Time Series Analysis of Muscle Deformation During Physiotherapy Using Optical Wearable Sensors

Area of Science:

• Biomechanics
• Rehabilitation Technology

Background:

• Standardized methods for analyzing wearable sensor data in physiotherapy are lacking.
• Interpreting muscle force and coordination data across studies is challenging due to device and analytical variability.

Purpose of the Study:

• To compare time series data of muscle force and coordination between physiotherapy novices and experts.
• To identify differences in muscle activation patterns and coordination strategies during physiotherapy sessions.

Main Methods:

• Utilized optical wearable muscle deformation sensor arrays to capture muscle bulging and estimate force levels.
• Analyzed time series data from upper and lower limb physiotherapy sessions using visualization, autocorrelation, and cross-correlation analysis.
• Compared data between novice and expert groups, adjusting for covariates.

Main Results:

• Visual and effect size differences in muscle force and coordination were observed between novices and experts.
• No statistically significant group differences persisted after covariate adjustment.
• Identified measurable distinctions in the magnitude of force and muscle coordination patterns.

Conclusions:

• Novices and experts exhibit distinct muscle activation patterns and coordination strategies during physiotherapy.
• Findings suggest potential applications for wearable sensor technology in physiotherapy training and skill assessment.
• Highlights the need for standardized approaches in wearable biometric data analysis for clinical applications.