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..

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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