IoT-Inspired Monitoring Framework for Real-Time Stereotypic Movement Analysis

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Physical activity recognition has become one of the prominent domain of research in the field of smart healthcare. The identification of physical activities through wearable sensors provide significant information about the functional ability and lifestyle of an individual. In this article, a smart physical activity-assisted behavior prediction framework is presented to recognize different stereotypical motor movements of the individual suffering from autism spectrum disorder in real time. This article employs the data acquisition efficiency of Internet of Things (IoT) assisted smart wearable sensors to capture the physical activities of an individual. To predict the physical activities, a deep learning-inspired multilayered convolutional neural network (CNN) + long short-term memory network is proposed. Furthermore, the utility of the framework is increased by presenting an effective fog-assisted alert-based decision-making module. The decision-making module notifies the concerned caretaker or medical specialist about the performed irregular physical activity in real time. Moreover, the process of cloud-based record generation also enhances the significance of the proposed solution. The proposed approach is validated by comparing the prediction performance with traditional handcraft-based and modern deep learning-based state-of-the-art approaches such as <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$k$</tex></formula> -nearest neighbors, hidden Markov model, and CNN, where it outperformed by achieving the overall accuracy of 91.88%.