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

Samuele Gasparrini; Enea Cippitelli; Ennio Gambi; Susanna Spinsante; Francisco Florez Revuelta

doi:10.1049/ic.2015.0133

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

Samuele Gasparrini
, Enea Cippitelli
, Ennio Gambi
, Susanna Spinsante
, Francisco Florez Revuelta

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	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 Organising Body: Institution of Engineering and Technology (IET), Kingston University London. Organising Body: Institution of Engineering and Technology (IET), Kingston University London.
DOIs	https://doi.org/10.1049/ic.2015.0133
Publication status	Published - 2015
Externally published	Yes

Bibliographical note

Note: 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

Organising Body: Institution of Engineering and Technology (IET), Kingston University London.

Organising Body: Institution of Engineering and Technology (IET), Kingston University London.

Keywords

Computer science and informatics

Access to Document

10.1049/ic.2015.0133

http://dx.doi.org/10.1049/ic.2015.0133

1 Paper

Performance analysis of self-organising neural networks tracking algorithms for intake monitoring using kinect
Gasparrini, S., Cippitelli, E., Gambi, E., Spinsante, S. & Florez Revuelta, F., 2015.
Research output: Contribution to conference › Paper › peer-review

Cite this

Gasparrini, S., Cippitelli, E., Gambi, E., Spinsante, S., & Florez Revuelta, F. (2015). Performance analysis of self-organising neural networks tracking algorithms for intake monitoring using kinect. In 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 Organising Body: Institution of Engineering and Technology (IET), Kingston University London. Organising Body: Institution of Engineering and Technology (IET), Kingston University London. https://doi.org/10.1049/ic.2015.0133

Gasparrini, Samuele ; Cippitelli, Enea ; Gambi, Ennio et al. / Performance analysis of self-organising neural networks tracking algorithms for intake monitoring using kinect. 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 Organising Body: Institution of Engineering and Technology (IET), Kingston University London. Organising Body: Institution of Engineering and Technology (IET), Kingston University London.. 2015.

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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.",

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author = "Samuele Gasparrini and Enea Cippitelli and Ennio Gambi and Susanna Spinsante and \{Florez Revuelta\}, Francisco",

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booktitle = "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 Organising Body: Institution of Engineering and Technology (IET), Kingston University London. Organising Body: Institution of Engineering and Technology (IET), Kingston University London.",

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Gasparrini, S, Cippitelli, E, Gambi, E, Spinsante, S & Florez Revuelta, F 2015, Performance analysis of self-organising neural networks tracking algorithms for intake monitoring using kinect. in 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 Organising Body: Institution of Engineering and Technology (IET), Kingston University London. Organising Body: Institution of Engineering and Technology (IET), Kingston University London.. https://doi.org/10.1049/ic.2015.0133

Performance analysis of self-organising neural networks tracking algorithms for intake monitoring using kinect. / Gasparrini, Samuele; Cippitelli, Enea; Gambi, Ennio et al.
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 Organising Body: Institution of Engineering and Technology (IET), Kingston University London. Organising Body: Institution of Engineering and Technology (IET), Kingston University London.. 2015.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Cippitelli, Enea

AU - Gambi, Ennio

AU - Spinsante, Susanna

AU - Florez Revuelta, Francisco

N1 - Note: 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 Organising Body: Institution of Engineering and Technology (IET), Kingston University London. Organising Body: Institution of Engineering and Technology (IET), Kingston University London.

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AB - 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.

KW - Computer science and informatics

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M3 - Conference contribution

BT - 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 Organising Body: Institution of Engineering and Technology (IET), Kingston University London. Organising Body: Institution of Engineering and Technology (IET), Kingston University London.

ER -

Gasparrini S, Cippitelli E, Gambi E, Spinsante S, Florez Revuelta F. Performance analysis of self-organising neural networks tracking algorithms for intake monitoring using kinect. In 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 Organising Body: Institution of Engineering and Technology (IET), Kingston University London. Organising Body: Institution of Engineering and Technology (IET), Kingston University London.. 2015 doi: 10.1049/ic.2015.0133

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

Abstract

Bibliographical note

Keywords

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Research output

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

Cite this