang Hyuk
SH
Son
D061725:Accelerometry / Q000295:instrumentation; D000465:Algorithms; D001331:Automation; D005240:Feasibility Studies; D064368:Healthy Volunteers; D006801:Humans; D009043:Motor Activity / Q000502:physiology*; D020521:Stroke / Q000503:physiopathology; D034941:Upper Extremity / Q000503:physiopathology*; D014584:User-Computer Interface
Fugl-Meyer Assessment; automated upper-limb assessment; low-cost sensors; machine learning; upper-limb motor impairment