Developing a Fuzzy C-Means Inference System for Muscle Strength Prediction Using sEMG

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

Abstract

The research involved the development of a fuzzy inference system (FIS) to predict grip strength through the analysis of sEMG signals. Fuzzy rules were generated using fuzzy c-means (FCM) clustering. In an experiment focused on lifting motions and including nine participants, the FIS demonstrated promising results. Specifically, in the non-weight condition, the FIS achieved a Mean Squared Error (MSE) of 0.1246 and an R-squared value of 0.3357 for grip strength prediction. However, when an 11-lb weight load was introduced, the accuracy of the FIS dropped significantly, leading to less reliable predictions. This was reflected in an increased MSE of 0.1804 and a reduced R-squared value of 0.0379. These outcomes underscore the potential of wearable sEMG devices coupled with a fuzzy inference system for grip strength prediction. The research also highlights the need for further research in this evolving field.

Original languageEnglish
Title of host publicationGCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1028-1032
Number of pages5
ISBN (Electronic)9798350340181
DOIs
Publication statusPublished - 2023
Event12th IEEE Global Conference on Consumer Electronics, GCCE 2023 - Nara, Japan
Duration: 2023 Oct 102023 Oct 13

Publication series

NameGCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics

Conference

Conference12th IEEE Global Conference on Consumer Electronics, GCCE 2023
Country/TerritoryJapan
CityNara
Period23-10-1023-10-13

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality
  • Instrumentation
  • Atomic and Molecular Physics, and Optics

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