부산대학교 도서관

Real-time biosignal classification in power-constrained embedded applications is a key step in designing portable e-healtb devices requiring hardware integration along with concurrent signal processing. This paper presents an application based on a novel biomedical System-On-Chip (SoC) for signal acquisition and processing combining a homogeneous multi-core cluster with a versatile bio-potential front-end. The presented implementation acquires raw EMG signals from 3 passive gel-electrodes and classifies 3 hand gestures using a Support Vector Machine (SVM) pattern recognition algorithm. Performance matches state-of-the-art high-end systems both in terms of recognition accuracy (>S5%) and of real-time execution (gesture recognition time 300 ms). The power consumption of the employed biomedical SoC is below 10 mW, outperforming implementations on conunercial MCUs by a factor of 10, ensuring a battery life of up to 160 hours with a common Li-ion 1600 mAh battery.