<b>DOO-RE: A dataset of ambient sensors in a meeting room for activity recognition</b>

We release the DOO-RE dataset which consists of data streams from 11 types of various ambient sensors by collecting data 24/7 from a real-world meeting room. 4 types of ambient sensors, called environment-driven sensors, measure continuous state changes in the environment (e.g. sound), and 4 types of sensors, called user-driven sensors, capture user state changes (e.g. motion). The remaining 3 types of sensors, called actuator-driven sensors, check whether the attached actuators are active (e.g. projector on/off). The values of each sensor are automatically collected by IoT agents which are responsible for each sensor in our IoT system. A part of the collected sensor data stream representing a user activity is extracted as an activity episode in the DOO-RE dataset. Each episode's activity labels are annotated and validated by cross-checking and the consent of multiple annotators. A total of 9 activity types appear in the space: 3 based on single users and 6 based on group (i.e. 2 or more people) users. As a result, DOO-RE is constructed with 696 labeled episodes for single and group activities from the meeting room. DOO-RE is a novel dataset created in a public space that contains the properties of the real-world environment and has the potential to be good uses for developing powerful activity recognition approaches.本研究公开发布DOO-RE数据集，该数据集通过在真实会议室中全天候（24/7）采集数据，由11类不同环境传感器的数据流构成。其中4类被称为环境驱动传感器（environment-driven sensors），用于监测环境的连续状态变化（如声音）；另有4类被称为用户驱动传感器（user-driven sensors），用于捕捉用户的状态变化（如肢体动作）；剩余3类被称为执行器驱动传感器（actuator-driven sensors），用于检测其所连接的执行器是否处于激活状态（如投影仪的开关机状态）。本物联网（Internet of Things）系统中，各传感器均由负责对应设备的物联网智能体（IoT agents）自动采集数值。DOO-RE数据集中，从采集得到的表征用户活动的传感器数据流中，提取出部分片段作为活动时段（activity episode）。每个活动时段的活动标签均经多名标注人员交叉核对并共同确认后，完成标注与验证工作。该数据集共涵盖9类活动类型：3类面向单人场景，6类面向群体（即2人及以上）场景。最终，DOO-RE数据集共包含来自该会议室的696条标注有活动类型的单人及群体活动时段数据。作为一款在公共空间中构建的新型数据集，DOO-RE蕴含真实环境的属性特征，有望在开发高性能活动识别方法的任务中展现出色的应用潜力。