  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.
