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

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.

[1]  Elad Alon,et al.  Wireless Recording in the Peripheral Nervous System with Ultrasonic Neural Dust , 2016, Neuron.

[2]  Felice T. Sun,et al.  Closed-loop Neurostimulation: The Clinical Experience , 2014, Neurotherapeutics.

[3]  Qin,et al.  A Brain–Spinal Interface Alleviating Gait Deficits after Spinal Cord Injury in Primates , 2017 .

[4]  Luca Benini,et al.  EMG-based hand gesture recognition with flexible analog front end , 2014, 2014 IEEE Biomedical Circuits and Systems Conference (BioCAS) Proceedings.

[5]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[6]  Luca Benini,et al.  Scalable EEG seizure detection on an ultra low power multi-core architecture , 2016, 2016 IEEE Biomedical Circuits and Systems Conference (BioCAS).

[7]  Thomas Burger,et al.  An output-capacitor-free adaptively biased LDO regulator with robust frequency compensation in 0.13μm CMOS for SoC application , 2016, ISCAS 2016.

[8]  J. Daube,et al.  Muscles Alive , 1981, Neurology.

[9]  John J. Foxe,et al.  Neurophysiological Indices of Atypical Auditory Processing and Multisensory Integration are Associated with Symptom Severity in Autism , 2014, Journal of Autism and Developmental Disorders.

[10]  L J Hargrove,et al.  Determining the Optimal Window Length for Pattern Recognition-Based Myoelectric Control: Balancing the Competing Effects of Classification Error and Controller Delay , 2011, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[11]  David Atienza,et al.  TamaRISC-CS: An ultra-low-power application-specific processor for compressed sensing , 2012, 2012 IEEE/IFIP 20th International Conference on VLSI and System-on-Chip (VLSI-SoC).

[12]  Jan M. Rabaey,et al.  Powering and communication for OMNI: A distributed and modular closed-loop neuromodulation device , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[13]  V. Parsonnet,et al.  Implantable Cardiac Pacemakers Status Report and Resource Guideline , 1974 .

[14]  Luca Benini,et al.  A Versatile Embedded Platform for EMG Acquisition and Gesture Recognition , 2015, IEEE Transactions on Biomedical Circuits and Systems.

[15]  N P Smyth,et al.  Report of the Inter-Society Commission for Heart Disease Resources. Implantable cardiac pacemakers: status report and resource guideline. , 1974, The American journal of cardiology.

[16]  Luca Benini,et al.  An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics , 2016, IEEE Transactions on Circuits and Systems I: Regular Papers.

[17]  Luca Benini,et al.  A Prosthetic Hand Body Area Controller Based on Efficient Pattern Recognition Control Strategies , 2017, Sensors.

[18]  Weidong Zhou,et al.  Automatic Seizure Detection Using Wavelet Transform and SVM in Long-Term Intracranial EEG , 2012, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[19]  Luca Benini,et al.  Power Line Interference Removal for High-Quality Continuous Biosignal Monitoring With Low-Power Wearable Devices , 2016, IEEE Sensors Journal.

[20]  Luca Benini,et al.  Tailoring instruction-set extensions for an ultra-low power tightly-coupled cluster of OpenRISC cores , 2015, 2015 IFIP/IEEE International Conference on Very Large Scale Integration (VLSI-SoC).

[21]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[22]  Luca Benini,et al.  A multi-sensor and parallel processing SoC for wearable and implantable telemetry systems , 2017, ESSCIRC 2017 - 43rd IEEE European Solid State Circuits Conference.

[23]  L. Benini,et al.  A wearable EEG-based drowsiness detection system with blink duration and alpha waves analysis , 2017, 2017 8th International IEEE/EMBS Conference on Neural Engineering (NER).

[24]  Luca Benini,et al.  A Reconfigurable 5-to-14 bit SAR ADC for Battery-Powered Medical Instrumentation , 2015, IEEE Transactions on Circuits and Systems I: Regular Papers.