Open-source RISC-V platforms for embedded medical grade EMG processing: Are we there yet?

Open hardware, which includes both silicon and embedded hardware platforms, has fueled progress in multiple sectors. The RISC-V instruction set architecture is at the leading edge of this transformation thanks to its flexibility and vast adoption. The efficiency of RISC-V-based processors and accelerators enables artificial intelligence-based biomedical applications to operate in real-time, safeguarding user privacy while ensuring long battery life. These capabilities are critical in human–machine interaction (HMI) systems to bridge the gap between complex high-end systems and low-power, low-cost wearable hardware to provide real-time and long-term consumer interfaces with medical-grade performance. To shed more light on this topic, we examine different processing platforms in the context of electromyography (EMG)-based gesture recognition. Based on experimental EMG data, we also extend analysis and profiling to sensor interfaces, feature extraction methods, and classification algorithms to optimize accuracy, energy efficiency, and cost in various application scenarios, thereby providing a resource for researchers and practitioners interested in open-source design at the system level, enabling them to select the most appropriate solution that achieves an optimal balance between performance and complexity. Our results indicate that for applications such as gaming and consumer object control, where cost and time-to-market outweigh precision and power constraints, low-complexity algorithms (e.g., linear discriminant analysis) implemented on low-cost microcontroller-based platforms offer sufficient classification accuracy. On the other hand, medical-grade performance for continuous monitoring in wearable devices requires the next generation of ultra-low-power RISC-V architectures, capable of executing high-performance and computationally demanding algorithms. These results highlight the potential of open hardware in biomedical applications, not only for the performance of current RISC-V architectures but also for their flexibility, which allows designers to modify current integrated circuits and system designs for specific applications (without incurring licensing costs), further reducing the gap with high-end systems. • Novel benchmarking framework for EMG HMIs, including open-source RISC-V hardware. • Low-cost microcontrollers deliver enough EMG performance for consumer applications. • Medical-grade EMG needs ultra-low-power RISC-V to run demanding algorithms. • Our findings provide a structured framework for optimizing EMG-based wearables.