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 (>85%) 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.

Benatti, S., Rovere, G., Bosser, J., Montagna, F., Farella, E., Glaser, F., et al. (2017). A sub-10mW real-Time implementation for EMG hand gesture recognition based on a multi-core biomedical SoC. Institute of Electrical and Electronics Engineers Inc. [10.1109/IWASI.2017.7974234].

A sub-10mW real-Time implementation for EMG hand gesture recognition based on a multi-core biomedical SoC

Benatti, Simone;Montagna, Fabio;Farella, Elisabetta;Benini, Luca
2017

Abstract

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 (>85%) 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.
2017
Proceedings of the 2017 7th IEEE International Workshop on Advances in Sensors and Interfaces (IWASI)
139
144
Benatti, S., Rovere, G., Bosser, J., Montagna, F., Farella, E., Glaser, F., et al. (2017). A sub-10mW real-Time implementation for EMG hand gesture recognition based on a multi-core biomedical SoC. Institute of Electrical and Electronics Engineers Inc. [10.1109/IWASI.2017.7974234].
Benatti, Simone; Rovere, Giovanni; Bosser, Jonathan; Montagna, Fabio; Farella, Elisabetta; Glaser, Florian; Schonle, Philipp; Burger, Thomas; Fateh, S...espandi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/609280
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