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Nurse care activity recognition: a GRU-based approach with attention mechanism

Human activity recognition is a challenging task due to complexity and variations of human movements while performing activities by different subjects. Extracting features to model the temporal evolution of different movements plays an important role in this task. In this paper, we present the approach followed by our team, Dark_Shadow, to recognize complex nurse activities in the "Nurse Care Activity Recognition Challenge" [1]. We present a deep learning method to capture the movements of essential body parts from time series of human activity data collected by sensors and then classify them. Deep learning approaches have provided satisfactory results in various human activity recognition tasks. In this work, we propose a Gated Recurrent Unit (GRU) model with attention mechanism to recognize the nurse activities. We obtain approximately 66.43% accuracy for person-wise one leave out cross validation.