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A feasibility study of depth image based intent recognition for lower limb prostheses.

Huseyin Atakan Varol, Yerzhan Massalin

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 9, 2017
    PubMed
    Summary

    This study introduces a depth camera system for recognizing human activities like walking and running. The system shows promise for controlling robotic prosthetic legs by accurately detecting activity transitions.

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    Area of Science:

    • Robotics
    • Biomedical Engineering
    • Computer Vision

    Background:

    • Robotic prosthetic legs require intuitive control systems.
    • Accurate human activity recognition is crucial for seamless prosthetic integration.
    • Depth cameras offer a non-invasive method for capturing kinematic data.

    Purpose of the Study:

    • To develop and evaluate a depth camera-based system for recognizing human activity modes.
    • To assess the system's potential for real-time application in controlling robotic prosthetic legs.
    • To improve the robustness and accuracy of activity recognition using depth data.

    Main Methods:

    • Activity modes (standing, walking, running, stair ascent/descent) were inferred using depth camera data.
    • Depth difference images were employed to distinguish between static and dynamic activities.
    • Features were extracted from filtered depth frames, and a support vector machine with a cubic kernel was used for classification.
    • A voting filter was applied for post-processing to enhance recognition robustness.

    Main Results:

    • The depth camera system successfully identified 28 activity mode transitions in experiments with a healthy subject.
    • The system demonstrated the efficacy of depth-based activity recognition for prosthetic leg applications.
    • A minor inaccuracy was observed in a run-to-stand transition, involving an intermediate run-to-walk recognition.

    Conclusions:

    • The preliminary results indicate the feasibility of a depth camera-based system for intent recognition in robotic prosthetics.
    • The approach shows potential for enhancing user experience and control of lower-limb robotic devices.
    • Further research and refinement are warranted to address complex transitions and improve real-world performance.