CADDI: An in-Class Activity Detection Dataset using IMU data from low-cost sensors
This addresses the lack of large, labeled datasets for activity recognition in educational settings, enabling scalable solutions for monitoring student engagement, though it is incremental as it focuses on data collection rather than new methods.
The paper tackles the problem of detecting in-class student activities by introducing a novel dataset using low-cost IMU sensors, comprising 19 activities from 12 participants with multimodal data including accelerometer, gyroscope, rotation vector, and synchronized stereo images.
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.