Real-time machine learning-based recognition of human thermal comfort-related activities using inertial measurement unit data

Abstract

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.