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

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. To this end, the point cloud, provided by a dept...

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Bibliographic Details
Published in:IET International Conference on Technologies for Active and Assisted Living (TechAAL) p. 6
Main Authors: Gasparrini, S, Cippitelli, E, Gambi, E, Spinsante, S, Florez-Revuelta, F
Format: Conference Proceeding
Language:English
Published: Stevenage, UK IET 2015
The Institution of Engineering & Technology
Subjects:
Biology and medical computing
Neural computing techniques
Social and behavioural sciences computing
medical disorders
self-organising feature maps
health condition
behavioural sciences
kinect
point cloud
diet related disorders
elderly people
depth camera
self organising neural networks tracking algorithms
performance analysis
intake monitoring
ISBN:1785611593, 9781785611599
Online Access:Get full text
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Summary: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. To this end, the point cloud, provided by a depth camera placed on the ceiling in top-down view, is used as input to three self-organising algorithms. The output are three different models that represent the monitored person during intake activities. Starting from these models, the nodes representing the head and the hands are selected. They are useful to identify most of the actions performed by the person while having a meal. In the experimental section, the positions of these nodes are compared with a ground truth and the performance of the proposed algorithms are evaluated in terms of distance error.
Bibliography:ObjectType-Article-1
ObjectType-Feature-2
SourceType-Conference Papers & Proceedings-1
content type line 22
ISBN:1785611593
9781785611599
DOI:10.1049/ic.2015.0133