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arXiv · 2605.19794

AffectAI-Capture: A Reproducible Multimodal Protocol for Small-Group Meeting Research

Meisam Jamshidi Seikavandi·Alice Modica·Anna Obara·Fabricio Batista Narcizo·Tanya Ignatenko·Ted Vucurevich·Jesper Bünsow Boldt·Paolo Burelli·Andrew Burke Dittberner
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Abstract

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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Meisam Jamshidi Seikavandi, Alice Modica, Anna Obara, Fabricio Batista Narcizo, Tanya Ignatenko, Ted Vucurevich, Jesper Bünsow Boldt, Paolo Burelli, Andrew Burke Dittberner. 2026-05-19. AffectAI-Capture: A Reproducible Multimodal Protocol for Small-Group Meeting Research. https://arxiv.org/abs/2605.19794

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