A Wearable Echomyography System Based on a Single Transducer

Wearable electromyography devices can be used to detect muscular activity for health monitoring and body motion tracking, but this approach is limited by weak and stochastic signals with a low spatial resolution. Alternatively, echomyography can detect muscle movement using ultrasound waves but typically relies on complex transducer arrays, which are bulky, have high power consumption, and can limit user mobility. Here, we report a fully-integrated wearable echomyography system that consists of a customized single transducer, a wireless circuit for data processing, and an onboard battery for power. The system can be attached to the skin and provides accurate long-term wireless monitoring of muscles. To illustrate its capabilities, we use this patch to detect the activity of the diaphragm, which allows the recognition of different breathing modes. We also develop a deep-learning algorithm to correlate the single-transducer radiofrequency data from forearm muscles with hand gestures to accurately and continuously track 13 hand joints with a mean error of only 7.9°.