Recognition of Activities of Daily Living via Hierarchical Long-Short Term Memory Networks

In order to offer optimal and personalized assistance services to frail people, smart homes or assistive robots must be able to understand the context and activities of users. With this outlook, we propose a vision-based approach for understanding activities of daily living (ADL) through skeleton data captured using an RGB-D camera. Upon decomposition of a skeleton sequence into short temporal segments, activities are classified via a hierarchical two-layer Long-Short Term Memory Network (LSTM) allowing to analyse the sequence at different levels of temporal granularity. The proposed approach is evaluated on a very challenging daily activity dataset wherein we attain superior performance. Our main contribution is a multi-scale, temporal dependency model of activities, founded on a comparison of context features that characterize previous recognition results and a hierarchical representation with a low-level behaviour-unit recognition layer and a high-level units chaining layer.