Activity Recognition and Abnormal Behaviour Detection with Recurrent Neural Networks.

Arifoglu, D. and Bouchachia, A., 2017. Activity Recognition and Abnormal Behaviour Detection with Recurrent Neural Networks. In: MOBISPC 2017: 14th International Conference on Mobile Systems and Pervasive Computing:, 24-26 July 2017, Leuven, Belgium, 86 - 93.

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DOI: 10.1016/j.procs.2017.06.121

Abstract

© 2017 The Authors. Published by Elsevier B.V. In this paper, we study the problem of activity recognition and abnormal behaviour detection for elderly people with dementia. Very few studies have attempted to address this problem presumably because of the lack of experimental data in the context of dementia care. In particular, the paper investigates three variants of Recurrent Neural Networks (RNNs): Vanilla RNNs (VRNN), Long Short Term RNNs (LSTM) and Gated Recurrent Unit RNNs (GRU). Here activity recognition is considered as a sequence labelling problem, while abnormal behaviour is flagged based on the deviation from normal patterns. To provide an adequate discussion of the performance of RNNs in this context, we compare them against the state-of-art methods such as Support Vector Machines (SVMs), NäIve Bayes (NB), Hidden Markov Models (HMMs), Hidden Semi-Markov Models (HSMM) and Conditional Random Fields (CRFs). The results obtained indicate that RNNs are competitive with those state-of-art methods. Moreover, the paper presents a methodology for generating synthetic data reflecting on some behaviours of people with dementia given the difficulty of obtaining real-world data.

Item Type:Conference or Workshop Item (Paper)
Group:Faculty of Science & Technology
ID Code:29720
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:20 Sep 2017 13:31
Last Modified:20 Sep 2017 13:31

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