Human Routine Change Detection using Bayesian Modelling
Yangdi Xu, Dima Damen · 2018
Automatic discovery of changes in a human's routine is one of the requirements for the future of smart home living, and its contribution to the E-health of the community. In this paper, a Bayesian modelling approach is used which models routine change discovery as a pairwise model selection problem. The method is evaluated on a collected office kitchen dataset that captures snapshots of the routine of the same person over multiple years (2014-2017). The results show that our method is able to detect not only the presence of routine changes, but also which activity patterns have been changed, fully automatically, and in a fully unsupervised manner. Moreover, changes within the same activity pattern can be discovered. Interestingly, discovered changes demonstrate subtle variations that are missed by the visual inspection of a human observer.