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

Maxime Devanne 1, 2 Panagiotis Papadakis 1, 2 Sao Mai Nguyen 1, 2
1 Lab-STICC_IMTA_CID_IHSEV
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : 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.
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Maxime Devanne, Panagiotis Papadakis, Sao Mai Nguyen. Recognition of Activities of Daily Living via Hierarchical Long-Short Term Memory Networks. IEEE International Conference on Systems, Man, and Cybernetics (SMC), Oct 2019, Bari, Italy. ⟨hal-02194928⟩

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