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id caadria2020_444
authors Higgs, Baptiste and Doherty, Ben
year 2020
title Sanitary Sanity: Evaluating Privacy Preserving Machine Learning Methods for Post-occupancy Evaluation
source D. Holzer, W. Nakapan, A. Globa, I. Koh (eds.), RE: Anthropocene, Design in the Age of Humans - Proceedings of the 25th CAADRIA Conference - Volume 2, Chulalongkorn University, Bangkok, Thailand, 5-6 August 2020, pp. 697-706
summary Traditional post-occupancy evaluation (POE) of building performance has typically privileged physical building attributes over human behavioural data. This is due to a lack of capability and is especially the case for private spaces such as Sanitary Facilities (SFs). A privacy-preserving sensor-based system using Machine Learning (ML) was previously developed, however it was limited to basic body position classification. Yet, SF usage behaviour can be significantly more complex. This research accordingly builds on the aforementioned work to expand behavioural classifications using a sensor-based ML system. Specifically, the case study uses a GridEYE thermal sensor array, which is trained on a cubicle location within a workplace SF. A variety of ML algorithms are then evaluated on their behaviour-classifying ability. A detailed analysis of behaviour-classification performance is then provided. A system with greater fidelity is thus demonstrated, albeit hampered by imprecise behaviour definitions. Regardless, this contributes to the capability of the broader field of research that is investigating Evidence Based Design (EBD) by extending the ability to examine human behaviour, especially in private spaces. This further contributes to the growing body of work surrounding SF provision.
keywords EBD; Data; Internet of Things; Machine Learning; Post Occupancy Evaluation
series CAADRIA
email BaptisteHiggs@gmail.com
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