构建可复现的四人会议多模态数据采集协议
AffectAI-Capture: A Reproducible Multimodal Protocol for Small-Group Meeting Research
- 固定任务块结合眼动、生理、音视频等多源数据同步采集
- 基于统一事件时间轴实现数据对齐与标准化输出
- 适合情感分析、行为研究与会议智能分析的科研人员
我们提出AffectAI-Capture，一种用于四人会议类互动的同步多模态数据采集协议，整合眼动追踪、可穿戴生理信号、近场与远场音频、多视角视频、事件日志及结构化自评。实验采用基于经典群体互动范式的固定任务块，采集与后处理均围绕单一权威事件时间轴展开，确保数据一致性。已通过受控测试验证音频质量与视频同步性；完整协议在参与者中持续运行。该协议实现了任务设计、仪器配置、时间溯源与数据封装的可复现架构，适用于情感计算、行为分析与会议智能研究。
原文摘要 · Abstract (English)
We present AffectAI-Capture, a protocol for collecting synchronized multimodal data in four-person meeting-like interactions, combining eye tracking, wearable physiology, close-talk and room audio, multi-view video, event logging, and structured self-report. Sessions use fixed task blocks grounded in established group-interaction paradigms, while acquisition and post-processing are organized around a single authoritative event timeline and standardized outputs. We describe the experimental rationale, synchronization philosophy, data organization, and practical trade-offs. Pilot-level validation of audio quality and video synchronization has been conducted using controlled bench tests; full protocol sessions with participants remain ongoing work. The contribution is a reproducible protocol architecture linking task design, instrumentation, timing provenance, and data packaging for affective, behavioral, and meeting-analytics research.
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