Performance analysis of self-organising neural networks tracking algorithms for intake monitoring using kinect

Gasparrini, Samuele, Cippitelli, Enea, Gambi, Ennio, Spinsante, Susanna and Florez Revuelta, Francisco (2015) Performance analysis of self-organising neural networks tracking algorithms for intake monitoring using kinect. In: IET International Conference on Technologies for Active and Assisted Living (TechAAL 2015); 05 Nov 2015, Kingston upon Thames, U.K..

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Abstract

The analysis of intake behaviour is a key factor to understand the health condition of a subject, such as elderly or people affected by diet-related disorders. The technology can be exploited for this purpose to promptly identify anomalous situations. This paper presents a comparison between three unsupervised machine learning algorithms used to track the movements performed by a person during an intake action and provides experimental results showing the best performing algorithm among those compared.

Item Type: Conference or Workshop Item (Paper)
Event Title: IET International Conference on Technologies for Active and Assisted Living (TechAAL 2015)
Organising Body: Institution of Engineering and Technology (IET), Kingston University London.
Additional Information: Published as: Gasparrini, Samuele, Cippitelli, Enea, Gambi, Ennio, Spinsante, Susanna and Florez Revuelta, Francisco (2015) Performance analysis of self-organising neural networks tracking algorithms for intake monitoring using kinect. In: Proceedings of IET International Conference on Technologies for Active and Assisted Living (TechAAL). IEEE. ISBN 9781785611599
Research Area: Computer science and informatics
Faculty, School or Research Centre: Faculty of Science, Engineering and Computing (until 2017)
Depositing User: Francisco Florez Revuelta
Date Deposited: 26 May 2017 10:07
Last Modified: 26 May 2017 10:22
DOI: https://doi.org/10.1049/ic.2015.0133
URI: http://eprints.kingston.ac.uk/id/eprint/33767

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