Recognition of Activities of Daily Living for Smart Home Environments

Abstract

The recognition of Activities of Daily Living (ADL) from video can prove particularly useful in assisted living and smart home environments, as behavioral and lifestyle profiles can be constructed through the recognition of ADLs over time. Often, existing methods for recognition of ADLs have a very high computational cost, which makes them unsuitable for real time or near real time applications. In this work we present a novel method for recognizing ADLs with accuracy comparable to the state of the art, at a lowered computational cost. Comprehensive testing of the best existing descriptors, encoding methods and BoW/SVM based classification methods takes place to determine the optimal recognition solution. A statistical method for determining the temporal duration of extracted trajectories is also introduced, to streamline the recognition process and make it less ad-hoc. Experiments take place with benchmark ADL datasets and a newly introduced set of ADL recordings of elderly people with dementia as well as healthy individuals. Our algorithm leads to accurate recognition rates, comparable or better than the State of the Art, at a lower computational cost.