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Updated: Aug 4, 2025
Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis
Published on: July 22, 2014
Novel computational protocol to support transfemoral prosthetic alignment procedure using machine learning techniques
Andres M Cárdenas1, Juliana Uribe2, Josep M Font-Llagunes3
1Bioinstrumentation and Clinical Engineering Research Group - GIBIC, Bioengineering Department, Engineering Faculty, Universidad de Antioquia UdeA, Calle 70 No. 52-21, Medellín, Colombia; Research Group in Computational Modeling and Simulation - GIMSC, Engineering Faculty, Universidad de San Buenaventura, Carrera 56C No. 51-110, Medellín, Colombia.
Machine learning aids prosthetists in aligning prosthetic limbs, reducing gait deviations and disease. This computational protocol improves amputee-prosthesis adherence and optimizes gait quality.
Area of Science:
- Biomedical Engineering
- Rehabilitation Technology
- Machine Learning in Healthcare
Background:
- Prosthetic alignment is crucial for acceptable gait and preventing long-term disease.
- Current alignment assessment is subjective and relies heavily on prosthetist experience.
- Machine learning offers a potential solution to assist in optimal prosthetic alignment.
Purpose of the Study:
- To introduce a novel computational protocol for prosthetic alignment using machine learning.
- To aid prosthetists in accurately assessing and achieving optimal prosthetic alignment.
- To reduce subjectivity and improve consistency in prosthetic alignment procedures.
Main Methods:
- Trained machine learning models (SVM and neural network) on ground reaction force data from 16 transfemoral amputees with various alignments.
- Models predicted alignment condition, magnitude, and angle for correction.
- Validated the protocol with junior and senior prosthetists on two amputees.
Main Results:
- Support vector machine model achieved 92.6% accuracy in detecting nominal alignment.
- Neural network recovered correction angles with 94.11% accuracy and a 0.51° fitting error.
- Computational models and prosthetists showed agreement in alignment assessment, with high gait quality satisfaction scores (8/10 and 9.6/10).
Conclusions:
- The developed computational protocol effectively assists prosthetists in prosthetic alignment.
- This tool can decrease gait deviations and musculoskeletal diseases linked to misalignment.
- Improved alignment accuracy and consistency enhance amputee-prosthesis adherence and overall gait quality.
