Locomo-Net: A Low -Complex Deep Learning Framework for sEMG-Based Hand Movement Recognition for Prosthetic Control

Gautam, Arvind and Panwar, Madhuri and Wankhede, Archana and Arjunan, Sridhar P. and Naik, Ganesh R. and Acharyya, Amit and Kumar, Dinesh K. (2020) Locomo-Net: A Low -Complex Deep Learning Framework for sEMG-Based Hand Movement Recognition for Prosthetic Control. IEEE Journal of Translational Engineering in Health and Medicine, 8. pp. 1-12. ISSN 2168-2372

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Background: The enhancement in the performance of the myoelectric pattern recognition techniques based on deep learning algorithm possess computationally expensive and exhibit extensive memory behavior. Therefore, in this paper we report a deep learning framework named ‘Low-Complex Movement recognition-Net’ (LoCoMo-Net) built with convolution neural network (CNN) for recognition of wrist and finger flexion movements; grasping and functional movements; and force pattern from single channel surface electromyography (sEMG) recording. The network consists of a two-stage pipeline: 1) input data compression; 2) data-driven weight sharing. Methods: The proposed framework was validated on two different datasets- our own dataset (DS1) and publicly available NinaPro dataset (DS2) for 16 movements and 50 movements respectively. Further, we have prototyped the proposed LoCoMo-Net on Virtex-7 Xilinx field-programmable gate array (FPGA) platform and validated for 15 movements from DS1 to demonstrate its feasibility for real-time execution. Results: The effectiveness of the proposed LoCoMo-Net was verified by a comparative analysis against the benchmarked models using the same datasets wherein our proposed model outperformed Twin- Support Vector Machine (SVM) and existing CNN based model by an average classification accuracy of 8.5 % and 16.0 % respectively. In addition, hardware complexity analysis is done to reveal the advantages of the two-stage pipeline where approximately 27 %, 49 %, 50 %, 23 %, and 43 % savings achieved in lookup tables (LUT’s), registers, memory, power consumption and computational time respectively. Conclusion: The clinical significance of such sEMG based accurate and low-complex movement recognition system can be favorable for the potential improvement in quality of life of an amputated persons. CCBY

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IITH Creators:
IITH CreatorsORCiD
Panwar, MadhuriUNSPECIFIED
Wankhede, ArchanaUNSPECIFIED
Acharyya, Amithttp://orcid.org/0000-0002-5636-0676
Item Type: Article
Uncontrolled Keywords: Classification (of information); Complex networks; Deep learning; Field programmable gate arrays (FPGA); Learning algorithms; Palmprint recognition; Pipelines; Support vector machines; Table lookup
Subjects: Electrical Engineering
Divisions: Department of Electrical Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 23 Jun 2021 05:23
Last Modified: 23 Jun 2021 05:23
URI: http://raiith.iith.ac.in/id/eprint/7980
Publisher URL: http://doi.org/10.1109/JTEHM.2020.3023898
OA policy: https://v2.sherpa.ac.uk/id/publication/24777
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