DEEP LEARNING MODELS FOR HUMAN ACTIVITY RECOGNITION IN SMART HOMES USING ARAS DATASET
Keywords:
Human Activity Recognition, Deep Learning Models, Activity Recognition, Internet Of Things, Smart HomesAbstract
Human Activity Recognition (HAR) is essential in recognizing and classifying human actions performed at home through internet of things (IoT) devices and artificial intelligence (AI) technologies. The IoT smart devices such as sensors together with AI techniques like deep learning models are used to identify activities performed by individuals, such activities include; sleeping, watching TV, walking and more. The identification of human behaviour changes helps for healthcare, security control and more. The goal of this research is to create models that can more accurately forecast the activities that occupants of smart homes will engage in deep learning models like Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN). The experimental results demonstrated that ANN outperformed with an excellent accuracy of 99.4% and 99.8% in households A and B respective, compared to CNN and RNN in identifying human behavior in smart residence using the ARAS dataset.
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Copyright (c) 2024 Manjaiah D. Huchaiah, John W. Kasubi, Geofrey Anganile Mwakifwamba (Author)
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