Arm Movements Recognition by Implementing CNN on Microcontrollers

Siyu Qin, Jiaqi Zhang, Hongji Shen, Yizhou Wang

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

3 Citations (Scopus)

Abstract

Surface electromyography is a technique mainly used to detect hand movements to help patients regain control over their fingers or manipulate prosthetic arms. This body signal measuring technique is usually used with machine learning to recognize various arm movement s. However, past studies on arm movement recognitions used powerful computers that is inconvenient for patients to carry around to perform real-time sEMG signal measuring. This paper compares the performance of the two commonly used sEMG signal feature extraction methods, 1D-CNN, and 2D-CNN architectures. We first collected sEMG signals from 10 subjects. The 1D-CNN architecture reached an average recognition accuracy of 89.4% and the 2D-CNN architecture reached an average recognition accuracy of 98.9%. The 2D-CNN architecture is converted from TensorFlow file to TensorFlow Lite file and is imported into the Arduino nano 33 BLE sense microcontroller. The microcontroller is able of repeating the machine learning process with a processing time of 79-85ms and 132-135ms respectively for 1D-CNN and 2D-CNN models. In the future, it is suggested that ASIC devices with specially designed electrodes can be applied to further reduce power consumption, size, and processing time of the device to help patients regain control of their hands or to manipulate prosthetic hands to perform dangerous experiments.

Original languageEnglish
Title of host publication2021 9th International Conference on Control, Mechatronics and Automation, ICCMA 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages171-176
Number of pages6
ISBN (Electronic)9781665410731
DOIs
Publication statusPublished - 2021
Externally publishedYes
Event9th International Conference on Control, Mechatronics and Automation, ICCMA 2021 - Luxembourg, Luxembourg
Duration: 11 Nov 202114 Nov 2021

Publication series

Name2021 9th International Conference on Control, Mechatronics and Automation, ICCMA 2021

Conference

Conference9th International Conference on Control, Mechatronics and Automation, ICCMA 2021
Country/TerritoryLuxembourg
CityLuxembourg
Period11/11/2114/11/21

Keywords

  • Convolutional neural network
  • Machine learning
  • sEMG
  • TensorFlow lite

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