PaperarXiv:2611.14093
BODYinTRANSIT-Lab: A Multimodal Dataset for Investigating Sensory-Driven Transformations in Body Perception and Affective Experiences
arXiv preprint (cs.HC; cross-list cs.LG)
(opens in a new tab)Dehshibi, Mohammad Mahdi et al., "BODYinTRANSIT-Lab: A Multimodal Dataset for Investigating Sensory-Driven Transformations in Body Perception and Affective Experiences", arXiv:2611.14093, CC BY 4.0
TL;DR
UC3M-led dataset: 101 people walk while hearing manipulated footstep sounds that change perceived body weight. Synchronised full-body IMU kinematics + ECG, EDA, respiration and EMG, with SAM valence/arousal/dominance and body-map labels; stratified by eating-disorder symptoms and activity level. Kinematics-only baseline: 45.9% balanced accuracy for valence, 44.5% for perceived weight.
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 development of computational models that infer subjective states from sensor data. We introduce BODYinTRANSIT-Lab, a multimodal dataset that addresses this gap by capturing affective, perceptual, and behavioural responses to the Footsteps Illusion. In this closed-loop paradigm, participants perform walking tasks while receiving real-time manipulated auditory feedback of their own footsteps intended to alter perceived body weight. The dataset comprises synchronised recordings from 101 participants recruited through systematic stratification by eating-disorder symptomatology and physical activity level. BODYinTRANSIT-Lab combines full-body kinematics with electromyography, electrocardiography, respiration, and electrodermal activity. Its annotations capture both immediate and post-trial experience, including within-trial perceived-weight ratings, post-trial assessments of body feelings and affective experience, 3D avatar body visualisations, and quantitative body maps. Psychological, behavioural, demographic, and social measures provide further information about factors that may influence individual responses. We provide a deep learning benchmark that uses walking kinematics to classify relative affective valence and perceived weight within subjects. On within-session, segment-level splits, the baseline model achieved mean balanced accuracies of 45.9% for affective valence and 44.5% for perceived weight. The BODYinTRANSIT-Lab dataset provides an experimentally controlled resource for cognitive neuroscience and affective computing research, with potential applications in affect-aware interactive systems, health and well-being technologies, and creative embodied experiences.
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Cite this paper
Dehshibi, M. M., Cortés, T. M., D'Adamo, A., de la Prida, D., Azpicueta-Ruíz, L.-A., Vidal, L. T., Lesur, M. R., Díaz-Durán, J. R., Väljamäe, A., Sánchez, A., Olugbade, T., Bianchi-Berthouze, N., Díaz-de-María, F., & Tajadura-Jiménez, A. (2026). BODYinTRANSIT-Lab: A Multimodal Dataset for Investigating Sensory-Driven Transformations in Body Perception and Affective Experiences. arXiv preprint. https://arxiv.org/abs/2611.14093
Dehshibi, Mohammad Mahdi, Tomás Martínez Cortés, Amar D'Adamo, Daniel de la Prida, Luis-Antonio Azpicueta-Ruíz, Laia Turmo Vidal, and Marte Roel Lesur, et al. 2026. "BODYinTRANSIT-Lab: A Multimodal Dataset for Investigating Sensory-Driven Transformations in Body Perception and Affective Experiences." arXiv preprint. https://arxiv.org/abs/2611.14093.
Dehshibi, M. M., Cortés, T. M., D'Adamo, A., de la Prida, D., Azpicueta-Ruíz, L.-A., Vidal, L. T. and Lesur, M. R. et al. (2026) 'BODYinTRANSIT-Lab: A Multimodal Dataset for Investigating Sensory-Driven Transformations in Body Perception and Affective Experiences', arXiv preprint. Available at: https://arxiv.org/abs/2611.14093
Dehshibi, Mohammad Mahdi, et al. "BODYinTRANSIT-Lab: A Multimodal Dataset for Investigating Sensory-Driven Transformations in Body Perception and Affective Experiences." arXiv preprint, 2026. https://arxiv.org/abs/2611.14093.
M. M. Dehshibi et al., "BODYinTRANSIT-Lab: A Multimodal Dataset for Investigating Sensory-Driven Transformations in Body Perception and Affective Experiences," arXiv preprint, 2026, [Online]. Available: https://arxiv.org/abs/2611.14093.
@misc{dehshibi2026bodyintransitlab,
title = {{BODYinTRANSIT-Lab: A Multimodal Dataset for Investigating Sensory-Driven Transformations in Body Perception and Affective Experiences}},
author = {Dehshibi, Mohammad Mahdi and Cortés, Tomás Martínez and D'Adamo, Amar and de la Prida, Daniel and Azpicueta-Ruíz, Luis-Antonio and Vidal, Laia Turmo and Lesur, Marte Roel and Díaz-Durán, Joaquín R. and Väljamäe, Alexander and Sánchez, Angel and Olugbade, Temitayo and Bianchi-Berthouze, Nadia and Díaz-de-María, Fernando and Tajadura-Jiménez, Ana},
year = {2026},
howpublished = {arXiv preprint},
eprint = {2611.14093},
archivePrefix = {arXiv}
}Added to the ChatPPG research hub
