用低成本传感器采集课堂行为数据，构建19类活动的标注数据集。
CADDI: An in-Class Activity Detection Dataset using IMU data from low-cost sensors
- 基于智能手表IMU数据，采集12人真实课堂行为。
- 包含19类动作，涵盖瞬时与持续行为，带多模态数据。
- 适合做教育场景下的行为识别与教学干预研究。
监控和预测课堂中学生的行为对理解学习投入度、提升教学效果至关重要。准确识别这些行为可使教师实时调整教学策略，减少负面情绪，改善学习体验。为此，采用嵌入智能手表中的惯性测量单元（IMU）等非侵入式设备是可行方案。然而，可靠预测系统的开发受限于教育领域缺乏大规模标注数据集。为填补这一空白，我们提出一个基于低成本IMU传感器的课堂行为检测新数据集。该数据集包含12名参与者在典型教室场景中完成的19种不同活动（包括瞬时与连续动作），提供加速度计、陀螺仪、旋转矢量数据及同步立体图像，为融合传感器与视觉数据的多模态算法开发提供了全面资源。该数据集是实现教育场景下行为识别规模化解决方案的关键一步。
原文摘要 · Abstract (English)
The monitoring and prediction of in-class student activities is of paramount importance for the comprehension of engagement and the enhancement of pedagogical efficacy. The accurate detection of these activities enables educators to modify their lessons in real time, thereby reducing negative emotional states and enhancing the overall learning experience. To this end, the use of non-intrusive devices, such as inertial measurement units (IMUs) embedded in smartwatches, represents a viable solution. The development of reliable predictive systems has been limited by the lack of large, labeled datasets in education. To bridge this gap, we present a novel dataset for in-class activity detection using affordable IMU sensors. The dataset comprises 19 diverse activities, both instantaneous and continuous, performed by 12 participants in typical classroom scenarios. It includes accelerometer, gyroscope, rotation vector data, and synchronized stereo images, offering a comprehensive resource for developing multimodal algorithms using sensor and visual data. This dataset represents a key step toward scalable solutions for activity recognition in educational settings.
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