PDF(Pubmed)
Abstract：
In current smart classroom research, numerous studies focus on recognizing hand-raising, but few analyze the movements to interpret students\' intentions. This limitation hinders teachers from utilizing this information to enhance the effectiveness of smart classroom teaching. Assistive teaching methods, including robotic and artificial intelligence teaching, require smart classroom systems to both recognize and thoroughly analyze hand-raising movements. This detailed analysis enables systems to provide targeted guidance based on students\' hand-raising behavior. This study proposes a morphology-based analysis method to innovatively convert students\' skeleton key point data into several one-dimensional time series. By analyzing these time series, this method offers a more detailed analysis of student hand-raising behavior, addressing the limitations of deep learning methods that cannot compare classroom hand-raising enthusiasm or establish a detailed database of such behavior. This method primarily utilizes a neural network to obtain students\' skeleton estimation results, which are then converted into time series of several variables using the morphology-based analysis method. The YOLOX and HrNet models were employed to obtain the skeleton estimation results; YOLOX is an object detection model, while HrNet is a skeleton estimation model. This method successfully recognizes hand-raising actions and provides a detailed analysis of their speed and amplitude, effectively supplementing the coarse recognition capabilities of neural networks. The effectiveness of this method has been validated through experiments.
摘要：
在当前的智慧课堂研究中，许多研究集中在识别举手，但很少有人分析这些动作来解释学生的意图。这种局限性阻碍了教师利用这些信息来提高智慧课堂教学的有效性。辅助教学方法，包括机器人和人工智能教学，需要智能教室系统来识别和彻底分析举手动作。这种详细分析使系统能够根据学生的举手行为提供有针对性的指导。本研究提出了一种基于形态学的分析方法，将学生的骨架关键点数据创新性地转换为几个一维时间序列。通过分析这些时间序列,这种方法对学生举手行为进行了更详细的分析，解决了深度学习方法无法比较课堂举手热情或建立此类行为的详细数据库的局限性。该方法主要利用神经网络获得学生的骨架估计结果，然后使用基于形态学的分析方法将其转换为几个变量的时间序列。采用YOLOX和HrNet模型获得骨架估计结果；YOLOX是目标检测模型，而HrNet是一个骨架估计模型。该方法成功识别举手动作，并对其速度和幅度进行了详细分析，有效补充了神经网络的粗识别能力。通过实验验证了该方法的有效性。
