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Continuous estimation of finger joint angles using muscle activation inputs from surface EMG signals.

Authors
  • Ngeo, Jimson
  • Tamei, Tomoya
  • Shibata, Tomohiro
Type
Published Article
Journal
Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Publication Date
Jan 01, 2012
Volume
2012
Pages
2756–2759
Identifiers
DOI: 10.1109/EMBC.2012.6346535
PMID: 23366496
Source
Medline
License
Unknown

Abstract

Prediction of dynamic hand finger movements has many clinical and engineering applications in the control of human interface devices such as those used in virtual reality control, robot prosthesis and rehabilitation aids. Surface electromyography (sEMG) signals have often been used in the mentioned applications because these reflect the motor intention of users very well. In this study, we present a method to estimate the finger joint angles of a hand from sEMG signals that considers electromechanical delay (EMD), which is inherent when EMG signals are captured alongside motion data. We use the muscle activation obtained from the sEMG signals as input to a neural network. In this muscle activation model, the EMD is parameterized and automatically obtained through optimization. With this method, we can predict the finger joint angles with sEMG signals in both periodic and nonperiodic free movements of the flexion and extension movement of the fingers. Our results show correlation as high as 0.92 between the actual and predicted metacarpophalangeal (MCP) joint angles for periodic finger flexion movements, and as high as 0.85 for nonperiodic movements, which are more dynamic and natural.

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