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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.19794 |
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| _version_ | 1866914580444217344 |
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| author | Seikavandi, Meisam Jamshidi Modica, Alice Obara, Anna Narcizo, Fabricio Batista Ignatenko, Tanya Vucurevich, Ted Boldt, Jesper Bünsow Burelli, Paolo Dittberner, Andrew Burke |
| author_facet | Seikavandi, Meisam Jamshidi Modica, Alice Obara, Anna Narcizo, Fabricio Batista Ignatenko, Tanya Vucurevich, Ted Boldt, Jesper Bünsow Burelli, Paolo Dittberner, Andrew Burke |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_19794 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AffectAI-Capture: A Reproducible Multimodal Protocol for Small-Group Meeting Research Seikavandi, Meisam Jamshidi Modica, Alice Obara, Anna Narcizo, Fabricio Batista Ignatenko, Tanya Vucurevich, Ted Boldt, Jesper Bünsow Burelli, Paolo Dittberner, Andrew Burke Human-Computer Interaction Artificial Intelligence Databases 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. |
| title | AffectAI-Capture: A Reproducible Multimodal Protocol for Small-Group Meeting Research |
| topic | Human-Computer Interaction Artificial Intelligence Databases |
| url | https://arxiv.org/abs/2605.19794 |