The Human Motion Behavior Recognition by Deep Learning Approach and the Internet of Things. - Li, Hui; Liu, Huayang; Zhao, Wei; Liu, Hao (International Journal of Interactive Multimedia and Artificial Intelligence, 2024)
This paper is dedicated to exploring the practical implementation of deep learning and Internet of Things (IoT) technology within systems designed for recognizing human motion behavior. It places a particular emphasis on evaluating performance in complex environments, aiming to mitigate challenges such as poor robustness and high computational workload encountered in conventional human motion behavior recognition approaches by employing Convolutional Neural Networks (CNN). The primary focus is on enhancing the performance of human motion behavior recognition systems for real-world scenarios, optimizing them for real-time accuracy, and enhancing their suitability for practical applications. Specifically, the paper investigates human motion behavior recognition using CNN, where the parameters of the CNN model are fine-tuned to improve recognition performance. The paper commences by delineating the process and methodology employed for human motion recognition, followed by an in-depth exploration of the CNN model's application in recognizing human motion behavior. To acquire data depicting human motion behavior in authentic settings, the Internet of Things (IoT) is utilized for extracting relevant information from the living environment. The dataset chosen for human motion behavior recognition is the Royal Institute of Technology (KTH) database. The analysis demonstrates that the network training loss function reaches a minimum value of 0.0001. Leveraging the trained CNN model, the recognition accuracy for human motion behavior achieves peak performance, registering an average accuracy of 94.41%. Notably, the recognition accuracy for static motion behavior generally exceeds that for dynamic motion behavior across different models. The CNN-based human motion behavior recognition method exhibits promising results in both static and dynamic behavior recognition scenarios. Furthermore, the paper advocates for the use of IoT in collecting human motion behavior data in real-world living environments, contributing to the advancement of human motion behavior recognition technology and its application in diverse domains such as intelligent surveillance and health management. The research findings carry significant implications for furthering the development of human motion behavior recognition technology and enhancing its applications in areas such as intelligent surveillance and health management.