A novel lightweight skeletal temporal model for real-time, computationally efficient recognition of occupant thermal adaptation behavior

作者：Zhe Wang, Hao Sun, John Kaiser Calautit, Mingda Yang, Jianbin Chen, Rui Guan, Jo Darkwa · 发表于：Building Simulation · 年份：2025 · DOI：10.1007/s12273-025-1335-6 · 被引用次数：6 · 研究领域：Building Energy and Comfort Optimization、Color perception and design、Urban Heat Island Mitigation

Abstract Optimizing building energy systems based on real-time occupant behavior and feedback can lead to improved energy efficiency and enhanced thermal comfort in buildings. Traditional thermal comfort surveys do not provide real-time insights, while conventional sensors, such as thermal sensors, are limited in their ability to capture continuous, detailed occupancy data. Meanwhile, deep learning and computer vision have emerged as promising approaches for real-time occupancy behavior detection, but existing artificial intelligence (AI) models suffer from low frame rates and high computational demands, which can lead to increased energy consumption for processing, potentially offsetting the energy savings achieved through occupant-responsive control. Thus, this study developed a novel occupant thermal adaptation behavior recognition model that balances accuracy, real-time performance and computational resource usage to enable effective operation indoors. Using a multi-camera setup with Raspberry Pi 3B+, a custom dataset comprising 400 video samples was collected from four different angles. The dataset captures four distinct human activities: dressing, undressing, sitting, and standing. Compared to SlowFast (SF) and Spatial Temporal Graph Convolutional Networks (ST-GCN), which are widely used deep learning architectures for action recognition, the proposed novel lightweight skeletal temporal model achieved good accuracy (0.975 accuracy) on the Kungliga Tekniska Högskolan (KT...