A Wearable Ultra-Low-Power sEMG-Triggered Ultrasound System for Long-Term Muscle Activity Monitoring

Wearable biosignal processing applications are driving significant progress toward miniaturized, energy-efficient Internet-of-Things solutions for both clinical and consumer applications. However, scaling toward high-density multi-channel front-ends is only feasible by performing data processing and \ac{ML} near-sensor through energy-efficient edge processing. To tackle these challenges, we introduce BioGAP, a novel, compact, modular, and lightweight (6g) medical-grade biosignal acquisition and processing platform powered by GAP9, a ten-core ultra-low-power SoC designed for efficient multi-precision (from FP to aggressively quantized integer) processing, as required for advanced ML and DSP. BioGAP's form factor is 16x21x14~mm$^3$ and comprises two stacked PCBs: a baseboard integrating the GAP9 SoC, a wireless \ac{BLE} capable SoC, a power management circuit, and an accelerometer; and a shield including an \ac{AFE} for ExG acquisition. Finally, the system also includes a flexibly placeable \ac{PPG} PCB with a size of 9x7x3~mm$^3$ and a rechargeable battery ($\phi$ 12x5~mm$^2$). We demonstrate BioGAP on a \ac{SSVEP}-based \ac{BCI} application. We achieve 3.6~$\mu J/sample$ in streaming and 2.2~$\mu J/sample$ in onboard processing mode, thanks to an efficiency on the FFT computation task of 16.7~Mflops/s/mW with wireless bandwidth reduction of 97\%, within a power budget of just 18.2~mW allowing for an operation time of 15~h.