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: Contribution to conference › Paper › 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
DOIs	https://doi.org/10.1049/ic.2015.0133
Publication status	Published - 2015
Externally published	Yes
Event	IET International Conference on Technologies for Active and Assisted Living (TechAAL 2015) - Kingston upon Thames, U.K. Duration: 5 Nov 2015 → 5 Nov 2015

Conference

Conference	IET International Conference on Technologies for Active and Assisted Living (TechAAL 2015)
Period	5/11/15 → 5/11/15

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.

Keywords

Computer science and informatics

Access to Document

10.1049/ic.2015.0133

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

1 Conference contribution

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

Cite this

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title = "Performance analysis of self-organising neural networks tracking algorithms for intake monitoring using kinect",

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.",

keywords = "Computer science and informatics",

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

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.; IET International Conference on Technologies for Active and Assisted Living (TechAAL 2015) ; Conference date: 05-11-2015 Through 05-11-2015",

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AU - Gasparrini, Samuele

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.

PY - 2015

Y1 - 2015

N2 - 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.

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

U2 - 10.1049/ic.2015.0133

DO - 10.1049/ic.2015.0133

M3 - Paper

T2 - IET International Conference on Technologies for Active and Assisted Living (TechAAL 2015)

Y2 - 5 November 2015 through 5 November 2015

ER -

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

Abstract

Conference

Bibliographical note

Keywords

Access to Document

Fingerprint

Research output

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

Cite this