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This paper presents a hierarchical detection algorithm for lower limb activities: sit-to-stand (STS) transfer, walking (W), stair ascent (SA), and stair descent (SD). The algorithm employs a Finite State Machine (FSM) and uses angular position and acceleration data in the sagittal plane to identify transitions between states. Expanding prior work [1], these activities are segmented into biomechanic-based states. The system integrates a Functional Electrical Stimulator (FES) to activate muscles in response to each activity, stimulating the lower limb muscles accordingly. Electromyography activity, which characterizes muscle activation patterns, is captured using the g.USBAMP biosignal amplifier. The algorithm was implemented in a neuroprosthetic device and validated with six healthy subjects, achieving satisfactory performance in terms of latency and accuracy.

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Hierarchical Detection and Segmentation of Lower Limb Activities for Motor Neuroprosthesis

  • 会议论文

  • 发表日期：

    2025年

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  • Sergio Elizalde,
  • Maximiliano Bonnin,
  • Juan Barboza,
  • Fernando Brunetti

摘要

This paper presents a hierarchical detection algorithm for lower limb activities: sit-to-stand (STS) transfer, walking (W), stair ascent (SA), and stair descent (SD). The algorithm employs a Finite State Machine (FSM) and uses angular position and acceleration data in the sagittal plane to identify transitions between states. Expanding prior work [1], these activities are segmented into biomechanic-based states. The system integrates a Functional Electrical Stimulator (FES) to activate muscles in response to each activity, stimulating the lower limb muscles accordingly. Electromyography activity, which characterizes muscle activation patterns, is captured using the g.USBAMP biosignal amplifier. The algorithm was implemented in a neuroprosthetic device and validated with six healthy subjects, achieving satisfactory performance in terms of latency and accuracy.