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A Real-Time Wearable Electromyography Measurement System for Small Animals

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Published on: November 15, 2024

Artificial-Intelligence-Driven Electromyography Adaptation for Elderly Assistance at Physiological, Functional, and

Jiaqi Xue¹, Ziqi Li¹, Xiaoyang Zou¹

¹Department of Biomedical Engineering, City University of Hong Kong, Hong Kong 999077, China.

Cyborg and Bionic Systems (Washington, D.C.)

|July 16, 2026

Summary

This study introduces an intelligent framework using electromyography (EMG) to enhance upper-limb robotic assistance for older adults. The system improves daily living activities through accurate muscle signal interpretation and adaptive coordination.

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Area of Science:

Biomedical Engineering
Rehabilitation Robotics
Human-Robot Interaction

Background:

Older adults experience functional decline impacting Activities of Daily Living (ADLs).
Myoelectric control (electromyography - EMG) offers intuitive human-robot interfaces but faces challenges in signal processing and coordination.
Existing systems struggle with robust signal annotation, multijoint control, and task generalization.

Purpose of the Study:

To develop and validate a 3-level intelligent framework for EMG-based multijoint upper-limb assistance tailored for older adults.
To improve the accuracy and robustness of myoelectric control for assistive robotic systems.
To enhance the autonomy and quality of life for older adults through adaptive robotic support.

Main Methods:

A 3-level framework integrating physiological signal processing, functional intent decoding, and behavioral adaptation.
Situation-aware labeling protocols for improved EMG signal robustness.
A deep backbone model for inferring single-joint and multijoint movements with high accuracy.
Model distillation for adapting to complex ADL tasks and continuous learning without catastrophic forgetting.

Main Results:

The deep backbone model achieved 95.34% accuracy in inferring joint movements.
The framework was successfully implemented in real-time on an EMG-controlled robotic system.
The system provided smooth and coordinated assistance during daily activities.

Conclusions:

The proposed framework offers a systematic solution for EMG-based multijoint coordination in assistive robotics.
It bridges the gap from physiological signal processing to adaptive behavioral control.
This work provides a foundation for practical, adaptive assistive systems, promoting independence for older adults and supporting healthy aging.