{"title":"基于背景减法和图像相交区域的有氧动作性能评价","authors":"F. John, I. Hipiny, Hamimah Ujir, M. Sunar","doi":"10.1109/IConDA47345.2019.9034912","DOIUrl":null,"url":null,"abstract":"It is recommended for a novice person to engage trained personnel before starting an unfamiliar aerobic or weight routine to gain real-time expert feedbacks. This greatly reduces the risk of injury and maximise physical gains. We present a simple image similarity measure based on intersected image region to assess a subject's performance of an aerobic routine. The method was implemented inside an Augmented Reality (AR) desktop app that employed a single RGB camera to capture still images of the subject as he or she progressed through the routine. The background-subtracted body pose image was compared against the exemplar image (i.e., AR template) at specific intervals. Based on a limited dataset, our pose matching function managed an accuracy of 93.67%.","PeriodicalId":175668,"journal":{"name":"2019 International Conference on Computer and Drone Applications (IConDA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Assessing Performance of Aerobic Routines using Background Subtraction and Intersected Image Region\",\"authors\":\"F. John, I. Hipiny, Hamimah Ujir, M. Sunar\",\"doi\":\"10.1109/IConDA47345.2019.9034912\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"It is recommended for a novice person to engage trained personnel before starting an unfamiliar aerobic or weight routine to gain real-time expert feedbacks. This greatly reduces the risk of injury and maximise physical gains. We present a simple image similarity measure based on intersected image region to assess a subject's performance of an aerobic routine. The method was implemented inside an Augmented Reality (AR) desktop app that employed a single RGB camera to capture still images of the subject as he or she progressed through the routine. The background-subtracted body pose image was compared against the exemplar image (i.e., AR template) at specific intervals. Based on a limited dataset, our pose matching function managed an accuracy of 93.67%.\",\"PeriodicalId\":175668,\"journal\":{\"name\":\"2019 International Conference on Computer and Drone Applications (IConDA)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-10-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 International Conference on Computer and Drone Applications (IConDA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IConDA47345.2019.9034912\",\"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 Computer and Drone Applications (IConDA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IConDA47345.2019.9034912","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Assessing Performance of Aerobic Routines using Background Subtraction and Intersected Image Region
It is recommended for a novice person to engage trained personnel before starting an unfamiliar aerobic or weight routine to gain real-time expert feedbacks. This greatly reduces the risk of injury and maximise physical gains. We present a simple image similarity measure based on intersected image region to assess a subject's performance of an aerobic routine. The method was implemented inside an Augmented Reality (AR) desktop app that employed a single RGB camera to capture still images of the subject as he or she progressed through the routine. The background-subtracted body pose image was compared against the exemplar image (i.e., AR template) at specific intervals. Based on a limited dataset, our pose matching function managed an accuracy of 93.67%.