Smartphone Control for People with Tetraplegia by Decoding Wearable Electromyography with an On-Device Convolutional Neural Network

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

People with high-level cervical spinal cord injury can have significant impairments in their ability to control their environment, including challenges operating a smartphone or navigating a power wheelchair. Smartphones are often controlled using a mouth stick and mobility is controlled using either a head array or sip-and-puff control system. A wearable system that allows continuous, multi-dimensional control of both smartphones and mobility based on the intuitive movement of cervically-innervated muscles with intact volitional activation could provide an improvement in quality of life for this population. Here I present a number of steps towards this including 1) a Bluetooth connected 8-channel, wearable electromyography sensor, 2) a neural network running on a smartphone that allows continuous two-dimensional control, and 3) rapid training of the neural network by calibrating to self-selected movements. This system was validated on two participants with a cervical spinal cord injury.

Original languageEnglish (US)
Title of host publication2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020
PublisherIEEE Computer Society
Pages1140-1145
Number of pages6
ISBN (Electronic)9781728159072
DOIs
StatePublished - Nov 2020
Event8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020 - New York City, United States
Duration: Nov 29 2020Dec 1 2020

Publication series

NameProceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics
Volume2020-November
ISSN (Print)2155-1774

Conference

Conference8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics, BioRob 2020
CountryUnited States
CityNew York City
Period11/29/2012/1/20

ASJC Scopus subject areas

  • Artificial Intelligence
  • Biomedical Engineering
  • Mechanical Engineering

Fingerprint Dive into the research topics of 'Smartphone Control for People with Tetraplegia by Decoding Wearable Electromyography with an On-Device Convolutional Neural Network'. Together they form a unique fingerprint.

Cite this