Real-time Monitoring of Lower Limb Movement Resistance Based on Deep Learning

TL;DR

This paper introduces MMTL-Net, a deep learning model that enables accurate, real-time lower limb resistance monitoring using multi-task learning and MobileNetV3, improving clinical and sports applications.

Contribution

The paper presents a novel multi-task learning network that combines MobileNetV3 for efficient feature extraction, achieving superior accuracy and speed in resistance and activity prediction.

Findings

Lower Force Error Rate of 6.8%

Resistance Prediction Accuracy of 91.2%

Real-time Responsiveness of 12 ms

Abstract

Real-time lower limb movement resistance monitoring is critical for various applications in clinical and sports settings, such as rehabilitation and athletic training. Current methods often face limitations in accuracy, computational efficiency, and generalizability, which hinder their practical implementation. To address these challenges, we propose a novel Mobile Multi-Task Learning Network (MMTL-Net) that integrates MobileNetV3 for efficient feature extraction and employs multi-task learning to simultaneously predict resistance levels and recognize activities. The advantages of MMTL-Net include enhanced accuracy, reduced latency, and improved computational efficiency, making it highly suitable for real-time applications. Experimental results demonstrate that MMTL-Net significantly outperforms existing models on the UCI Human Activity Recognition and Wireless Sensor Data Mining Activity Prediction datasets, achieving a lower Force Error Rate (FER) of 6.8% and a higher Resistance Prediction Accuracy (RPA) of 91.2%. Additionally, the model shows a Real-time Responsiveness (RTR) of 12 milliseconds and a Throughput (TP) of 33 frames per second. These findings underscore the model's robustness and effectiveness in diverse real-world scenarios. The proposed framework not only advances the state-of-the-art in resistance monitoring but also paves the way for more efficient and accurate systems in clinical and sports applications. In real-world settings, the practical implications of MMTL-Net include its potential to enhance patient outcomes in rehabilitation and improve athletic performance through precise, real-time monitoring and feedback.