Automated Evaluation of Upper-Limb Motor Function Impairment Using Fugl-Meyer Assessment

Area of Science:

• Biomedical Engineering
• Rehabilitation Technology
• Neuroscience

Background:

• The Fugl-Meyer assessment (FMA) is a standard clinical tool for assessing upper extremity motor function post-stroke.
• Traditional FMA administration is resource-intensive, requiring significant clinician time and effort.
• Existing limitations hinder widespread and efficient application of the FMA in clinical settings.

Purpose of the Study:

• To develop and validate a novel automated system for the Fugl-Meyer assessment (FMA).
• To overcome the labor-intensive and time-consuming nature of the traditional FMA.
• To provide a clinically viable, automated solution for evaluating upper extremity motor function in stroke survivors.

Main Methods:

• Development of an automated FMA system utilizing Kinect v2 and force-sensing resistor sensors.
• Implementation of a rule-based binary logic classification algorithm for FMA score assignment based on sensor data.
• Clinical validation involving nine stroke patients to assess system performance.

Main Results:

• The automated system successfully automated 79% of FMA test components.
• Clinical trials demonstrated high scoring accuracy of 92% compared to manual FMA scoring.
• Significant time efficiency was achieved, with an 85% reduction in clinician time required.

Conclusions:

• The proposed automated FMA system offers a practical and efficient alternative to traditional methods.
• The system's non-reliance on machine learning makes it readily applicable in clinical settings without extensive data requirements.
• This technology has the potential to enhance the accessibility and consistency of motor function assessment in stroke rehabilitation.