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Related Concept Videos

Root-Locus Method01:19

Root-Locus Method

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This system can be represented by a block diagram,...

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Related Experiment Video

Updated: May 14, 2026

Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis
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Published on: July 22, 2014

An automatic and user-driven training method for locomotion mode recognition for artificial leg control.

Xiaorong Zhang1, Ding Wang, Qing Yang

  • 1Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI 02881, USA.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|February 1, 2013
PubMed
Summary
This summary is machine-generated.

A new, automatic training method simplifies using powered artificial legs. This user-driven system enhances locomotion-mode-recognition (LMR) for intuitive prosthetic control without expert assistance.

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Area of Science:

  • Biomedical Engineering
  • Robotics
  • Human-Computer Interaction

Background:

  • Locomotion-mode-recognition (LMR) systems offer intuitive control for powered artificial legs.
  • Current LMR training methods are slow and impractical for clinical use in prosthetic legs.

Purpose of the Study:

  • To design an automatic, user-driven training method for LMR systems in prosthetic legs.
  • To simplify the training procedure and enable self-training for prosthesis users.

Main Methods:

  • Developed a wearable terrain detection interface using a laser distance sensor and inertial measurement unit (IMU).
  • Integrated prosthetic pylon measurements for gait phase detection.
  • Combined terrain and gait data for real-time automatic identification of locomotion mode transitions and data labeling.

Main Results:

  • The new method accurately detects terrain changes and gait phases.
  • Automatic identification and labeling of movement class and gait phase were achieved in real-time.
  • Pilot results on an able-bodied subject demonstrated accuracy and user-friendliness.

Conclusions:

  • The novel training method significantly simplifies LMR system training for powered lower limb prostheses.
  • This approach removes the need for external devices and expert assistance.
  • The system paves the way for clinical adoption of LMR in prosthetic leg control.