Temporal Variability Analysis in sEMG Hand Grasp Recognition using Temporal Convolutional Networks
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Date
2020
Publication Type
Conference Paper
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yes
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Abstract
Hand movement recognition via surface electromyographic (sEMG) signal is a promising approach for the advance in Human-Computer Interaction. However, this field has to deal with two main issues: (1) the long-term reliability of sEMG-based control is limited by the variability affecting the sEMG signal (especially, variability over time); (2) the classification algorithms need to be suitable for implementation on embedded devices, which have strict constraints in terms of power budget and computational resources. Current solutions present a performance over-time drop that makes them unsuitable for reliable gesture controller design. In this paper, we address temporal variability of sEMG-based grasp recognition, proposing a new approach based on Temporal Convolutional Networks, a class of deep learning algorithms particularly suited for time series analysis and temporal pattern recognition. Our approach improves by 7.6% the best results achieved in the literature on the NinaPro DB6, a reference dataset for temporal variability analysis of sEMG. Moreover, when targeting the much more challenging inter-session accuracy objective, our method achieves an accuracy drop of just 4.8% between intra- and inter-session validation. This proves the suitability of our setup for a robust, reliable long-term implementation. Furthermore, we distill the network using deep network quantization and pruning techniques, demonstrating that our approach can use down to 120 lower memory footprint than the initial network and 4 lower memory footprint than a baseline Support Vector Machine, with an inter-session accuracy degradation of only 2.5%, proving that the solution is suitable for embedded resource-constrained implementations.
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published
Editor
Book title
2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)
Journal / series
Volume
Pages / Article No.
228 - 232
Publisher
IEEE
Event
2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS 2020) (virtual)
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Methods
Software
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Organisational unit
03996 - Benini, Luca / Benini, Luca
Notes
Conference postponed due to Corona virus (COVID-19). Due to the Corona virus (COVID-19) the conference was conducted virtually.
Funding
732631 - Open Transprecision Computing (EC)
780788 - software framework for runtime-Adaptive and secure deep Learning On Hetergeneous Architectures (EC)
780788 - software framework for runtime-Adaptive and secure deep Learning On Hetergeneous Architectures (EC)