用智能手表实时采集学生多模态数据，助力教育分析。
Real-Time Multimodal Data Collection Using Smartwatches and Its Visualization in Education
- 开发智能手表数据采集与可视化系统，支持多人同步监测
- 在65名学生课堂中成功采集心率、动作等多源数据
- 适合教育研究者开展真实场景下的学习行为分析
可穿戴传感器（如智能手表）在医疗、体育和教育等领域日益普及，能够持续监测生理与行为数据。在教育中，这些技术为研究参与度、注意力与表现等认知与情感过程提供了新机遇。然而，缺乏可扩展、同步且高分辨率的多模态数据采集工具，仍是多模态学习分析在真实教育环境中广泛应用的主要障碍。本文提出两个互补工具：Watch-DMLT，用于Fitbit Sense 2智能手表的实时多用户生理与运动信号采集；ViSeDOPS，基于仪表板的可视化系统，用于分析口语展示期间同步获取的多模态数据。在包含65名学生及最多16块手表的课堂部署中，成功捕获心率、动作、视线、视频与情境标注等数据流，并进行分析。结果表明，该系统在真实学习环境中具备实现细粒度、可扩展且可解释的多模态学习分析的可行性与实用性。
原文摘要 · Abstract (English)
Wearable sensors, such as smartwatches, have become increasingly prevalent across domains like healthcare, sports, and education, enabling continuous monitoring of physiological and behavioral data. In the context of education, these technologies offer new opportunities to study cognitive and affective processes such as engagement, attention, and performance. However, the lack of scalable, synchronized, and high-resolution tools for multimodal data acquisition continues to be a significant barrier to the widespread adoption of Multimodal Learning Analytics in real-world educational settings. This paper presents two complementary tools developed to address these challenges: Watch-DMLT, a data acquisition application for Fitbit Sense 2 smartwatches that enables real-time, multi-user monitoring of physiological and motion signals; and ViSeDOPS, a dashboard-based visualization system for analyzing synchronized multimodal data collected during oral presentations. We report on a classroom deployment involving 65 students and up to 16 smartwatches, where data streams including heart rate, motion, gaze, video, and contextual annotations were captured and analyzed. Results demonstrate the feasibility and utility of the proposed system for supporting fine-grained, scalable, and interpretable Multimodal Learning Analytics in real learning environments.
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