Online Learning and Classification of EMG-Based Gestures on a Parallel Ultra-Low Power Platform Using Hyperdimensional Computing

This paper presents a wearable electromyographic gesture recognition system based on the hyperdimensional computing paradigm, running on a programmable parallel ultra-low-power (PULP) platform. The processing chain includes efficient on-chip training, which leads to a fully embedded implementation w...

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Veröffentlicht in:IEEE transactions on biomedical circuits and systems Jg. 13; H. 3; S. 516 - 528
Hauptverfasser: Benatti, Simone, Montagna, Fabio, Kartsch, Victor, Rahimi, Abbas, Rossi, Davide, Benini, Luca
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.06.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Autonomy
Batteries
Classification
Computation
Distance learning
Electromyography
Embedded systems
Energy budget
Energy efficiency
Energy management
Gesture recognition
Gestures
Hidden Markov models
HMI
Humans
hyperdimensional computing
Internet
Learning
Machine learning
Machine learning algorithms
Pattern Recognition, Automated
Personal computers
Power consumption
Power management
Prototyping
Pulp
PULP platform
Signal Processing, Computer-Assisted
Support vector machines
System on chip
Training
Wearable Electronic Devices
Wearable technology
ISSN:1932-4545, 1940-9990, 1940-9990
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Zusammenfassung:This paper presents a wearable electromyographic gesture recognition system based on the hyperdimensional computing paradigm, running on a programmable parallel ultra-low-power (PULP) platform. The processing chain includes efficient on-chip training, which leads to a fully embedded implementation with no need to perform any offline training on a personal computer. The proposed solution has been tested on 10 subjects in a typical gesture recognition scenario achieving 85% average accuracy on 11 gestures recognition, which is aligned with the state-of-the-art, with the unique capability of performing online learning. Furthermore, by virtue of the hardware friendly algorithm and of the efficient PULP system-on-chip (Mr. Wolf) used for prototyping and evaluation, the energy budget required to run the learning part with 11 gestures is 10.04 mJ, and 83.2 <inline-formula><tex-math notation="LaTeX">\mu</tex-math></inline-formula>J per classification. The system works with a average power consumption of 10.4 mW in classification, ensuring around 29 h of autonomy with a 100 mAh battery. Finally, the scalability of the system is explored by increasing the number of channels (up to 256 electrodes), demonstrating the suitability of our approach as universal, energy-efficient biopotential wearable recognition framework.
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ISSN:1932-4545
1940-9990
1940-9990
DOI:10.1109/TBCAS.2019.2914476