This live demonstration showcases our custom surface electromyography (sEMG) armband, enabling 16-channel monopolar acquisition. It features the RISC-V-based GAPWatch platform, which integrates two ADS1298 ADCs, an ESP32 radio module, GAP9 (a programmable multi-core RISC-V processor), and an STM32U5 microcontroller acting as a system gateway.
The armband is used to control a cursor in a 2D reach-and-hold task through EMG gestures. The system runs a context-informed incremental learning pipeline directly on GAP9. EMG signals are acquired, filtered, and fed to a tiny CNN, which predicts one of four gestures mapped to cursor directions (e.g., index finger contraction for LEFT, middle finger contraction for UP, etc.). Predictions are transmitted via BLE to a computer running the GUI with the task. The GUI updates the cursor position and derives a pseudolabel from the task context. If the predicted movement brings the cursor closer to the target, the pseudolabel acts as a reward signal; otherwise, it provides corrective feedback. This pseudolabel is returned to the device, where the CNN is updated via stochastic gradient descent (SGD). A replay mechanism is also implemented to stabilize training. EMG processing, inference, and SGD are all executed on GAP9.
During the demo, a participant will perform the task starting from an untrained model. As the task progresses, attendees can observe real-time on-device adaptation. The demonstration highlights how parallel RISC-V processing enables fully embedded, adaptive HMIs without reliance on the cloud or external PCs for recalibration.