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Updated: May 2, 2026

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

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Multifunctional Human-Computer Interaction System Based on Deep Learning-Assisted Strain Sensing Array.

Hao Gu^1,2, Ke Jiang², Fei Yu¹

¹Key Laboratory of Advanced Structural Materials, Ministry of Education & Advanced Institute of Materials Science, Changchun University of Technology, Changchun 130012, China.

ACS Applied Materials & Interfaces

|September 26, 2024

Summary

This study introduces an intelligent gait monitoring system using flexible piezoelectric sensors and deep learning. The system achieves high accuracy in real-time motion state recognition, enabling continuous health tracking.

Keywords:

convolutional neural network gait analysis human–computer interaction system strain sensing array telemedicine

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Area of Science:

Biomedical Engineering
Sensor Technology
Artificial Intelligence

Background:

Continuous gait monitoring is vital for health management, including post-surgery recovery and disease diagnosis.
Current gait analysis systems are often cumbersome, requiring specialized spaces and limiting real-world application.

Purpose of the Study:

To develop an intelligent gait monitoring and analysis system using flexible piezoelectric sensors and deep learning.
To enable real-time, accurate gait data acquisition and motion state inference for health monitoring.

Main Methods:

Flexible piezoelectric sensors with high sensitivity and stability were developed and integrated into shoe soles.
Deep learning neural networks were employed to analyze the acquired gait data.
A flexible wearable recognition system with a human-computer interaction interface was constructed.

Main Results:

The piezoelectric sensors demonstrated high sensitivity (241.29 mV/N), quick response, and excellent stability (R^2 = 0.9946).
The integrated system achieved 94.7% accuracy in real-time human motion state recognition.
The system successfully tracked athletes' gait for long-term monitoring.

Conclusions:

The developed system offers a practical solution for continuous and reliable gait analysis in daily life.
This technology has the potential to aid personalized health management, early disease detection, and remote medical care.