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A Deep Learning Framework With Domain Generalization and Few-Shot Learning for Locomotion Mode Classification Across
Eugenio Anselmino1, Ann M Simon2, Levi J Hargrove3
1Department of Excellence in Robotics and AI, and The BioRobotics Institute, Scuola Superiore Sant'Anna, 56127 Pisa.
IEEE Transactions on Medical Robotics and Bionics
|April 2, 2026
Summary
This study introduces a deep learning framework for classifying prosthetic leg movements across different sessions and users. The novel approach achieves high accuracy, improving prosthetic control for transfemoral amputees.
Area of Science:
- Biomedical Engineering
- Machine Learning
- Prosthetics
Background:
- Transfemoral amputees require reliable prosthetic control for daily activities.
- Accurate classification of locomotion modes is crucial for advanced prosthetic function.
- Inter-session and inter-subject variability pose challenges for current prosthetic algorithms.
Purpose of the Study:
- To develop and validate a deep learning framework for robust locomotion mode classification in transfemoral amputees.
- To address the challenge of classifying movements across different sessions, subjects, and prosthesis models.
- To improve the clinical applicability of prosthetic control systems.
Main Methods:
- A deep-learning framework utilizing domain-adversarial training and few-shot learning fine-tuning was employed.
- The approach was validated using a leave-one-session-out cross-validation strategy on a dataset from 11 subjects.
- Data from two different prosthesis models (Vanderbilt University Gen 2 and Gen 3) were merged for analysis.
- Locomotion modes included level walking, incline/decline walking, and stair ascent/descent, analyzed at heel-strike (HS) and toe-off (TO) events.
Main Results:
- The proposed framework achieved high median f1-scores: 99.12% (HS, VU Gen 2), 92.41% (HS, VU Gen 3), 96.83% (TO, VU Gen 2), and 94.36% (TO, VU Gen 3).
- The method demonstrated strong performance on unseen sessions and subjects across different prosthesis models.
- Comparisons showed superior performance over prosthesis-specific classifiers.
Conclusions:
- The developed deep learning framework offers a promising solution for reliable locomotion mode classification in unseen data.
- This approach enhances the potential for seamless integration and improved functionality of prosthetic devices.
- The framework's ability to generalize across sessions, subjects, and prosthesis models is key for clinical implementation.
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