RADHOME:Radar based Human Activity Dataset in Home Environments

With the rapid advancements in smart home technologies, health monitoring, and safety management systems, there is a growing demand for improving quality of life and ensuring security. In this study, we present a dataset of daily human activities constructed using millimeter-wave (mmWave) radar technology, aimed at providing a robust foundation for non-contact health monitoring, safety management, and smart home systems. The dataset involves 10 participants of diverse genders and body types, performing six activity transitions in a specified sequence: sitting to walking, sitting to stretching, walking to picking up, sitting to drinking water, walking to falling, and falling to waving for help. Each activity was repeated 20 times, resulting in 1,200 activity samples. The radar system was positioned at a height of 0.8–1 meters, with a detection range of 2–5 meters, ensuring precise capture of participants' indoor movements. This dataset is made publicly available with comprehensive documentation to facilitate reproducibility and usability. It offers a valuable resource for developing advanced human activity recognition algorithms and contributes to research on behavioral patterns, with applications in smart home systems, health monitoring, and safety management.随着智能家居技术、健康监测与安全管理系统的快速发展，人们对提升生活品质与保障安全的需求日益增长。本研究构建了一套基于毫米波（millimeter-wave, mmWave）雷达技术的日常人类活动数据集，旨在为非接触式健康监测、安全管理及智能家居系统提供坚实的研究基础。该数据集包含10名性别与体型各异的受试者，按照指定顺序完成6种活动转换动作：从坐姿到行走、从坐姿到伸展肢体、从行走到拾取物品、从坐姿到饮水、从行走到跌倒，以及从跌倒到挥手求助。每项动作均重复执行20次，最终共计生成1200组活动样本。雷达系统部署高度为0.8至1米，探测范围覆盖2至5米，可精准采集受试者的室内活动轨迹。本数据集已公开上线，并附带完整的文档说明，以保障研究的可复现性与易用性。该数据集为开发先进的人类活动识别算法提供了宝贵的研究资源，同时助力行为模式相关研究，可应用于智能家居系统、健康监测及安全管理领域。