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Differentiating hand gestures from forearm muscle activity using machine learning.

This study explored the use of forearm electromyography data to distinguish eight hand gestures. The neural network (NN) and random forest (RF) algorithms were tested on data from 10 participants. As window sizes increase from 200 ms to 1000 ms, the algorithm accuracies increased with RF from 85% to...

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Published in:International Journal of Occupational Safety & Ergonomics Vol. 31; no. 1; pp. 1 - 12
Main Authors: Cho, Ryan, Puli, Sunil, Hwang, Jaejin
Format: equations & formulas pictorial research tables/charts tracings Journal Article
Published: Taylor & Francis Ltd Mar2025
Online Access:View this record in EBSCOhost
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        10803548
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      jtl: International Journal of Occupational Safety & Ergonomics
      issn: 10803548
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      dt: Mar2025
      vid: 31
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      pub: Taylor & Francis Ltd
      place: Philadelphia, Pennsylvania
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        10.1080/10803548.2024.2383021
        183540576
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        atl: Differentiating hand gestures from forearm muscle activity using machine learning.
      aug:
        au:
          Cho, Ryan
          Puli, Sunil
          Hwang, Jaejin
        affil: Illinois Mathematics and Science Academy, USA
      sug:
        subj:
          Machine Learning Algorithms Utilization
          Electromyography
          Wearable Sensors
          Forearm
          Muscle, Skeletal
          Body Language Evaluation
          Movement Evaluation
          Signal Processing, Computer Assisted
          Human
          Male
          Female
          Adult
          Neural Networks (Computer)
          Random Forest
          Comparative Studies
          Adult: 19-44 years
          Male
          Female
      ab: This study explored the use of forearm electromyography data to distinguish eight hand gestures. The neural network (NN) and random forest (RF) algorithms were tested on data from 10 participants. As window sizes increase from 200 ms to 1000 ms, the algorithm accuracies increased with RF from 85% to 97% due to the increased temporal resolution. It was also noticed that the RF performed better with an accuracy of 85% than the NN with accuracy 80% when the temporal resolution was smaller, indicating the RF will be efficient when quick-response time is important. As the window size increases, the NN showed higher performance, suggesting that NN will be useful when higher accuracy is required. Future studies should increase the sample size, include more hand gestures, use different feature extraction methods and test different algorithms to improve the accuracy and efficiency of the system.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        pictorial
        research
        tables/charts
        tracings
        Journal Article
      ougenre: Article
    language: English
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