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In this paper, we present our work on developing robot arm prosthetic viadeep learning. Our work proposes to use transfer learning techniques applied tothe Google Inception model to retrain the final layer for surfaceelectromyography (sEMG) classification. Data have been collected using theThalmic Labs Myo Armband and used to generate graph images comprised of 8subplots per image containing sEMG data captured from 40 data points persensor, corresponding to the array of 8 sEMG sensors in the armband. Datacaptured were then classified into four categories (Fist, Thumbs Up, Open Hand,Rest) via using a deep learning model, Inception-v3, with transfer learning totrain 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 embeddingsystem to enable the brain-controlled robot-arm prosthetic manufactured fromour 3D printer. Testing of the functionality of the method, a robotic arm wasproduced using a 3D printer and off-the-shelf hardware to control it. SSHcommunication protocols are employed to execute python files hosted on anembedded Raspberry Pi with ARM processors to trigger movement on the robot armof the predicted gesture.

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