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 (IET International Conference on Technologies for Active and Assisted Living (TechAAL), 2015)
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.