The real-time detection of indoor thermal comfort can bring significant benefits for energy-efficient controls over the heating, ventilation and air conditioning (HVAC) systems. At present, few studies have been conducted to propose non-intrusive and cost-effective solutions for the real-time sensing of individual thermal comfort. Leveraging advances in machine learning, this study proposes a data-driven method for real-time recognition of thermal comfort-related activities based on human inertial measurement unit (IMU) data. Using wearable devices at human wrists, experiments have been designed to collect prototype IMU data on 30 thermal comfort-related activities from building occupants. An end-to-end data analytic framework, which consists of offline training and online detection modules, has been developed for practical applications. Various feature engineering and state-of-the-art machine learning techniques have been implemented and tested to derive optimal data-driven solutions, leading to an activity recognition accuracy of 86.2%. The methods proposed provide an automated and feasible approach for thermal comfort-related activity recognition, which can be integrated with HVAC systems for smart and customized controls, such as personalized ventilation and air-conditioning.

室内热舒适度的实时检测可以为供暖、通风和空调 (HVAC) 系统的节能控制带来显着好处。目前，很少有研究提出用于实时感知个人热舒适度的非侵入式且具有成本效益的解决方案。利用机器学习的进步，本研究提出了一种基于人体惯性测量单元 (IMU) 数据实时识别热舒适相关活动的数据驱动方法。使用可穿戴设备在人类的手腕上，设计了实验来收集原型 IMU 数据，这些数据来自建筑物居住者的 30 项与热舒适相关的活动。已经为实际应用开发了一个端到端的数据分析框架，它由离线训练和在线检测模块组成。已经实施和测试了各种特征工程和最先进的机器学习技术，以得出最佳的数据驱动解决方案，从而使活动识别准确度达到 86.2%。所提出的方法为与热舒适相关的活动识别提供了一种自动化且可行的方法，该方法可以与 HVAC 系统集成以实现智能和定制控制，例如个性化通风和空调。