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

**Authors:** Satoshi Shimabukuro, Tamon Miyake, Emi Tamaki

PMC · DOI: 10.3390/s25113507 · Sensors (Basel, Switzerland) · 2025-06-02

## TL;DR

This study uses optical wearable sensors to analyze muscle deformation during physiotherapy, finding differences in force and coordination between novices and experts.

## Contribution

The study introduces optical wearable sensors to capture and compare muscle deformation time series data between novices and experts in physiotherapy.

## Key findings

- Differences in muscle deformation patterns were observed visually and in effect sizes between novices and experts.
- Analysis revealed measurable distinctions in force magnitude and muscle coordination strategies.
- Findings suggest potential applications for training and skill evaluation in physiotherapy.

## Abstract

Wearable devices are used to acquire and analyze biometric information. However, the lack of consensus on standardized devices and analytical methods for representing the time series data of muscle force and coordination has hindered the interpretation and comparison of such data across studies. This study aimed to compare time series data between novices and experts during physiotherapy sessions to identify differences in the degree of force and coordination. Optical wearable muscle deformation sensor arrays were used to capture muscle bulging (muscle deformation) and visualize the force levels. Two types of physiotherapy sessions were conducted in the upper and lower limbs, and the time series data of muscle deformations collected during these sessions were analyzed using visualization, autocorrelation coefficients, and cross-correlation analysis. Although differences were observed visually and in effect sizes, no statistically significant group differences remained after covariate adjustment. The results revealed differences in the magnitude of force and muscle coordination. These findings highlight measurable distinctions in muscle activation patterns and coordination strategies between novices and experts, suggesting potential applications for training and skill evaluation in physiotherapy and related domains.

## Full-text entities

- **Diseases:** muscle (MESH:D019042), Muscle Deformation (MESH:D009135)

## Full text

_Full body text omitted from this summary view._ Fetch the complete paper as Markdown: https://tomesphere.com/paper/PMC12158351/full.md

## Figures

8 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12158351/full.md

## References

39 references — full list in the complete paper: https://tomesphere.com/paper/PMC12158351/full.md

---
Source: https://tomesphere.com/paper/PMC12158351