EMG and Joint Angle-Based Machine Learning to Predict Future Joint Angles at the Knee

Details

• Personal Author:
  Chen H ; Coker J ; Gallagher S ; Schall MC Jr. ; Zabala M
• Description:
  Electromyography (EMG) is commonly used to measure electrical activity of the skeletal muscles. As exoskeleton technology advances, these signals may be used to predict human intent for control purposes. This study used an artificial neural network trained and tested with knee flexion angles and knee muscle EMG signals to predict knee flexion angles during gait at 50, 100, 150, and 200 ms into the future. The hypothesis of this study was that the algorithm's prediction accuracy would only be affected by time into the future, not subject, gender or side, and that as time into the future increased, the prediction accuracy would decrease. A secondary hypothesis was that as the number of algorithm training trials increased, the prediction accuracy of the artificial neural network (ANN) would increase. The results of this study indicate that only time into the future affected the accuracy of knee flexion angle prediction (p < 0.001), whereby greater time resulted in reduced accuracy (0.68 to 4.62 degrees root mean square error (RMSE) from 50 to 200 ms). Additionally, increased number of training trials resulted in increased angle prediction accuracy. [Description provided by NIOSH]
• Subjects:
  Biomechanical Phenomena Occupational Health Safety
• Keywords:
  Artificial Intelligence Author Keywords: EMG Biomechanical Modeling Electromyography Exoskeletons Joint Angle Machine Learning Neural Networks Prediction Sensors Wearable Technologies

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• ISSN:
  1424-8220
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  Alabama ; OSHA Region 4
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  21
• Issue:
  11
• NIOSHTIC Number:
  nn:20067268
• Citation:
  Sensors 2021 Jun; 21(11):3622
• Contact Point Address:
  Michael Zabala, Department of Mechanical Engineering, Auburn University, Auburn, AL 36849, USA
• Email:
  zabalme@auburn.edu
• Federal Fiscal Year:
  2021
• Performing Organization:
  University of Alabama at Birmingham
• Peer Reviewed:
  True
• Start Date:
  20050701
• Source Full Name:
  Sensors
• End Date:
  20270630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:e7df547baffc951a2470c02595ec87a6d0e4d6d94043636d1a57b1ce77121345697a8ece35521b48b8e7f52d239e42fd6c185fe49f5ce2d0f2a44a9cc6bdaace
• Download URL:
  https://stacks.cdc.gov/view/cdc/231381/cdc_231381_DS1.pdf
• File Type:
  [PDF - 3.03 MB ]