H. Gjoreski, Mathias Ciliberto, Francisco Javier Ordonez, D. Roggen, S. Mekki, S. Valentin
{"title":"A Versatile Annotated Dataset for Multimodal Locomotion Analytics with Mobile Devices","authors":"H. Gjoreski, Mathias Ciliberto, Francisco Javier Ordonez, D. Roggen, S. Mekki, S. Valentin","doi":"10.1145/3131672.3136976","DOIUrl":null,"url":null,"abstract":"We explain how to obtain a highly versatile and precisely annotated dataset for the multimodal locomotion of mobile users. After presenting the experimental setup, data management challenges and potential applications, we conclude with the best practices for assuring data quality and reducing loss. The dataset currently comprises 7 months of measurements, collected by smartphone's sensors and a body-worn camera, while the 3 participants used 8 different modes of transportation. It comprises 950 GB of sensor data, which corresponds to 750 hours of labelled data. The obtained data will be useful for a wide range of research questions related to activity recognition, and will be made available to the community1.","PeriodicalId":424262,"journal":{"name":"Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems","volume":"64 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"32","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3131672.3136976","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 32
Abstract
We explain how to obtain a highly versatile and precisely annotated dataset for the multimodal locomotion of mobile users. After presenting the experimental setup, data management challenges and potential applications, we conclude with the best practices for assuring data quality and reducing loss. The dataset currently comprises 7 months of measurements, collected by smartphone's sensors and a body-worn camera, while the 3 participants used 8 different modes of transportation. It comprises 950 GB of sensor data, which corresponds to 750 hours of labelled data. The obtained data will be useful for a wide range of research questions related to activity recognition, and will be made available to the community1.
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