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

Assessing upper-limb motor function impairment, such as Fugl-Meyer Assessment (FMA), is a critical aspect of rehabilitation after neurological disorders. The assessment typically takes long time (about 30 minutes for FMA) for clinician to perform on a patient, which is a severe burden in a clinical environment. In this paper, we propose a framework for automating the assessment 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 framework that can automate most parts of the FMA. Our experiment shows that the system provides similar FMA scores to in-person setup, and reduces time spent evaluating each patient by 82%. Moreover, the proposed framework can be used to implement customized tests or tests specified in other existing assessment methods.

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People

  • Paul Otten (pcotten@dgist.ac.kr)
  • Jonghyun Kim (jhkim@dgist.ac.kr)
  • Sang Hyuk Son (son@dgist.ac.kr)

Reference

Paul Otten, Jonghyun Kim, Sang Hyuk Son, “A Framework to Automate Assessment of Upper-Limb Motor Function Impairment: A Feasibility Study”, Sensors, 2.245, Vol. 15(8), pp20097-20114, Aug. 2015.