J. López-Leyva, E. Mejia-Gonzalez, J. Estrada-Lechuga, Raul I. Ramos-Garcia
{"title":"基于便携式肌电读卡器的自动腿部手势识别","authors":"J. López-Leyva, E. Mejia-Gonzalez, J. Estrada-Lechuga, Raul I. Ramos-Garcia","doi":"10.1109/ICMEAE.2019.00008","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":422872,"journal":{"name":"2019 International Conference on Mechatronics, Electronics and Automotive Engineering (ICMEAE)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Automatic Leg Gesture Recognition Based on Portable Electromyography Readers\",\"authors\":\"J. López-Leyva, E. Mejia-Gonzalez, J. Estrada-Lechuga, Raul I. Ramos-Garcia\",\"doi\":\"10.1109/ICMEAE.2019.00008\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":422872,\"journal\":{\"name\":\"2019 International Conference on Mechatronics, Electronics and Automotive Engineering (ICMEAE)\",\"volume\":\"85 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 International Conference on Mechatronics, Electronics and Automotive Engineering (ICMEAE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMEAE.2019.00008\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Conference on Mechatronics, Electronics and Automotive Engineering (ICMEAE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMEAE.2019.00008","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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 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.