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New multimodal dataset BODYinTRANSIT-Lab released for body perception research

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Researchers have introduced BODYinTRANSIT-Lab, a new multimodal dataset designed to advance the study of body perception and affective experiences. This dataset captures synchronized behavioral and physiological signals alongside subjective measures from 101 participants exposed to the "Footsteps Illusion," where auditory feedback manipulates perceived body weight. The dataset includes detailed annotations such as perceived-weight ratings, body feelings, affective experiences, and 3D avatar visualizations, along with psychological and demographic data. A deep learning benchmark using walking kinematics demonstrated baseline accuracies of approximately 45% for classifying affective valence and perceived weight, highlighting the dataset's potential for research in cognitive neuroscience and affective computing. AI

IMPACT Enables development of computational models for inferring subjective states from sensor data, with potential applications in affect-aware systems and health technologies.

RANK_REASON The item describes a new research dataset and associated benchmark, published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New multimodal dataset BODYinTRANSIT-Lab released for body perception research

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The item describes a new research dataset and associated benchmark, published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Mohammad Mahdi Dehshibi, Tom\'{a}s Mart\'{i}nez Cort\'{e}s, Amar D'Adamo, Daniel de la Prida, Luis-Antonio Azpicueta-Ru\'{i}z, Laia Turmo Vidal, Marte Roel Lesur, Joaqu\'{i}n R. D\'{i}az-Dur\'{a}n, Alexander V\"{a}ljam\"{a}e, Angel S\'{a}nchez, Temitayo … ·

    BODYinTRANSIT-Lab: A Multimodal Dataset for Investigating Sensory-Driven Transformations in Body Perception and Affective Experiences

    arXiv:2611.14093v1 Announce Type: cross Abstract: Multimodal datasets that combine synchronised behavioural and physiological signals with subjective affective and body perception measures are still rare. This limits both the understanding of embodied experience and the developme…

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