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A Framework to Automate Assessment of Upper-Limb Motor Function Impairment: A Feasibility Study
- A Framework to Automate Assessment of Upper-Limb Motor Function Impairment: A Feasibility Study
- Otten, P[Otten, Paul]; Kim, J[Kim, Jonghyun]; Son, SH[Son, Sang Hyuk]
- DGIST Authors
- Kim, J[Kim, Jonghyun]; Son, SH[Son, Sang Hyuk]
- Issue Date
- Sensors, 15(8), 20097-20114
- Article Type
- Artificial Intelligence; Automated Upper-Limb Assessment; Clinical Environments; Feasibility Studies; Fugl-Meyer Assessment; Fugl-Meyer Assessments; Learning Algorithms; Learning Systems; Low-Cost Sensors; Machine Learning; Motor Impairments; Movement Datum; Neurological Disorders; Upper-Limb Motor Impairment; Upper-Limbs
- 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. © 2015 by the authors; licensee MDPI, Basel, Switzerland.
- MDPI AG
- Related Researcher
Son, Sang Hyuk
RTCPS(Real-Time Cyber-Physical Systems Research) Lab
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