An adaptive convolutional neural network framework for multi-user myoelectric interfaces

Keun Tae Kim, Ki Hee Park, Seong Whan Lee

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

1 Citation (Scopus)

Abstract

Recently, the electromyogram (EMG)-based userinterfaces have developed for control of wearable rehabilitation robots such as arm prosthetics. In these interfaces, decoding of the user's movement intention is significant for controlling the robots properly. However, the high inter-user variations in EMG signals have disturbed to a stable decoding performance with multi-user. In this context, we developed an user-independent decoding method using the convolutional neural networks (CNN) for multi-user myoelectric interfaces. Specifically, we devise an user-Adaptive framework based on the CNN for decoding of movement intentions using raw EMG signals. The Ninapro database was used to our experiments, and the experimental results show that our methods successfully decoded hand movement intentions. The effectiveness of the proposed method was also confirmed by experiment to decode movement intentions with across different subjects.

Original languageEnglish
Title of host publicationProceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages793-798
Number of pages6
ISBN (Electronic)9781538633540
DOIs
Publication statusPublished - 2018 Dec 13
Event4th Asian Conference on Pattern Recognition, ACPR 2017 - Nanjing, China
Duration: 2017 Nov 262017 Nov 29

Other

Other4th Asian Conference on Pattern Recognition, ACPR 2017
CountryChina
CityNanjing
Period17/11/2617/11/29

Keywords

  • Convolutional Neural Networks
  • Electromyogram (EMG)
  • Myoelectric Interfaces

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Signal Processing

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