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Related Experiment Video

Updated: Jul 8, 2026

A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program

A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program

Published on: April 19, 2019

An Intelligent Pressurized Thigh Band for Muscular Assistance and Multi-Mode Activity Recognition.

Wenda Wang¹, Wenbin Jiang¹, Yang Yu¹

¹State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China.

Sensors (Basel, Switzerland)

|March 14, 2026

Summary

This intelligent thigh band assists quadriceps and reduces knee load. It accurately recognizes movements using a deep learning model, improving locomotion and showing potential for closed-loop control.

Keywords:

motion recognition sEMG soft robot wearable assistive device

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Area of Science:

Biomedical Engineering
Wearable Technology
Rehabilitation Robotics

Background:

Quadriceps muscle assistance and knee joint load reduction are critical in rehabilitation and performance enhancement.
Existing assistive devices often lack integrated sensing and actuation for precise movement analysis and adaptive support.
High-precision recognition of movement modes is essential for personalized and effective assistive strategies.

Purpose of the Study:

To develop a sensing-actuation integrated intelligent pressurized thigh band for quadriceps assistance and knee load alleviation.
To achieve high-precision recognition of various movement modes using pneumatic data.
To evaluate the impact of air bladder pressure on metabolic cost and muscle activity.

Main Methods:

Development of a portable integrated controller and a textile-integrated flexible pneumatic actuator system.
Experimental evaluation of different air bladder pressures on metabolic rate and quadriceps muscle activity.
Collection of pneumatic data for six common activities and development of a lightweight deep learning model for motion classification.
Deployment of the deep learning model to an embedded platform for real-time application demonstration.

Main Results:

Appropriate air bladder pressure significantly reduced quadriceps muscle activation and average metabolic cost.
The deep learning model achieved 99.17% accuracy in classifying six common activities.
Successful deployment of the motion classification model to an embedded platform validated its practical application potential.

Conclusions:

The intelligent pressurized thigh band effectively improves locomotor performance under static pressure conditions.
Air bladder pressure variations can serve as a proxy for movement intent, enabling future closed-loop control systems.
This technology holds promise for advanced rehabilitation and human performance augmentation.