  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.
