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Temporal learning using echo state network for human activity recognition

Basterrech, S. and Ojha, V. ORCID: (2016) Temporal learning using echo state network for human activity recognition. In: 2016 Third European Network Intelligence Conference (ENIC), 5-7 Sep 2016, Wrocław, Poland, pp. 217-223,

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To link to this item DOI: 10.1109/ENIC.2016.039


Several works have been applied to non-temporal classification techniques in the Human Activity Recognition area. Instead of that, we present an approach for modelling human activities using a temporal learning tool. Here, the activities are considered as time-dependent events, and we use a temporal learning method for their classification. We employ a well-known learning tool named Echo State Network (ESN). An ESN is a specific type of Recurrent Neural Networks, which has proven well performances for solving benchmark problems with sequential and time-series data. Another advantage is that the method is very robust and fast during the learning algorithm. Therefore, it is a good tool for being applied in real-time contexts. We apply the proposed approach for analyzing a well-know benchmark dataset, and we obtain promising results.

Item Type:Conference or Workshop Item (Paper)
Divisions:Interdisciplinary Research Centres (IDRCs) > Centre for the Mathematics of Planet Earth (CMPE)
Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:93554


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