DOO-RE：一个用于活动识别的会议室环境传感器数据集
DOO-RE: A dataset of ambient sensors in a meeting room for activity recognition
AI总结：
本文提出DOO-RE数据集，利用会议室环境传感器数据流支持单人和群体活动识别，通过多标注者交叉验证获得9种活动类型的高质量标注，填补了公共空间活动识别数据集的空白。
摘要：
随着物联网技术的发展，利用机器学习方法识别用户活动是一种向用户提供各种智能服务的有前景的方式。具有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。来自周围环境传感器的数据流非常适合这一要求。现有的环境传感器数据集仅支持受限的私人空间，而针对公共空间的数据集尽管研究兴趣日益增长，却尚未得到探索。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中收集的数据集。该数据集名为DOO-RE，包含来自多种环境传感器类型（如声音和投影仪）的数据流。每个传感器数据流被分割成活动单元，多名标注者通过交叉验证标注过程提供活动标签，以提高标注质量。我们最终获得了9种活动类型。据我们所知，DOO-RE是第一个支持在真实会议室中识别单人和群体活动并具有可靠标注的数据集。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
