Human Upper Limb Motion Analysis for Post-Stroke Impairment Assessment Using Video Analytics

Abstract

Stroke is a worldwide healthcare problem, which often causes long-term motor impairment, handicap, and disability. Optical motion analysis systems are commonly used for impairment assessment due to high accuracy. However, the requirement of equipment-heavy and large laboratory space together with operational expertise makes these systems impractical for local clinic and home use. We propose an alternative, cost-effective and portable, decision support system for optical motion analysis, using a single camera. The system relies on detecting and tracking markers attached to subject's joints, data analytics for calculating relevant rehabilitation parameters, visualization, and robust classification based on graph-based signal processing. Experimental results show that the proposed decision support system has the potential to offer stroke survivors and clinicians an alternative, affordable, accurate, and convenient impairment assessment option suitable for home healthcare and telerehabilitation.

Keywords

• Rehabilitation
• Graph-based signal processing
• Video analytics