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Related Experiment Video

Updated: May 11, 2026

Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis

Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis

Published on: July 22, 2014

An Individual Prosthesis Control Method with Human Subjective Choices.

Lei Sun¹, Hongxu Ma¹, Honglei An¹

¹College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China.

Biomimetics (Basel, Switzerland)

|February 23, 2024

Summary

This study presents a new method for personalized lower-limb prosthesis trajectory planning, optimizing gait for amputees. The approach improves walking comfort and safety by adapting to individual movement patterns.

Keywords:

gait trajectory individual algorithm lower limb prosthesis planning subjective choices

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Last Updated: May 11, 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

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Published on: November 6, 2015

Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses

Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses

Published on: January 7, 2019

Area of Science:

Biomedical Engineering
Robotics
Human-Computer Interaction

Background:

Intelligent lower-limb prostheses aim to enhance mobility and convenience for amputees.
Effective prosthetic control relies heavily on joint trajectory planning, directly impacting performance and user comfort.
Individual physiological differences and walking habits necessitate personalized prosthetic gait patterns.

Purpose of the Study:

To develop and validate a method for personalized trajectory planning in intelligent lower-limb prostheses.
To adapt average gait trajectories to individual user demands for improved comfort and safety.
To integrate human subjective feedback into the prosthetic control loop for more intuitive operation.

Main Methods:

Gait trajectories were normalized and planned using the phase variable method.
Deviations between optimal and current trajectories were analyzed using Fourier series expansion.
A multi-subject kinematics dataset was utilized to demonstrate the method's feasibility and effectiveness.

Main Results:

The proposed algorithm successfully optimized average gait trajectories into personalized ones for individual subjects.
Experimental results validated the feasibility and effectiveness of the individual trajectory planning method.
The optimization process demonstrated a significant improvement in adapting prosthetics to user-specific gait.

Conclusions:

Personalized trajectory planning is crucial for enhancing the intelligence and user experience of lower-limb prostheses.
The developed method offers a viable approach to creating prosthetics that more closely mimic individual human locomotion.
This advancement contributes to improved walking comfort, safety, and overall functionality for lower-limb amputees.