An IMU dataset for human thermal comfort activities identification: Experimental designs and applications

• Published in: Energy and built environment Vol. 6; no. 1; pp. 66 - 79
• Main Authors: He, Weilin, Fan, Cheng, Wu, Zebin, Yong, Qiaoqiao
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2025; KeAi Communications Co., Ltd
• Subjects: Human activity recognition; IMU dataset; Occupant behavior; Thermal comfort; Thermal comfort; Occupant behavior; IMU dataset; Human activity recognition
• ISSN: 2666-1233; 2666-1233
• DOI: 10.1016/j.enbenv.2023.09.001

•An IMU dataset has been prepared for human thermal comfort-related studies.•340 questionnaires have been collected for defining thermal comfort activities.•Experiments have been conducted to collect 4500 IMU samples from 30 participants.•Data-driven models can be developed for real-time human activity identification. Thermal comfort of occupants is key feedback information for improving indoor environment and managing building energy use. Through analyzing inertial measurement units (IMU) data from wearable devices with machine learning, thermal comfort of occupants can be detected in a non- intrusive method. This paper proposed a dataset consisted of IMU data collected from 30 participants (14 males and 16 females, aged 23.23 ± 1.70 years, height 168.67 ± 8.02 cm, and weight 59.55 ± 10.96 kg) who wore two IMUs on their hands while performing 30 thermal comfort activities (10 cold-related, 10 hot-related, and 10 neutral activities) according to their personal habits. The database is divided into two parts: (1) Single activities data, which includes 4500 samples acquired from experiments where each participant was asked to perform 30 thermal comfort activities individually. (2) Continuous multi-activity data, which comprise 360 samples collected while participants performed a series of randomly assigned activities in a more natural and continuous manner. The combination of these two parts provides a comprehensive dataset for both the training and testing phases of machine learning models. By offering detailed labels, this database aims to serve as a foundation for research exploring machine learning applications in detecting occupant thermal comfort, ultimately contributing to improved indoor environments and more efficient building energy management. [Display omitted]