Deep learning-based optimization algorithm for health risk behavior identification and intervention in smart communities

With the continuous advancement of smart city and community construction, residents' health management has gradually become an important part of intelligent services. Health risk behaviors (e.g., sedentary, smoking, lack of exercise, etc.) are the main factors that cause chronic diseases and reduce the quality of life of residents, so it is of great significance to identify and effectively intervene these behaviors in a timely manner in a smart community. In this paper, we propose a deep learning-based health risk behavior identification and intervention optimization algorithm for smart communities, using multimodal data sources (including video surveillance, wearable devices and mobile terminal data) to construct a behavior identification model and design an efficient health intervention optimization strategy. In the identification phase, this paper integrates convolutional neural network and time series modeling methods to achieve accurate identification of complex behaviors of community residents; in the intervention phase, it combines reinforcement learning to optimize the timing and mode of intervention to improve the personalization and effectiveness of intervention. Through experimental verification in real community scenarios, the method is significantly better than traditional methods in terms of recognition accuracy and intervention response rate, showing good application prospects. This study provides a new technical support for the construction of intelligent, accurate and efficient community health management system.