School of Mechanical Engineering, Hubei University of Arts and Science, Xiangyang 441053, China.
Hubei Key Laboratory of Digital Education, Central China Normal University, Wuhan 430079, China.
Sensors (Basel). 2024 Jun 3;24(11):3609. doi: 10.3390/s24113609.
Cognitive engagement involves mental and physical involvement, with observable behaviors as indicators. Automatically measuring cognitive engagement can offer valuable insights for instructors. However, object occlusion, inter-class similarity, and intra-class variance make designing an effective detection method challenging. To deal with these problems, we propose the Object-Enhanced-You Only Look Once version 8 nano (OE-YOLOv8n) model. This model employs the YOLOv8n framework with an improved Inner Minimum Point Distance Intersection over Union (IMPDIoU) Loss to detect cognitive engagement. To evaluate the proposed methodology, we construct a real-world Students' Cognitive Engagement (SCE) dataset. Extensive experiments on the self-built dataset show the superior performance of the proposed model, which improves the detection performance of the five distinct classes with a precision of 92.5%.
认知参与涉及心理和身体的投入，以可观察的行为作为指标。自动测量认知参与可以为教师提供有价值的见解。然而，目标遮挡、类间相似性和类内方差使得设计有效的检测方法具有挑战性。为了解决这些问题，我们提出了 Object-Enhanced-You Only Look Once version 8 nano (OE-YOLOv8n) 模型。该模型采用 YOLOv8n 框架，改进了 Inner Minimum Point Distance Intersection over Union (IMPDIoU) 损失，以检测认知参与。为了评估所提出的方法，我们构建了一个真实世界的学生认知参与 (SCE) 数据集。在自建数据集上的广泛实验表明，所提出的模型具有优越的性能，它提高了五个不同类别的检测性能，精度达到 92.5%。
