Sirajum Munir, Jonathan Francis, Matias Quintana, Nadine von Frankenberg, M. Berges
Thermal comfort is very important for well-being and productivity of building occupants. It has been shown that body shape is a useful feature to determine thermal comfort of individuals [2]. It is because, the heat dissipation rate of individuals depends on the body surface area. As a result, a tall and skinny person can tolerate higher room temperature than a rounded body shape person [5]. In order to test this hypothesis, we performed a year-long experiment in 2017, where we recruited 77 participants and put each of them in a thermally controlled conference room in CMU for 3 hours and recorded their subjective responses regarding thermal comfort at different temperature ranging from 60°F to 80°F. In addition, we collected depth data of individuals using a vertically mounted Microsoft Kinect for XBOX One at the entrance of the conference room to capture their body shape. We also collected biometric features (e.g., Galvanic Skin Response (GSR), skin temperature) using a Microsoft Health Band worn by the subjects. The resulting dataset provides rich information regarding how different features can be used to infer thermal comfort of the individuals.
{"title":"Dataset","authors":"Sirajum Munir, Jonathan Francis, Matias Quintana, Nadine von Frankenberg, M. Berges","doi":"10.1145/3359427.3361915","DOIUrl":"https://doi.org/10.1145/3359427.3361915","url":null,"abstract":"Thermal comfort is very important for well-being and productivity of building occupants. It has been shown that body shape is a useful feature to determine thermal comfort of individuals [2]. It is because, the heat dissipation rate of individuals depends on the body surface area. As a result, a tall and skinny person can tolerate higher room temperature than a rounded body shape person [5]. In order to test this hypothesis, we performed a year-long experiment in 2017, where we recruited 77 participants and put each of them in a thermally controlled conference room in CMU for 3 hours and recorded their subjective responses regarding thermal comfort at different temperature ranging from 60°F to 80°F. In addition, we collected depth data of individuals using a vertically mounted Microsoft Kinect for XBOX One at the entrance of the conference room to capture their body shape. We also collected biometric features (e.g., Galvanic Skin Response (GSR), skin temperature) using a Microsoft Health Band worn by the subjects. The resulting dataset provides rich information regarding how different features can be used to infer thermal comfort of the individuals.","PeriodicalId":263447,"journal":{"name":"Proceedings of the 2nd Workshop on Data Acquisition To Analysis - DATA'19","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125477155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Usman Raza, Aftab Khan, Roget Kou, T. Farnham, Thajanee Premalal, Aleksandar Stanoev, W. Thompson
Localization accuracy varies significantly across multiple radio and optical technologies. The choice of which technology is a best-fit for a particular application is not only based on the accuracy alone but also on other factors including the cost, availability of the technology in commodity hardware such as mobile phones, and the need to have a pre-existing fixed infrastructure. As such, there is no one-size-fits-all solution and therefore we provide, from a single indoor testbed, localization data collected from three very different technologies including the narrowband Bluetooth Low Energy, the Ultra-Wideband system from Decawave, and an optical motion capture system from OptiTrack. By doing so, we hope to encourage research on benchmarking of multiple localization technologies, multi-technology data fusion techniques, fingerprinting, and calibration methods of less accurate localization systems using more accurate ones.
{"title":"Dataset","authors":"Usman Raza, Aftab Khan, Roget Kou, T. Farnham, Thajanee Premalal, Aleksandar Stanoev, W. Thompson","doi":"10.1145/3359427.3361919","DOIUrl":"https://doi.org/10.1145/3359427.3361919","url":null,"abstract":"Localization accuracy varies significantly across multiple radio and optical technologies. The choice of which technology is a best-fit for a particular application is not only based on the accuracy alone but also on other factors including the cost, availability of the technology in commodity hardware such as mobile phones, and the need to have a pre-existing fixed infrastructure. As such, there is no one-size-fits-all solution and therefore we provide, from a single indoor testbed, localization data collected from three very different technologies including the narrowband Bluetooth Low Energy, the Ultra-Wideband system from Decawave, and an optical motion capture system from OptiTrack. By doing so, we hope to encourage research on benchmarking of multiple localization technologies, multi-technology data fusion techniques, fingerprinting, and calibration methods of less accurate localization systems using more accurate ones.","PeriodicalId":263447,"journal":{"name":"Proceedings of the 2nd Workshop on Data Acquisition To Analysis - DATA'19","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117245790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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