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. After intermediate and final feature extraction, we use an ensemble of a random forest classifier and a stacked convolutional neural network (S-CNN) model to detect activities and users. Unlike traditional CNN, the S-CNN takes the input feature channels in separate pathways with equal importance, which makes it robust to intra-class variation and produces accurate results. We apply this method to two benchmark open-source nurse care activity data sets. Our algorithm is robust enough to recognize both activity and user (Nurse) simultaneously. During the recognition process, this algorithm automatically finds the important features in the data set. Using this algorithm, the highest testing accuracies were achieved for activity recognition on the two (publicly available in IEEE DataPort) benchmark data sets: The CARECOM Nurse Care Activity (70.6% accuracy) and the Heiseikai Nurse Care Activity data set (85.7% accuracy). Moreover, the highest accuracy achieved for user identification on Data Set 1 and Data Set 2 is 78.2% and 92.7%, respectively.