Abstract
Wearable sensing technologies have enabled continuous, non-invasive monitoring of physiological signals, accelerating advances in neuromuscular assessment, rehabilitation, and human–machine interaction. Among these technologies, surface electromyography (sEMG) has become a cornerstone for decoding muscle activity. However, the reliability of sEMG remains highly dependent on user–device interaction factors—including skin–electrode impedance, contact pressure, and skin temperature—that introduce signal variability, degrade long-term stability, and limit the generalizability of machine learning models. Despite their importance, these interaction variables are rarely acquired synchronously with sEMG in existing wearable systems, limiting systematic investigation of their influence on signal quality. Here, we present MyoProBand, a compact multimodal forearm wearable that records multichannel sEMG along with skin–electrode impedance, contact pressure, skin temperature, and inertial measurements. By combining neuromuscular, physiological, and mechanical sensing within a synchronized acquisition framework, the platform enables the characterization of the factors governing sEMG reliability during prolonged use. The MyoProBand incorporates custom analog front-end electronics, multimodal sensor conditioning, and embedded signal synchronization while maintaining a lightweight form factor suitable for experimental and translational research. We evaluated the system in healthy participants to assess hardware performance, multimodal signal synchronization, and operational stability during forearm muscle activity. The results show that the sensing modalities can be recorded within the same experimental session while preserving the primary sEMG acquisition and provide initial observations of how interface variables change during use. MyoProBand is intended as a research platform for collecting multimodal datasets that can support later studies of sEMG variability and robust myoelectric control.