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Sensors 2017, 17(9), 2020; https://doi.org/10.3390/s17092020

An Adaptive Classification Strategy for Reliable Locomotion Mode Recognition

Neuromuscular Rehabilitation Engineering Laboratory, UNC/NCSU Joint Department of Biomedical Engineering, North Carolina State University, Raleigh, NC, 27606, USA
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Received: 24 July 2017 / Revised: 25 August 2017 / Accepted: 25 August 2017 / Published: 4 September 2017
(This article belongs to the Special Issue Sensors for Health Monitoring and Disease Diagnosis)
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Abstract

Algorithms for locomotion mode recognition (LMR) based on surface electromyography and mechanical sensors have recently been developed and could be used for the neural control of powered prosthetic legs. However, the variations in input signals, caused by physical changes at the sensor interface and human physiological changes, may threaten the reliability of these algorithms. This study aimed to investigate the effectiveness of applying adaptive pattern classifiers for LMR. Three adaptive classifiers, i.e., entropy-based adaptation (EBA), LearnIng From Testing data (LIFT), and Transductive Support Vector Machine (TSVM), were compared and offline evaluated using data collected from two able-bodied subjects and one transfemoral amputee. The offline analysis indicated that the adaptive classifier could effectively maintain or restore the performance of the LMR algorithm when gradual signal variations occurred. EBA and LIFT were recommended because of their better performance and higher computational efficiency. Finally, the EBA was implemented for real-time human-in-the-loop prosthesis control. The online evaluation showed that the applied EBA effectively adapted to changes in input signals across sessions and yielded more reliable prosthesis control over time, compared with the LMR without adaptation. The developed novel adaptive strategy may further enhance the reliability of neurally-controlled prosthetic legs. View Full-Text
Keywords: locomotion mode recognition; powered prosthesis leg; adaptive pattern classifier; surface electromyography; and human-in-the-loop locomotion mode recognition; powered prosthesis leg; adaptive pattern classifier; surface electromyography; and human-in-the-loop
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Liu, M.; Zhang, F.; Huang, H.H. An Adaptive Classification Strategy for Reliable Locomotion Mode Recognition. Sensors 2017, 17, 2020.

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