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Related Experiment Video

Updated: May 11, 2026

Oscillation and Reaction Board Techniques for Estimating Inertial Properties of a Below-knee Prosthesis

Oscillation and Reaction Board Techniques for Estimating Inertial Properties of a Below-knee Prosthesis

Published on: May 8, 2014

Diffusion models enable zero-shot pose estimation for lower-limb prosthetic users.

Tianxun Zhou¹, Muhammad Nur Shahril Iskandar², Keng-Hwee Chiam¹

¹Bioinformatics Institute, Agency for Science, Technology and Research, Singapore, Singapore.

PLOS Digital Health

|March 31, 2025

Summary

This study introduces a novel zero-shot method for markerless gait analysis in prosthetic limb users. It significantly reduces errors in prosthetic joint detection, aiding in rehabilitation assessment.

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Area of Science:

Biomechanics
Computer Vision
Rehabilitation Engineering

Background:

Quantitative gait analysis is crucial for lower-limb prosthetic users' rehabilitation.
Markerless motion capture is challenging due to difficulties in detecting prosthetic joints with standard models.

Purpose of the Study:

To propose a zero-shot method for markerless gait analysis in lower-limb prosthetic users.
To leverage generative diffusion models to improve prosthetic joint detection.

Main Methods:

Utilized generative diffusion models (ControlNet) to transform prosthetic limb images into able-bodied representations.
Applied standard pose estimation models (OpenPose) to synthetic images for keypoint detection.
Evaluated the method on publicly available videos of transfemoral and transtibial amputees.

Main Results:

Achieved significant reductions in keypoint coordinate errors: 37% for transtibial and 76% for transfemoral prostheses.
Enabled identification of gait deviations like reduced knee flexion and altered kinematic timing.
Demonstrated feasibility of markerless gait analysis for prosthetic users.

Conclusions:

The zero-shot approach shows potential for personalized rehabilitation using consumer cameras.
Further validation on diverse prosthetic designs and larger datasets is warranted.
This method can enhance gait assessment for individuals with lower-limb prosthetics.