A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Physiological responses and data-driven thermal comfort models with personal conditioning devices (PCD)
https://orcid.org/0000-0001-5657-4538
https://orcid.org/0000-0002-7870-6308
https://orcid.org/0000-0002-0202-5514
https://orcid.org/0000-0001-6825-5091
Abstract Thermal comfort in uniform environments created by HVAC systems have been investigated for decades. However, the case in nonuniform microenvironments created by the personal conditioning device (PCD) is different because of the local thermal stimuli on occupants. This study used PCD to provide localized cooling for users based on their individual thermal preferences. We collected PCD users' wrist temperatures and heart rate variabilities (HRV), as well as thermal sensation/comfort questionnaires. Because of the nonuniform thermal stimuli, the physiological responses triggered by the PCD show unclear patterns, which are incompatible with traditional thermal comfort models developed for the uniform environment. Therefore, we utilized machine learning methods to model PCD users’ thermal comfort. By using SVM with RBF kernel based on multiple HRV indices in addition to wrist temperatures, the F1 score is improved by more than four times when compared to the model only based on wrist temperatures. This indicates the importance of HRV as physiological indices for the thermal comfort with nonuniform thermal stimuli. The best performances of our models are higher than 0.90 for both thermal sensation and comfort, which can provide a reliable solution in predicting the thermal sensation and comfort in nonuniform environments with the PCD.
Highlights We considered HRV for comforts in nonuniform microenvironments created by PCD. The PCD created physiological responses incompatible with traditional comfort models. SVM with RBF kernel achieved the best performance among machine learning methods. Including multiple HRVs in addition to wrist temperatures improved the model performance. The highest model performance indices exceeded 0.9 for both thermal sensation and comfort.
Physiological responses and data-driven thermal comfort models with personal conditioning devices (PCD)
https://orcid.org/0000-0001-5657-4538
https://orcid.org/0000-0002-7870-6308
https://orcid.org/0000-0002-0202-5514
https://orcid.org/0000-0001-6825-5091
Abstract Thermal comfort in uniform environments created by HVAC systems have been investigated for decades. However, the case in nonuniform microenvironments created by the personal conditioning device (PCD) is different because of the local thermal stimuli on occupants. This study used PCD to provide localized cooling for users based on their individual thermal preferences. We collected PCD users' wrist temperatures and heart rate variabilities (HRV), as well as thermal sensation/comfort questionnaires. Because of the nonuniform thermal stimuli, the physiological responses triggered by the PCD show unclear patterns, which are incompatible with traditional thermal comfort models developed for the uniform environment. Therefore, we utilized machine learning methods to model PCD users’ thermal comfort. By using SVM with RBF kernel based on multiple HRV indices in addition to wrist temperatures, the F1 score is improved by more than four times when compared to the model only based on wrist temperatures. This indicates the importance of HRV as physiological indices for the thermal comfort with nonuniform thermal stimuli. The best performances of our models are higher than 0.90 for both thermal sensation and comfort, which can provide a reliable solution in predicting the thermal sensation and comfort in nonuniform environments with the PCD.
Highlights We considered HRV for comforts in nonuniform microenvironments created by PCD. The PCD created physiological responses incompatible with traditional comfort models. SVM with RBF kernel achieved the best performance among machine learning methods. Including multiple HRVs in addition to wrist temperatures improved the model performance. The highest model performance indices exceeded 0.9 for both thermal sensation and comfort.
Physiological responses and data-driven thermal comfort models with personal conditioning devices (PCD)
https://orcid.org/0000-0001-5657-4538
https://orcid.org/0000-0002-7870-6308
https://orcid.org/0000-0002-0202-5514
https://orcid.org/0000-0001-6825-5091
Abstract Thermal comfort in uniform environments created by HVAC systems have been investigated for decades. However, the case in nonuniform microenvironments created by the personal conditioning device (PCD) is different because of the local thermal stimuli on occupants. This study used PCD to provide localized cooling for users based on their individual thermal preferences. We collected PCD users' wrist temperatures and heart rate variabilities (HRV), as well as thermal sensation/comfort questionnaires. Because of the nonuniform thermal stimuli, the physiological responses triggered by the PCD show unclear patterns, which are incompatible with traditional thermal comfort models developed for the uniform environment. Therefore, we utilized machine learning methods to model PCD users’ thermal comfort. By using SVM with RBF kernel based on multiple HRV indices in addition to wrist temperatures, the F1 score is improved by more than four times when compared to the model only based on wrist temperatures. This indicates the importance of HRV as physiological indices for the thermal comfort with nonuniform thermal stimuli. The best performances of our models are higher than 0.90 for both thermal sensation and comfort, which can provide a reliable solution in predicting the thermal sensation and comfort in nonuniform environments with the PCD.
Highlights We considered HRV for comforts in nonuniform microenvironments created by PCD. The PCD created physiological responses incompatible with traditional comfort models. SVM with RBF kernel achieved the best performance among machine learning methods. Including multiple HRVs in addition to wrist temperatures improved the model performance. The highest model performance indices exceeded 0.9 for both thermal sensation and comfort.
Physiological responses and data-driven thermal comfort models with personal conditioning devices (PCD)
Wang, Lingzhe (author) / Dalgo, Daniel A. (author) / Mattise, Nicholas (author) / Zhu, Shengwei (author) / Srebric, Jelena (author)
Building and Environment ; 236
2023-04-06
Article (Journal)
Electronic Resource
English
| Taylor & Francis Verlag | 2021
Personal thermal comfort models with wearable sensors
| Elsevier | 2019