With the advancement of IoT technology, recognizing user activities with machine learning methods is a promising way to provide various smart services to users. High-quality data with privacy protection is essential for deploying such services in the real world. Data streams from surrounding ambient sensors are well suited to the requirement. Existing ambient sensor datasets only support constrained private spaces and those for public spaces have yet to be explored despite growing interest in research on them. To meet this need, we build a dataset collected from a meeting room equipped with ambient sensors. The dataset, DOO-RE, includes data streams from various ambient sensor types such as Sound and Projector. Each sensor data stream is segmented into activity units and multiple annotators provide activity labels through a cross-validation annotation process to improve annotation quality. We finally obtain 9 types of activities. To our best knowledge, DOO-RE is the first dataset to support the recognition of both single and group activities in a real meeting room with reliable annotations.

利用机器学习方法识别用户活动是通过IoT技术为用户提供智能服务的一种有前景的方式。在真实世界中部署这样的服务需要具备高质量的数据和隐私保护。目前，环境传感器采集的数据流很适合此需求。虽然对于私密空间的数据集已有支持，但公共空间的数据集尚未得到深入研究。为了满足这一需求，我们构建了一个从配备环境传感器的会议室收集的数据集，名为DOO-RE。该数据集包括来自各种环境传感器类型（如声音和投影仪）的数据流。每个传感器数据流被分段为活动单元，并通过交叉验证的注释过程由多个注释员提供活动标签，以提高注释质量。最终，我们得到了9种类型的活动。据我们所知，DOO-RE是第一个支持在真实会议室中识别单个和群体活动，并提供可靠注释的数据集。

DOO-RE：会议室环境传感器活动识别数据集