A Low-Cost, IMU-Based Real-Time on Device Gesture Recognition Glove

Oleg Makaussov, Mikhail Krassavin, Maxim Zhabinets, Siamac Fazli

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

Abstract

This paper evaluates the possibility of performing fine gesture recognition including finger movements on a low-tech device. In particular, we present a solution with a recognition model that is small enough to fit in the memory of a low- tech device and describe related difficulties associated with this approach. Several different Machine Learning techniques are employed and their individual advantages and drawbacks are explored for the task at hand. Our results indicate an average of 95% accuracy during real-time testing for an eight class decoding task with a custom Recurrent Neural Network approach, that runs on the low-tech device, namely an Arduino Nano 33 BLE. The novelty and strength of this research lies in the fact that we are able to recognize fine hand gestures including finger movements rather than recognizing only coarse hand gestures. The recognition process is conducted on the low-tech device and as a result this solution has all advantages that are typically associated with embedded systems, namely cost-efficiency, battery life efficiency, and a high degree of independence from other devices as well as compatibility with them.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3346-3351
Number of pages6
Volume2020-October
ISBN (Electronic)9781728185262
DOIs
Publication statusPublished - Oct 11 2020
Event2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020 - Toronto, Canada
Duration: Oct 11 2020Oct 14 2020

Conference

Conference2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
CountryCanada
CityToronto
Period10/11/2010/14/20

Keywords

  • CNN
  • Gesture Recognition
  • IMUs
  • Machine Learning
  • RNN
  • Time Series
  • Wearables

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

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