Data Fusion Application in Predicting Human Comfort
| Autor: | Fadi Alsaleem, Andrew Holthaus, Mostafa Rafaie |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Human comfort
Computer science business.industry 020209 energy media_common.quotation_subject Wearable computer Thermal comfort 02 engineering and technology Machine learning computer.software_genre Sensor fusion Feature (computer vision) Voting 0202 electrical engineering electronic engineering information engineering Biometric data Artificial intelligence business computer media_common |
| Zdroj: | Structural Health Monitoring 2017. |
| DOI: | 10.12783/shm2017/14170 |
| Popis: | This paper studies the use of wearable device data along with other parameters such ambient temperature to model human comfort. Several machine-learning methods were used to build thermal comfort models from five individual’s wearable biometric data and their surrounding ambient conditions. The effects of the machine learning and input feature type and the output class size on the model accuracy were investigated. It is the goal to determine exactly what combinations of these factors will be able to accurately predict human thermal comfort. The accuracy of these models was determined by comparing their prediction to the individual’s actual thermal comfort found using voting input |
| Databáze: | OpenAIRE |
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