Behaviour Analytics of Indoor Occupants

This project will deploy machine learning to human behaviour and building operational data obtained through Activity Based Working (ABW), aimed at providing insights towards optimising and personalising ABW. The outcome is a cloud-based integrated platform, with tailored ABW apps, for capturing and analysing occupancy behaviour and building performance data.

A rich model of workers’ movements, online activities, and social groupings will be learned from synthesising fine-grained ABW behaviours and space utilisation patterns. The web dashboard will be used by Arup to approach their work holistically, making decisions based on a clear view of the total outcomes for their clients and stakeholders, including value for money. It allows Arup to close the design feedback loop, through a deeper understanding of the impact of the engineered parameters of modern offices on ABW and building operations, enhancing the precision with which Arup prescribe solutions on multiple client contexts.

Main Participants

Flora Salim
Associate Professor
Yongli Ren
Senior Lecturer

His research interest lies in Personalisation, Recommender System, Collaborative Filtering, Web Mining, and Log Analysis.

Jonathan Liono
Senior Data Scientist, AiDA Technologies, Singapore