{"title":"预测传感数据和延长可穿戴设备的电池寿命","authors":"Songchun Fan, Qiuyun Llull, Benjamin C. Lee","doi":"10.1145/3032970.3032971","DOIUrl":null,"url":null,"abstract":"Telepath is a framework that supports communication-free offloading for wearable devices. With offline training, activity recognition tasks can be offloaded from the wearable to the user's phone, without transferring raw sensing data. The key observation is that when the user is carrying both devices, the sensing streams on the two devices are highly correlated. By exploiting the correlation, the phone can estimate the wearable's sensing data and emulate the watch. Our evaluations shows that with Telepath, the phone performs accurately on activity recognition tasks that are designed for smart watches, achieving on average 87% of the watch's accuracy while extending the watch's battery life by 2.1x.","PeriodicalId":309322,"journal":{"name":"Proceedings of the 18th International Workshop on Mobile Computing Systems and Applications","volume":"128 2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predicting Sensory Data and Extending Battery Life for Wearable Devices\",\"authors\":\"Songchun Fan, Qiuyun Llull, Benjamin C. Lee\",\"doi\":\"10.1145/3032970.3032971\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Telepath is a framework that supports communication-free offloading for wearable devices. With offline training, activity recognition tasks can be offloaded from the wearable to the user's phone, without transferring raw sensing data. The key observation is that when the user is carrying both devices, the sensing streams on the two devices are highly correlated. By exploiting the correlation, the phone can estimate the wearable's sensing data and emulate the watch. Our evaluations shows that with Telepath, the phone performs accurately on activity recognition tasks that are designed for smart watches, achieving on average 87% of the watch's accuracy while extending the watch's battery life by 2.1x.\",\"PeriodicalId\":309322,\"journal\":{\"name\":\"Proceedings of the 18th International Workshop on Mobile Computing Systems and Applications\",\"volume\":\"128 2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-02-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 18th International Workshop on Mobile Computing Systems and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3032970.3032971\",\"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 18th International Workshop on Mobile Computing Systems and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3032970.3032971","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Predicting Sensory Data and Extending Battery Life for Wearable Devices
Telepath is a framework that supports communication-free offloading for wearable devices. With offline training, activity recognition tasks can be offloaded from the wearable to the user's phone, without transferring raw sensing data. The key observation is that when the user is carrying both devices, the sensing streams on the two devices are highly correlated. By exploiting the correlation, the phone can estimate the wearable's sensing data and emulate the watch. Our evaluations shows that with Telepath, the phone performs accurately on activity recognition tasks that are designed for smart watches, achieving on average 87% of the watch's accuracy while extending the watch's battery life by 2.1x.