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

[Submitted on 10 Nov 2026]

View a PDF of the paper titled BODYinTRANSIT-Lab: A Multimodal Dataset for Investigating Sensory-Driven Transformations in Body Perception and Affective Experiences, by Mohammad Mahdi Dehshibi and Tom\'{a}s Mart\'{i}nez Cort\'{e}s and Amar D’Adamo and Daniel de la Prida and Luis-Antonio Azpicueta-Ru\'{i}z and Laia Turmo Vidal and Marte Roel Lesur and Joaqu\'{i}n R. D\'{i}az-Dur\'{a}n and Alexander V\”{a}ljam\”{a}e and Angel S\'{a}nchez and Temitayo Olugbade and Nadia Bianchi-Berthouze and Fernando D\'{i}az-de-Mar\'{i}a and Ana Tajadura-Jim\'{e}nez

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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.