# Muscle Fatigue Assessment in Healthcare Application by Using Surface Electromyography: A Transfer Learning Approach

## TL;DR

This paper presents a deep learning method using muscle signals to detect fatigue levels in elderly and non-elderly adults, aiming to improve safety in assisted living environments.

## Contribution

A novel transfer learning framework for muscle fatigue classification using surface electromyography and time–frequency images.

## Key findings

- The system achieved 98.6% accuracy in binary fatigue classification.
- It reached 95.6% accuracy in a three-level fatigue classification task.
- The method outperformed traditional machine learning approaches with manually extracted features.

## Abstract

Monitoring muscle fatigue is essential to ensure safety and support activity in populations such as the elderly. This study introduces a novel deep learning framework for classifying muscle fatigue levels using data from wireless surface electromyographic sensors, with the long-term goal of supporting applications in Ambient Assisted Living. A new dataset was collected from healthy elderly and non-elderly adults performing dynamic tasks under controlled conditions, with muscle fatigue levels labelled through self-assessment. The proposed method employs a pipeline that transforms one-dimensional electromyographic signals into two-dimensional time–frequency images (scalograms) using the Continuous Wavelet Transform, which are then classified by a fine-tuned, pre-trained Convolutional Neural Network. These images are then classified by pretrained Convolutional Neural Networks on large-scale image datasets. The classification pipeline includes an initial binary discrimination between non-fatigued and fatigued conditions, followed by a refined three-level classification into No Fatigue, Moderate Fatigue, and Hard Fatigue. The system achieved an accuracy of 98.6% in the binary task and 95.6% in the multiclass setting. This integrated transfer learning pipeline outperformed traditional Machine Learning methods based on manually extracted features, which reached a maximum of 92% accuracy. These findings highlight the robustness and generalizability of the proposed approach, supporting its potential as a real-time, non-invasive muscle fatigue monitoring solution tailored to Ambient Assisted Living scenarios.