M. A. Ayu, T. Mantoro, A. F. A. Matin, Saeed Salem Basamh
{"title":"基于来自手机的加速度计数据来识别用户活动","authors":"M. A. Ayu, T. Mantoro, A. F. A. Matin, Saeed Salem Basamh","doi":"10.1109/ISCI.2011.5958987","DOIUrl":null,"url":null,"abstract":"Activity recognition refers to the ability of a machine/device to recognize the activity of users. This area of research has attracted many works especially related to the context aware and ubiquitous computing area. Wearable accelerometers have been explored for this activity recognition purpose; however the impracticality of attaching accelerometers to the user presents significant issues. Accelerometers today are embedded in many mobile devices. This paper explores the potential and possibility of using these accelerometer data to determine user activity recognition. The initial experiments show encouraging results with a very good accuracy rate of 92%. A simple prototype developed supports the implementation of the recognition process conducted.","PeriodicalId":166647,"journal":{"name":"2011 IEEE Symposium on Computers & Informatics","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"32","resultStr":"{\"title\":\"Recognizing user activity based on accelerometer data from a mobile phone\",\"authors\":\"M. A. Ayu, T. Mantoro, A. F. A. Matin, Saeed Salem Basamh\",\"doi\":\"10.1109/ISCI.2011.5958987\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Activity recognition refers to the ability of a machine/device to recognize the activity of users. This area of research has attracted many works especially related to the context aware and ubiquitous computing area. Wearable accelerometers have been explored for this activity recognition purpose; however the impracticality of attaching accelerometers to the user presents significant issues. Accelerometers today are embedded in many mobile devices. This paper explores the potential and possibility of using these accelerometer data to determine user activity recognition. The initial experiments show encouraging results with a very good accuracy rate of 92%. A simple prototype developed supports the implementation of the recognition process conducted.\",\"PeriodicalId\":166647,\"journal\":{\"name\":\"2011 IEEE Symposium on Computers & Informatics\",\"volume\":\"28 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-03-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"32\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 IEEE Symposium on Computers & Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISCI.2011.5958987\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 IEEE Symposium on Computers & Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCI.2011.5958987","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Recognizing user activity based on accelerometer data from a mobile phone
Activity recognition refers to the ability of a machine/device to recognize the activity of users. This area of research has attracted many works especially related to the context aware and ubiquitous computing area. Wearable accelerometers have been explored for this activity recognition purpose; however the impracticality of attaching accelerometers to the user presents significant issues. Accelerometers today are embedded in many mobile devices. This paper explores the potential and possibility of using these accelerometer data to determine user activity recognition. The initial experiments show encouraging results with a very good accuracy rate of 92%. A simple prototype developed supports the implementation of the recognition process conducted.