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.

翻译：我们提出AffectAI-Capture协议，该协议用于在四人会议式互动中采集同步多模态数据，融合眼动追踪、可穿戴生理传感、近讲与房间音频、多视角视频、事件记录及结构化自报告。会话采用基于既定小组互动范式的固定任务模块，数据采集与后处理均围绕单一权威事件时间线与标准化输出展开。我们阐述了实验原理、同步理念、数据组织方式及实践权衡。通过受控台架测试，已开展音频质量与视频同步的试点验证；含完整协议会话的参与者研究仍在推进中。本工作的核心贡献在于：为情感、行为及会议分析研究，提供一套将任务设计、仪器配置、时序溯源与数据封装相衔接的可复现协议架构。