3D Printed Brain-Controlled Robot-Arm Prosthetic via Embedded Deep Learning From sEMG Sensors

Lonsdale, David, Zhang, Li and Jiang, Richard (2020) 3D Printed Brain-Controlled Robot-Arm Prosthetic via Embedded Deep Learning From sEMG Sensors. In: Proceedings of 2020 International Conference on Machine Learning and Cybernetics, ICMLC 2020. IEEE, Piscataway, NJ, pp. 247-253. ISBN 9780738124261

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Official URL: https://doi.org/10.1109/ICMLC51923.2020.9469532

Abstract

In this paper, we present our work on developing robot arm prosthetic via deep learning. Our work proposes to use transfer learning techniques applied to the Google Inception model to retrain the final layer for surface electromyography (sEMG) classification. Data have been collected using the Thalmic Labs Myo Armband and used to generate graph images comprised of 8 subplots per image containing sEMG data captured from 40 data points per sensor, corresponding to the array of 8 sEMG sensors in the armband. Data captured were then classified into four categories (Fist, Thumbs Up, Open Hand, Rest) via using a deep learning model, Inception-v3, with transfer learning to train the model for accurate prediction of each on real-time input of new data. This trained model was then downloaded to the ARM processor based embedding system to enable the brain-controlled robot-arm prosthetic manufactured from our 3D printer. Testing of the functionality of the method, a robotic arm was produced using a 3D printer and off-the-shelf hardware to control it. SSH communication protocols are employed to execute python files hosted on an embedded Raspberry Pi with ARM processors to trigger movement on the robot arm of the predicted gesture.

Item Type: Book Section
Additional Information: 19th International Conference on Machine Learning and Cybernetics, ICMLC 2020 4/12/20 → … Virtual, Online
Uncontrolled Keywords: Brain Interface, Deep Learning on ARM, Prosthetics
Subjects: G400 Computer Science
G700 Artificial Intelligence
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Rachel Branson
Date Deposited: 03 May 2022 15:01
Last Modified: 03 May 2022 15:15
URI: http://nrl.northumbria.ac.uk/id/eprint/49021

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