{"title":"ikneebrace:步态检测中运动传感器测量的膝关节内收力矩评估","authors":"Hsin-Ruey Tsai, Shih-Yao Wei, Jui-Chun Hsiao, Ting-Wei Chiu, Yi-Ping Lo, Chi-Feng Keng, Y. Hung, Jin-Jong Chen","doi":"10.1145/2971648.2971675","DOIUrl":null,"url":null,"abstract":"We propose light-weight wearable devices, iKneeBraces, to prevent knee osteoarthritis (OA) using knee adduction moment (KAM) evaluation. iKneeBrace consists of two inertial measurement units (IMUs) to measure shin and thigh angles. KAM is estimated by ground force reaction (GRF), knee position and center of pressure position. Instead of heavy and bulky 3DoF force plates conventionally used, we propose to build a 2D input regression model using shin and thigh angles from iKneeBrace as input to infer GRF direction and further estimate KAM. We perform an experiment to evaluate the method. The results show that iKneeBrace can infer KAM similar to the ground truth in the first peak, the most important part to prevent knee OA. Furthermore, the proposed method can infer KAM in all parts if better IMUs used in iKneeBrace in the future. The proposed method not only makes KAM evaluation portable but also requires only light-weight devices.","PeriodicalId":303792,"journal":{"name":"Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"iKneeBraces: knee adduction moment evaluation measured by motion sensors in gait detection\",\"authors\":\"Hsin-Ruey Tsai, Shih-Yao Wei, Jui-Chun Hsiao, Ting-Wei Chiu, Yi-Ping Lo, Chi-Feng Keng, Y. Hung, Jin-Jong Chen\",\"doi\":\"10.1145/2971648.2971675\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose light-weight wearable devices, iKneeBraces, to prevent knee osteoarthritis (OA) using knee adduction moment (KAM) evaluation. iKneeBrace consists of two inertial measurement units (IMUs) to measure shin and thigh angles. KAM is estimated by ground force reaction (GRF), knee position and center of pressure position. Instead of heavy and bulky 3DoF force plates conventionally used, we propose to build a 2D input regression model using shin and thigh angles from iKneeBrace as input to infer GRF direction and further estimate KAM. We perform an experiment to evaluate the method. The results show that iKneeBrace can infer KAM similar to the ground truth in the first peak, the most important part to prevent knee OA. Furthermore, the proposed method can infer KAM in all parts if better IMUs used in iKneeBrace in the future. The proposed method not only makes KAM evaluation portable but also requires only light-weight devices.\",\"PeriodicalId\":303792,\"journal\":{\"name\":\"Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-09-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2971648.2971675\",\"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 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2971648.2971675","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
iKneeBraces: knee adduction moment evaluation measured by motion sensors in gait detection
We propose light-weight wearable devices, iKneeBraces, to prevent knee osteoarthritis (OA) using knee adduction moment (KAM) evaluation. iKneeBrace consists of two inertial measurement units (IMUs) to measure shin and thigh angles. KAM is estimated by ground force reaction (GRF), knee position and center of pressure position. Instead of heavy and bulky 3DoF force plates conventionally used, we propose to build a 2D input regression model using shin and thigh angles from iKneeBrace as input to infer GRF direction and further estimate KAM. We perform an experiment to evaluate the method. The results show that iKneeBrace can infer KAM similar to the ground truth in the first peak, the most important part to prevent knee OA. Furthermore, the proposed method can infer KAM in all parts if better IMUs used in iKneeBrace in the future. The proposed method not only makes KAM evaluation portable but also requires only light-weight devices.