A Deep Learning Approach to EMG-Based Classification of Gait Phases during Level Ground Walking
AbstractCorrectly identifying gait phases is a prerequisite to achieve a spatial/temporal characterization of muscular recruitment during walking. Recent approaches have addressed this issue by applying machine learning techniques to treadmill-walking data. We propose a deep learning approach for surface electromyographic (sEMG)-based classification of stance/swing phases and prediction of the foot–floor-contact signal in more natural walking conditions (similar to everyday walking ones), overcoming constraints of a controlled environment, such as treadmill walking. To this aim, sEMG signals were acquired from eight lower-limb muscles in about 10.000 strides from 23 healthy adults during level ground walking, following an eight-shaped path including natural deceleration, reversing, curve, and acceleration. By means of an extensive evaluation, we show that using a multi layer perceptron to learn hidden features provides state of the art performances while avoiding features engineering. Results, indeed, showed an average classification accuracy of
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Morbidoni, C.; Cucchiarelli, A.; Fioretti, S.; Di Nardo, F. A Deep Learning Approach to EMG-Based Classification of Gait Phases during Level Ground Walking. Electronics 2019, 8, 894.
Morbidoni C, Cucchiarelli A, Fioretti S, Di Nardo F. A Deep Learning Approach to EMG-Based Classification of Gait Phases during Level Ground Walking. Electronics. 2019; 8(8):894.Chicago/Turabian Style
Morbidoni, Christian; Cucchiarelli, Alessandro; Fioretti, Sandro; Di Nardo, Francesco. 2019. "A Deep Learning Approach to EMG-Based Classification of Gait Phases during Level Ground Walking." Electronics 8, no. 8: 894.
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