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An Adaptive Classification Strategy for Reliable Locomotion Mode Recognition.

Ming Liu¹, Fan Zhang², He Helen Huang³

¹Neuromuscular Rehabilitation Engineering Laboratory, UNC/NCSU Joint Department of Biomedical Engineering, North Carolina State University, Raleigh, NC, 27606, USA. mliu10@ncsu.edu.

Sensors (Basel, Switzerland)

|September 5, 2017

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Adaptive algorithms improve prosthetic leg control by maintaining reliable locomotion mode recognition despite signal changes. Entropy-based adaptation (EBA) and Learning From Testing data (LIFT) show promise for enhanced neural control.

Area of Science:

Biomedical Engineering
Rehabilitation Engineering
Neuroprosthetics

Background:

Locomotion mode recognition (LMR) algorithms using surface electromyography and mechanical sensors are crucial for neural control of powered prosthetic legs.
Signal variations due to sensor interface changes and physiological shifts can compromise LMR algorithm reliability.

Purpose of the Study:

To investigate the effectiveness of adaptive pattern classifiers for enhancing LMR reliability.
To compare the performance of entropy-based adaptation (EBA), LearnIng From Testing data (LIFT), and Transductive Support Vector Machine (TSVM) for LMR.

Main Methods:

Offline evaluation of three adaptive classifiers (EBA, LIFT, TSVM) using data from two able-bodied subjects and one transfemoral amputee.
Online, real-time human-in-the-loop evaluation of the EBA classifier for prosthesis control.

Keywords:

adaptive pattern classifier and human-in-the-loop locomotion mode recognition powered prosthesis leg surface electromyography

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Main Results:

Offline analysis demonstrated that adaptive classifiers maintain or restore LMR performance during gradual signal variations.
EBA and LIFT exhibited superior performance and computational efficiency compared to TSVM.
Online evaluation confirmed EBA's ability to adapt to signal changes across sessions, leading to more reliable prosthesis control over time.

Conclusions:

Adaptive pattern classifiers effectively enhance the reliability of LMR algorithms for neurally-controlled prosthetic legs.
EBA and LIFT are recommended for their performance and efficiency.
The developed adaptive strategy, particularly EBA, offers a promising approach for improving the robustness and reliability of prosthetic leg control.