Human Action Monitoring for Healthcare Based on Deep Learning

Abstract

Human action monitoring can be advantageous to remotely monitor the status of patients or elderly person for intelligent healthcare. Human action recognition enables efficient and accurate monitoring of human behaviors, which can exhibit multifaceted complexity attributed to disparities in viewpoints, personality, resolution and motion speed of individuals, etc. The spatial-temporal information plays an important role in the human action recognition. In this paper, we proposed a novel deep learning architecture named as recurrent 3D convolutional neural network (R3D) to extract effective and discriminative spatial-temporal features to be used for action recognition, which enables the capturing of long-range temporal information by aggregating the 3D convolutional network entries to serve as an input to the LSTM (Long Short-Term Memory) architecture. The 3D convolutional network and LSTM are two effective methods for extracting the temporal information. The proposed R3D network integrated these two methods by sharing a shared 3D convolutional network in sliding windows on video streaming to capturing short-term spatial-temporal features into the LSTM. The output features of LSTM encapsulate the long-range spatial-temporal information representing high-level abstraction of the human actions. The proposed algorithm is compared to traditional and the-state-of-the-art and deep learning algorithms. The experimental results demonstrated the effectiveness of the proposed system, which can be used as smart monitoring for remote healthcare.