Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
OccuTherm: Occupant Thermal Comfort Inference using Body Shape Information
OccuTherm: Occupant Thermal Comfort Inference using Body Shape Information This repository contains the official data from a USDOE-funded project at Carnegie Mellon University and Bosch Research Pittsburgh. The primary goal of the project was to investigate the relationship between indoor commercial building occupant thermal comfort and various biometric and environmental predictors. We performed 77 individual comfort experiments, approved by our Institutional Review Board (IRB) and in satisfaction of participant consent guidelines. Our goal was to generate a dataset than enables comprehensive study of human thermal comfort preferences, in a commercial building environment, across a wide range of indoor environmental conditions. The data is comprised of the following feature groups: depth camera frames, biometrics sensor data, body shape information, subjective comfort data from the mobile device application, environmental sensor data from the commercial building HVAC system, and outdoor weather station data. This is the official dataset release for the following conference paper: Jonathan Francis*, Matias Quintana*, Nadine von Frankenberg, Sirajum Munir, and Mario Bergés. 2019. OccuTherm: Occupant Thermal Comfort Inference using Body Shape Information. In BuildSys '19: ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, November 13–14, 2019, New York, NY. ACM, New York, NY, USA, 10 pages. To use this dataset, first download all the files. Next, issue the following commands on, e.g., Linux terminal: >$ cd /path/to/dataset/files >$ cat occutherm_dataset_v0-0-0.tar.gza* > archive.tar.gz >$ tar -xvzf archive.tar.gz Modeling and mobile application code are available in our project repository: https://github.com/jonfranc/occutherm If you find the repository or the dataset useful, please cite our paper: @inproceedings{francis_buildsys2019, author = {Francis, Jonathan and Quintana, Matias and von Frankenberg, Nadine and Munir, Sirajum and Berges, Mario}, title = {OccuTherm: Occupant Thermal Comfort Inference using Body Shape Information}, booktitle = {Proceedings of the 6th International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation}, series = {BuildSys '19}, year = {2019}, isbn = {978-1-4503-7005-9/19/11}, location = {New York, NY}, numpages = {10}, acmid = {3360858}, publisher = {ACM}, address = {New York, NY, USA}, keywords = {Thermal Comfort, Human Studies, Machine Learning}, }
OccuTherm: Occupant Thermal Comfort Inference using Body Shape Information
OccuTherm: Occupant Thermal Comfort Inference using Body Shape Information This repository contains the official data from a USDOE-funded project at Carnegie Mellon University and Bosch Research Pittsburgh. The primary goal of the project was to investigate the relationship between indoor commercial building occupant thermal comfort and various biometric and environmental predictors. We performed 77 individual comfort experiments, approved by our Institutional Review Board (IRB) and in satisfaction of participant consent guidelines. Our goal was to generate a dataset than enables comprehensive study of human thermal comfort preferences, in a commercial building environment, across a wide range of indoor environmental conditions. The data is comprised of the following feature groups: depth camera frames, biometrics sensor data, body shape information, subjective comfort data from the mobile device application, environmental sensor data from the commercial building HVAC system, and outdoor weather station data. This is the official dataset release for the following conference paper: Jonathan Francis*, Matias Quintana*, Nadine von Frankenberg, Sirajum Munir, and Mario Bergés. 2019. OccuTherm: Occupant Thermal Comfort Inference using Body Shape Information. In BuildSys '19: ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, November 13–14, 2019, New York, NY. ACM, New York, NY, USA, 10 pages. To use this dataset, first download all the files. Next, issue the following commands on, e.g., Linux terminal: >$ cd /path/to/dataset/files >$ cat occutherm_dataset_v0-0-0.tar.gza* > archive.tar.gz >$ tar -xvzf archive.tar.gz Modeling and mobile application code are available in our project repository: https://github.com/jonfranc/occutherm If you find the repository or the dataset useful, please cite our paper: @inproceedings{francis_buildsys2019, author = {Francis, Jonathan and Quintana, Matias and von Frankenberg, Nadine and Munir, Sirajum and Berges, Mario}, title = {OccuTherm: Occupant Thermal Comfort Inference using Body Shape Information}, booktitle = {Proceedings of the 6th International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation}, series = {BuildSys '19}, year = {2019}, isbn = {978-1-4503-7005-9/19/11}, location = {New York, NY}, numpages = {10}, acmid = {3360858}, publisher = {ACM}, address = {New York, NY, USA}, keywords = {Thermal Comfort, Human Studies, Machine Learning}, }
OccuTherm: Occupant Thermal Comfort Inference using Body Shape Information
Jonathan Francis (Autor:in) / Matias Quintana (Autor:in) / Nadine von Frankenberg (Autor:in) / Sirajum Munir (Autor:in) / Mario Bergés (Autor:in) / Michael Frenak / Charles Shelton / Nicole Ho / Alexander Davis
08.08.2019
Forschungsdaten
Elektronische Ressource
Englisch
DDC:
690
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