BODYinTRANSIT-Lab：身体知覚と情動体験の感覚駆動型変化を調べるマルチモーダルデータセット
BODYinTRANSIT-Lab: A Multimodal Dataset for Investigating Sensory-Driven Transformations in Body Perception and Affective Experiences
足音の聴覚フィードバックを操作して体重知覚を変える「足音錯覚」課題で、101名の歩行運動・生理信号・主観評価を同期収録したマルチモーダルデータセットを構築し、歩行キネマティクスから情動価と体重知覚を分類する深層学習ベンチマークを提供した。
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著者: Mohammad Mahdi Dehshibi, Tomás Martínez Cortés, Amar D'Adamo, Daniel de la Prida, Luis-Antonio Azpicueta-Ruíz, Laia Turmo Vidal, Marte Roel Lesur, Joaquín R. Díaz-Durán, Alexander Väljamäe, Angel Sánchez, Temitayo Olugbade, Nadia Bianchi-Berthouze, Fernando Díaz-de-María, Ana Tajadura-Jiménez
分類: cs.HC, cs.LG
原文アブストラクト
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.