A dynamic segmentation based activity discovery through topic modelling

Book chapter

Kennedy, Ihianle Isibor, Naeem, U. and Tawil, A. 2016. A dynamic segmentation based activity discovery through topic modelling. in: IET International Conference on Technologies for Active and Assisted Living (TechAAL) IEEE.
AuthorsKennedy, Ihianle Isibor, Naeem, U. and Tawil, A.
Abstract

Recent developments in ubiquitous and pervasive technologies have made it easier to capture activities through sensors. The “bag-of-word” topic models have been applied to discover latent topics in corpus of words. In this paper, we propose the Probabilistic Latent Semantic Analysis to discover activity routines. The framework we propose set latent topics as corresponding class labels and use the Expectation Maximization (EM) algorithm for posterior inference. The experimental results we present are based on the Kasteren dataset which validates our framework and shows that it is comparable to existing activity discovery approaches.

KeywordsProbabilistic Latent Semantic Analysis (PLSA); Bag-of-word; Topic Model; Activity Discovery; Sensor Segments
Book titleIET International Conference on Technologies for Active and Assisted Living (TechAAL)
Year2016
PublisherIEEE
Publication dates
Print25 Jan 2016
Publication process dates
Deposited08 Mar 2017
EventInternational Conference on Technologies for Active and Assisted Living (TechAAL)
ISBN978-1-78561-159-9
Digital Object Identifier (DOI)https://doi.org/10.1049/ic.2015.0136
Web address (URL)http://ieeexplore.ieee.org/abstract/document/7389242/
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