Vision-based approach to assess performance levels while eating

The elderly population is increasing at a rapid rate, and the need for effectively supporting independent living has become crucial. Wearable sensors can be helpful, but these are intrusive as they require adherence by the elderly. Thus, a semi-anonymous (no image records) vision-based non-intrusive monitoring system might potentially be the answer. As everyone has to eat, we introduce a first investigation into how eating behavior might be used as an indicator of performance changes. This study aims to provide a comprehensive model of the eating behavior of individuals. This includes creating a visual representation of the different actions involved in the eating process, in the form of a state diagram, as well as measuring the level of performance or decay over time during eating. Also, in studies that involve humans, getting a generalized model across numerous human subjects is challenging, as indicative features that parametrize decay/performance changes vary significantly from person to person. We present a two-step approach to get a generalized model using distinctive micro-movements, i.e., (1) get the best features across all subjects (all features are extracted from 3D poses of subjects) and (2) use an uncertainty-aware regression model to tackle the problem. Moreover, we also present an extended version of EatSense, a dataset that explores eating behavior and quality of motion assessment while eating.