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Motion Intent Recognition in Intelligent Lower Limb Prosthesis Using One-Dimensional Dual-Tree Complex Wavelet

Min Sheng1, Wan-Jun Wang1, Ting-Ting Tong1

  • 1School of Mathematics and Physics, Anqing Normal University, Anqing 246133, Anhui, China.

Computational Intelligence and Neuroscience
|December 6, 2021
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Summary

This study introduces the one-dimensional dual-tree complex wavelet transform (1D-DTCWT) for improved lower limb prosthesis motion intent recognition. The novel method enhances accuracy in recognizing gait transitions, crucial for seamless prosthetic limb control.

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Human-Computer Interaction

Background:

  • Motion intent recognition for lower limb prostheses is vital for user mobility.
  • Traditional methods struggle with unstable statistical features during gait transitions.
  • Gait instantaneous conversion between steady states presents a significant challenge.

Purpose of the Study:

  • To introduce a novel method for motion intent recognition in lower limb prostheses.
  • To address the limitations of traditional statistical feature-based approaches.
  • To improve the accuracy and stability of recognizing gait transitional patterns.

Main Methods:

  • Application of the one-dimensional dual-tree complex wavelet transform (1D-DTCWT).
  • Utilizing the local analysis ability of wavelet transform to amplify gait variation characteristics.
  • Leveraging translation invariance and direction selectivity of 1D-DTCWT for continuous feature extraction.

Main Results:

  • High recognition accuracy achieved: 98.91% (steady), 98.92% (transitional), 97.27% (total) for able-bodied subjects.
  • Excellent accuracy for an amputee subject: 100% (steady), 91.16% (transitional), 90.27% (total).
  • Demonstrated superior performance in exploring gait instantaneous conversion compared to state-of-the-art methods.

Conclusions:

  • The proposed 1D-DTCWT method offers a more stable and accurate approach to motion intent recognition.
  • This technique effectively captures the continuous features of human lower limb movements.
  • The findings indicate a significant advancement in lower limb prosthesis control and user experience.