{"title":"你在哪里?:利用智能手机传感器数据进行人体活动识别","authors":"Gulustan Dogan, Iremnaz Cay, Sinem Sena Ertas, Seref Recep Keskin, Nouran Alotaibi, Elif Sahin","doi":"10.1145/3410530.3414354","DOIUrl":null,"url":null,"abstract":"This paper describes our submission as Team-Petrichor to the competition that was organized by the SHL recognition challenge dataset authors. We compared multiple machine learning approach for classifying eight different activities (Still, Walk, Run, Bike, Car, Bus, Train, Subway). The first step was feature engineering, a wide set of statistical domain features were computed and their quality was evaluated. Finally, the appropriate machine learning model was chosen. The recognition result for the testing dataset will be presented in the summary paper of the SHL recognition challenge.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"7 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Where are you?: human activity recognition with smartphone sensor data\",\"authors\":\"Gulustan Dogan, Iremnaz Cay, Sinem Sena Ertas, Seref Recep Keskin, Nouran Alotaibi, Elif Sahin\",\"doi\":\"10.1145/3410530.3414354\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper describes our submission as Team-Petrichor to the competition that was organized by the SHL recognition challenge dataset authors. We compared multiple machine learning approach for classifying eight different activities (Still, Walk, Run, Bike, Car, Bus, Train, Subway). The first step was feature engineering, a wide set of statistical domain features were computed and their quality was evaluated. Finally, the appropriate machine learning model was chosen. The recognition result for the testing dataset will be presented in the summary paper of the SHL recognition challenge.\",\"PeriodicalId\":7183,\"journal\":{\"name\":\"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers\",\"volume\":\"7 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-09-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3410530.3414354\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3410530.3414354","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Where are you?: human activity recognition with smartphone sensor data
This paper describes our submission as Team-Petrichor to the competition that was organized by the SHL recognition challenge dataset authors. We compared multiple machine learning approach for classifying eight different activities (Still, Walk, Run, Bike, Car, Bus, Train, Subway). The first step was feature engineering, a wide set of statistical domain features were computed and their quality was evaluated. Finally, the appropriate machine learning model was chosen. The recognition result for the testing dataset will be presented in the summary paper of the SHL recognition challenge.