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Title	Unveiling Human Activity Patterns In Smart Cities Through A Cnn-Lstm Approach
ID_Doc	59770
Authors	Ameur I.; Ameur M.E.A.; Ameur M.; Dagha H.E.
Year	2025
Published	Lecture Notes in Networks and Systems, 1267 LNNS
DOI	http://dx.doi.org/10.1007/978-3-031-82112-7_4
Abstract	Over the past two decades, significant technological advancements have taken place, giving rise to novel applications of intelligent digital sensors. The proliferation of Internet of Things (IoT) services in various settings such as smart homes, buildings, cities, factories, and other intelligent environments has brought substantial benefits to individuals, industries, and public institutions. A typical smart home has undergone enhancements and is equipped with an array of sensors and actuators to provide services to its residents. One of the key aspects in many smart home applications is the identification of residents’ daily routines. Automating the Human Activity Recognition system based on human behavior patterns is a complex task due to the intricate nature of human life within a home environment, where multiple inhabitants and residents coexist. To address this complexity, various deep learning algorithms, known for their effectiveness in diverse domains, have been explored to enhance the accuracy of unveiling the human activity pattern. This research aims to investigate and develop a Human Activity Recognition system for smart cities through the implementation of a hybrid deep learning approach known as CNN-LSTM. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Author Keywords	Deep learning; HAR; Smart cities; Smart home

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