DOO-RE: A dataset of ambient sensors in a meeting room for activity recognition
This dataset addresses a gap for researchers in smart services and IoT by providing the first reliable dataset for single and group activities in a real meeting room, though it is incremental as it extends existing work to a new setting.
The authors tackled the lack of public ambient sensor datasets for activity recognition in meeting rooms by creating DOO-RE, a dataset with 9 activity types from various sensors, using cross-validation annotation to ensure reliability.
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.