Human Routine Change Detection using Bayesian Modelling

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.

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