Eating and drinking activity recognition based on discriminant analysis of fuzzy distances and activity volumes
Eating and drinking activity recognition can be considered a solitary research field in activity recognition area. The development of an application capable to identify human eating and drinking activity can be really useful in a smart home environment targeting to extend independent living of older persons in the early stages of dementia. In this paper a novel method aiming at eating and drinking activity recognition is presented. Activities are considered as a sequence of human body poses forming 3D volumes, in which the third dimension refers to time. Fuzzy Vector Quantization is performed to associate the 3D volume representation of an activity video with 3D volume prototypes and Linear Discriminant Analysis is used to map activity representations in a low dimensional discriminant feature space. In this space a simple Nearest Centroid classification procedure leads to very satisfactory classification results.