D. Aranki, U. Balakrishnan, H. Sarver, Lucas Serven, Carlos Asuncion, Kaidi Du, Caitlin Gruis, Gao Xian Peh, Yu Xiao, R. Bajcsy
{"title":"RunningCoach:长跑运动员节奏训练系统","authors":"D. Aranki, U. Balakrishnan, H. Sarver, Lucas Serven, Carlos Asuncion, Kaidi Du, Caitlin Gruis, Gao Xian Peh, Yu Xiao, R. Bajcsy","doi":"10.1145/3154862.3154935","DOIUrl":null,"url":null,"abstract":"Long-distance running is a category of sports that is injury-prone. Half of the injuries sustained in long-distance running are at the knee and are attributed to the inability of the lower extremity joints to sufficiently handle the load applied during initial stance. Furthermore, cadence (steps per minute) has been identified as a factor that is strongly associated with running-related injuries. Increasing cadence results in reduced energy absorption at the hip and the knee, thus reducing the risk of some common running injuries. Therefore, it is vital for runners to run at an appropriate running cadence in order to minimize risk of injury. In this paper, we present an mHealth system that remotely monitors running cadence levels of runners in a continuous fashion, among other variables, and provides immediate feedback to runners in an effort to help them optimize their running cadence. We also present some initial findings based on a feasibility study we are currently conducting using this system.","PeriodicalId":200810,"journal":{"name":"Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare","volume":"132 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"RunningCoach: cadence training system for long-distance runners\",\"authors\":\"D. Aranki, U. Balakrishnan, H. Sarver, Lucas Serven, Carlos Asuncion, Kaidi Du, Caitlin Gruis, Gao Xian Peh, Yu Xiao, R. Bajcsy\",\"doi\":\"10.1145/3154862.3154935\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Long-distance running is a category of sports that is injury-prone. Half of the injuries sustained in long-distance running are at the knee and are attributed to the inability of the lower extremity joints to sufficiently handle the load applied during initial stance. Furthermore, cadence (steps per minute) has been identified as a factor that is strongly associated with running-related injuries. Increasing cadence results in reduced energy absorption at the hip and the knee, thus reducing the risk of some common running injuries. Therefore, it is vital for runners to run at an appropriate running cadence in order to minimize risk of injury. In this paper, we present an mHealth system that remotely monitors running cadence levels of runners in a continuous fashion, among other variables, and provides immediate feedback to runners in an effort to help them optimize their running cadence. We also present some initial findings based on a feasibility study we are currently conducting using this system.\",\"PeriodicalId\":200810,\"journal\":{\"name\":\"Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare\",\"volume\":\"132 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-05-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3154862.3154935\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3154862.3154935","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
RunningCoach: cadence training system for long-distance runners
Long-distance running is a category of sports that is injury-prone. Half of the injuries sustained in long-distance running are at the knee and are attributed to the inability of the lower extremity joints to sufficiently handle the load applied during initial stance. Furthermore, cadence (steps per minute) has been identified as a factor that is strongly associated with running-related injuries. Increasing cadence results in reduced energy absorption at the hip and the knee, thus reducing the risk of some common running injuries. Therefore, it is vital for runners to run at an appropriate running cadence in order to minimize risk of injury. In this paper, we present an mHealth system that remotely monitors running cadence levels of runners in a continuous fashion, among other variables, and provides immediate feedback to runners in an effort to help them optimize their running cadence. We also present some initial findings based on a feasibility study we are currently conducting using this system.