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Related Experiment Video

Updated: Jan 14, 2026

Home-Based Monitor for Gait and Activity Analysis

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

Intelligent Gait Analysis System Enabled by Liquid Metal-Embedded Sponge Triboelectric Sensor Arrays.

Hongwei Liao¹, Wandi Chen², Yun Ye^2,3

¹School of Advanced Manufacturing, Fuzhou University, Quanzhou 362251, China.

ACS Applied Materials & Interfaces

|October 26, 2025

Summary

This summary is machine-generated.

This study introduces a wearable gait monitoring system using flexible nanogenerators and AI. The system achieves high accuracy for early disease detection and health assessment.

Keywords:

deep learning gait recognition liquid metal sponge triboelectric nanogenerator wearable health monitoring

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Last Updated: Jan 14, 2026

Home-Based Monitor for Gait and Activity Analysis

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Published on: August 8, 2019

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Lower-Limb Biomechanical Characteristics Associated with Unplanned Gait Termination Under Different Walking Speeds

Published on: August 25, 2020

Paw-Print Analysis of Contrast-Enhanced Recordings PrAnCER: A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits

Paw-Print Analysis of Contrast-Enhanced Recordings PrAnCER: A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits

Published on: August 12, 2019

Area of Science:

Biomedical Engineering
Materials Science
Artificial Intelligence

Background:

Gait dynamics are crucial biomarkers for health assessment and disease prediction.
Existing gait monitoring methods often lack comfort and long-term usability.

Purpose of the Study:

To develop an intelligent, comfortable, and long-term gait monitoring system.
To integrate flexible triboelectric nanogenerators with AI for gait analysis.

Main Methods:

A flexible PDMS/liquid metal sponge triboelectric nanogenerator (PLMFT) array was developed.
An insole-type monitoring system with five PLMFT sensing units was constructed.
Convolutional neural networks (CNNs) were employed to analyze gait signals.

Main Results:

The PLMFT device demonstrated excellent flexibility, high electrical output, and stability over 3000 cycles.
The insole system offered breathability and wearable comfort.
CNN analysis achieved a gait recognition accuracy of 98.95%.

Conclusions:

The proposed system offers a high-precision, lightweight solution for wearable health monitoring.
This technology has significant potential for gait abnormality detection, motor function assessment, and disease prediction.