Muscle fatigue is characterized by the progressive decrease in the capacity of a muscle to generate force during sustained or repeated contractions. A well-established indicator of fatigue is the increase in muscle thickness, which can be measured non-invasively using ultrasound (US). In this work, we present the first demonstration of end-to-end, on-device muscle thickness tracking for fatigue monitoring using a truly wearable A-mode US system (WULPUS). We collected data from a subject performing isometric biceps contractions under load. We implemented a lightweight signal processing pipeline to extract muscle thickness from raw A-mode signals, obtaining a thickness increase consistent with fatigue progression patterns presented by prior studies. For the first time, we showcase the feasibility of on-device (nRF52832) muscle thickness extraction, with a computation time of only 4.1ms, opening the possibility for real-time, wearable muscle fatigue tracking. By transmitting only muscle thickness information instead of raw data, our system achieves a significant reduction in power consumption (up to 26%) and 201× lower wireless bandwidth, supporting continuous operation for up to 4 days on 320mAh Li-Po battery.
Spacone, G., Frey, S., Leitner, C., Benini, L., Cossettini, A. (2025). Towards Fully Wearable Muscle Fatigue Assessment with A-mode Ultrasound. IEEE Computer Society [10.1109/ius62464.2025.11201415].
Towards Fully Wearable Muscle Fatigue Assessment with A-mode Ultrasound
Benini, Luca;
2025
Abstract
Muscle fatigue is characterized by the progressive decrease in the capacity of a muscle to generate force during sustained or repeated contractions. A well-established indicator of fatigue is the increase in muscle thickness, which can be measured non-invasively using ultrasound (US). In this work, we present the first demonstration of end-to-end, on-device muscle thickness tracking for fatigue monitoring using a truly wearable A-mode US system (WULPUS). We collected data from a subject performing isometric biceps contractions under load. We implemented a lightweight signal processing pipeline to extract muscle thickness from raw A-mode signals, obtaining a thickness increase consistent with fatigue progression patterns presented by prior studies. For the first time, we showcase the feasibility of on-device (nRF52832) muscle thickness extraction, with a computation time of only 4.1ms, opening the possibility for real-time, wearable muscle fatigue tracking. By transmitting only muscle thickness information instead of raw data, our system achieves a significant reduction in power consumption (up to 26%) and 201× lower wireless bandwidth, supporting continuous operation for up to 4 days on 320mAh Li-Po battery.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.



