Novel Muscle Monitoring by Radiomyography(RMG) and Application to Hand Gesture Recognition

TL;DR

This paper introduces radiomyography (RMG), a wearable, contactless muscle sensing technology that accurately recognizes hand gestures and monitors muscle activity, offering potential for advanced human-machine interfaces and medical applications.

Contribution

The paper presents a novel RMG method for continuous, wearable muscle monitoring that captures both superficial and deep muscles, with high accuracy in gesture recognition and adaptability across users.

Findings

Recognizes 23 hand gestures with 99% accuracy.

Achieves 97% accuracy in user and sensor variation transfer learning.

Successfully monitors eye and leg muscles with high precision.

Abstract

Conventional electromyography (EMG) measures the continuous neural activity during muscle contraction, but lacks explicit quantification of the actual contraction. Mechanomyography (MMG) and accelerometers only measure body surface motion, while ultrasound, CT-scan and MRI are restricted to in-clinic snapshots. Here we propose a novel radiomyography (RMG) for continuous muscle actuation sensing that can be wearable and touchless, capturing both superficial and deep muscle groups. We verified RMG experimentally by a forearm wearable sensor for detailed hand gesture recognition. We first converted the radio sensing outputs to the time-frequency spectrogram, and then employed the vision transformer (ViT) deep learning network as the classification model, which can recognize 23 gestures with an average accuracy up to 99% on 8 subjects. By transfer learning, high adaptivity to user difference and sensor variation were achieved at an average accuracy up to 97%. We further demonstrated RMG to monitor eye and leg muscles and achieved high accuracy for eye movement and body postures tracking. RMG can be used with synchronous EMG to derive stimulation-actuation waveforms for many future applications in kinesiology, physiotherapy, rehabilitation, and human-machine interface.