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.

翻译：随着物联网技术的发展，利用机器学习方法识别用户活动已成为向用户提供多种智能服务的重要途径。具备隐私保护的高质量数据对于在现实世界中部署此类服务至关重要，而来自周边环境传感器的数据流恰好符合这一需求。现有环境传感器数据集仅支持受限的私密空间，尽管针对公共空间的研究兴趣日益增长，但相关数据集尚未得到探索。为满足这一需求，我们构建了一个从配备环境传感器的会议室中采集的数据集。该数据集DOO-RE包含来自多种环境传感器类型（如声音传感器和投影仪）的数据流。每个传感器数据流被分割为活动单元，并通过交叉验证标注流程由多名标注员提供活动标签，以提升标注质量。最终我们获得了9类活动。据我们所知，DOO-RE是首个支持真实会议室中单人及群体活动识别且具有可靠标注的数据集。