M. N. Rylo, Walmir A. Silva, R. L. P. Medeiros, V. Lucena
{"title":"基于低频PPG的手腕动作手势识别","authors":"M. N. Rylo, Walmir A. Silva, R. L. P. Medeiros, V. Lucena","doi":"10.1109/ICCE-Berlin56473.2022.9937135","DOIUrl":null,"url":null,"abstract":"This paper evaluated two machine learning techniques using low-frequency photoplethysmography and motion sensor data from wearable devices in gesture segmentation and classification. SVM and random forests were the classifiers selected for testing. Preliminary evaluations show that frequencies of 25 Hz are suitable for the recognition process, achieving an F1-score of 0.819 for seven gesture sets.","PeriodicalId":138931,"journal":{"name":"2022 IEEE 12th International Conference on Consumer Electronics (ICCE-Berlin)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Gesture recognition of wrist motion using low-frequency PPG\",\"authors\":\"M. N. Rylo, Walmir A. Silva, R. L. P. Medeiros, V. Lucena\",\"doi\":\"10.1109/ICCE-Berlin56473.2022.9937135\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper evaluated two machine learning techniques using low-frequency photoplethysmography and motion sensor data from wearable devices in gesture segmentation and classification. SVM and random forests were the classifiers selected for testing. Preliminary evaluations show that frequencies of 25 Hz are suitable for the recognition process, achieving an F1-score of 0.819 for seven gesture sets.\",\"PeriodicalId\":138931,\"journal\":{\"name\":\"2022 IEEE 12th International Conference on Consumer Electronics (ICCE-Berlin)\",\"volume\":\"12 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-09-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE 12th International Conference on Consumer Electronics (ICCE-Berlin)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCE-Berlin56473.2022.9937135\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 12th International Conference on Consumer Electronics (ICCE-Berlin)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCE-Berlin56473.2022.9937135","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Gesture recognition of wrist motion using low-frequency PPG
This paper evaluated two machine learning techniques using low-frequency photoplethysmography and motion sensor data from wearable devices in gesture segmentation and classification. SVM and random forests were the classifiers selected for testing. Preliminary evaluations show that frequencies of 25 Hz are suitable for the recognition process, achieving an F1-score of 0.819 for seven gesture sets.
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