A dynamic segmentation based activity discovery through topic modelling
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.
|Authors||Kennedy, Ihianle Isibor, Naeem, U. and Tawil, A.|
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.
|Keywords||Probabilistic Latent Semantic Analysis (PLSA); Bag-of-word; Topic Model; Activity Discovery; Sensor Segments|
|Book title||IET International Conference on Technologies for Active and Assisted Living (TechAAL)|
|25 Jan 2016|
|Publication process dates|
|Deposited||08 Mar 2017|
|Event||International Conference on Technologies for Active and Assisted Living (TechAAL)|
|Digital Object Identifier (DOI)||https://doi.org/10.1049/ic.2015.0136|
|Web address (URL)||http://ieeexplore.ieee.org/abstract/document/7389242/|
© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
|Accepted author manuscript|
5views this month
3downloads this month