用生理信号和头部姿态检测在线学习时的手机分心，准确率达91%
AI-based Multimodal Biometrics for Detecting Smartphone Distractions: Application to Online Learning
- 融合脑电、心率与头部姿态的多模态分析方法
- 单一信号准确率不足，多模态组合达91%准确率
- 适合需要实时注意力监测的在线教育场景
本文研究利用多模态生物特征检测在需持续注意力的任务中由智能手机使用引发的分心问题，重点聚焦于基于计算机的在线学习场景。尽管该方法适用于自动驾驶等多个领域，但本研究关注学习者在动机、课程设计及手机使用等内外因素影响下维持专注的挑战。传统学习平台缺乏细致的行为数据，而多模态学习分析（MMLA）与生物传感器为理解学习者注意力提供了新视角。我们提出一种基于人工智能的方法，利用生理信号与头部姿态数据检测手机使用行为。结果显示，单一生物特征信号（如脑电或心率）的准确率较低，而仅使用头部姿态即达87%准确率；融合所有信号的多模态模型准确率达到91%，凸显了多源信息整合的优势。最后，文章讨论了该模型在在线学习环境中实现实时支持的潜力与局限。
原文摘要 · Abstract (English)
This work investigates the use of multimodal biometrics to detect distractions caused by smartphone use during tasks that require sustained attention, with a focus on computer-based online learning. Although the methods are applicable to various domains, such as autonomous driving, we concentrate on the challenges learners face in maintaining engagement amid internal (e.g., motivation), system-related (e.g., course design) and contextual (e.g., smartphone use) factors. Traditional learning platforms often lack detailed behavioral data, but Multimodal Learning Analytics (MMLA) and biosensors provide new insights into learner attention. We propose an AI-based approach that leverages physiological signals and head pose data to detect phone use. Our results show that single biometric signals, such as brain waves or heart rate, offer limited accuracy, while head pose alone achieves 87%. A multimodal model combining all signals reaches 91% accuracy, highlighting the benefits of integration. We conclude by discussing the implications and limitations of deploying these models for real-time support in online learning environments.
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