A stacked CNN and random forest ensemble architecture for complex nursing activity recognition and nurse identification.

Nursing activity recognition has immense importance in the development of smart healthcare management and is an extremely challenging area of research in human activity recognition. The main reasons are an extreme class-imbalance problem and intra-class variability depending on both the subject and the recipient. In this paper, we apply a unique two-step feature extraction, coupled with an intermediate feature 'Angle' and a new feature called mean min max sum to render the features robust against intra-class variation.