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Neural computing for walking gait pattern identification based on multi-sensor data fusion of lower limb muscles

Neural Computing and Applications volume 28, pages 65–77 (2017)Cite this article

Abstract

The use of neural computing for gait analysis widely known as computational intelligent gait analysis is addressed recently. This research work reports multilayer feed-forward neural networks for walking gait pattern identification using multi-sensor data fusion; electromyography (EMG) signals and soft tissue deformation analysis using successive frames of video sequence extracted from lower limb muscles according to each gait phase within the considered gait cycle. Neural computing framework for walking gait pattern identification consists of system hardware and intelligent system software. System hardware comprises a wireless surface EMG sensor unit and two video cameras for measuring the neuromuscular activity of lower limb muscles, and a custom-developed artificial neural network for classifying the gait patterns of subjects during walking. The system uses root mean square and soft tissue deformation parameter as the input features. Multilayer feed-forward back propagation neural networks (FFBPNNs) with different network training functions were designed, and their classification results were compared. The intelligent gait analysis system validation has been carried out for a group of healthy and injured subjects. The results demonstrated that the overall accuracy of 98 % prediction is achieved for gait patterns classification established by multi-sensor data fusion of lower limb muscles using FFBPNN with Levenberg–Marquardt training function resulting better performance over FFBPNN with other training functions.

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Acknowledgments

The authors appreciate the suggestions provided by Owais A. Malik as well as his assistance in electromyography experiment of this work.

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Authors and Affiliations

  1. Motion Analysis Lab, Integrated Science Building, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong, BE1410, Brunei Darussalam

    Joko Triloka, S. M. N. Arosha Senanayake & Daphne Lai

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  1. Joko Triloka
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  2. S. M. N. Arosha Senanayake
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  3. Daphne Lai
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Correspondence to S. M. N. Arosha Senanayake.

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Triloka, J., Senanayake, S.M.N.A. & Lai, D. Neural computing for walking gait pattern identification based on multi-sensor data fusion of lower limb muscles. Neural Comput & Applic 28, 65–77 (2017). https://doi.org/10.1007/s00521-016-2312-x

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  • DOI: https://doi.org/10.1007/s00521-016-2312-x

Keywords

  • Electromyography
  • Soft tissue deformation
  • Gait patterns
  • Multi-sensor data fusion