Automatic Leg Gesture Recognition Based on Portable Electromyography Readers

In this paper, recognition of leg gestures is performed using Linear Discriminant Analysis in order to propose a real application for prosthetic leg considering transfemoral amputee. As results, the confusion matrix shows the performance of the algorithm, where the Class #1 and #3 were the best clas...

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Bibliographic Details
Published in2019 International Conference on Mechatronics, Electronics and Automotive Engineering (ICMEAE) pp. 3 - 6
Main Authors Lopez-Leyva, Josue A., Mejia-Gonzalez, Efrain A., Estrada-Lechuga, Jessica, Ramos-Garcia, Raul. I.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2019
Subjects
discriminant analysis
Electrodes
Electromyography
gestures recognition
Legged locomotion
Linear discriminant analysis
Machine learning
Muscles
Prosthetics
temporal features
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Summary:In this paper, recognition of leg gestures is performed using Linear Discriminant Analysis in order to propose a real application for prosthetic leg considering transfemoral amputee. As results, the confusion matrix shows the performance of the algorithm, where the Class #1 and #3 were the best classes classified (sensitivity is 100%), and Class #2 was the worst classified (sensitivity is 67%). In addition, the probability that the classifier ranks a randomly chosen positive instance higher than a randomly chosen negative for Class #2 and #4 is the same, AUC =0.94, and AUC =1 for Class #1 and #3. Although the hardware and algorithm used have adequate performance, the optimization and improve the real testing conditions are important requirements for real human applications.
ISSN:2573-3001
DOI:10.1109/ICMEAE.2019.00008