Abstract
Human motion analysis, including data visualization and annotation, is crucial for understanding human behavior and intentions during various activities, aiding in the development of innovative tools that support independent living. Current wearable sensing technology provides rich 3D spatial movement data but generates multimodal complex datasets that require specialized skills for effective analysis. Despite the need, limited research exists on tools for effective visualization and easy annotation of such complex motion data. MoViAn (Motion Data Visualization and Annotation) is an innovative 3D data analysis system offering enriched visual representations of 3D human motion data (e.g., gaze, hand movements), along with an interactive user interface for data annotation. This paper presents a user study with 11 participants and demonstrates that MoViAn facilitates data annotation of human motion effectively through both quantitative and qualitative assessments. Specifically, gaze visualization improves annotation accuracy by 18.23%, while hand-tracing visualization increases annotation speed by 28.05% compared to a controlled setting. These findings highlight MoViAn’s potential for handling multimodal human motion data, paving the way for developing innovative tools that provide real-time monitoring and assessment of physical activities to support independent living.
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The human subject research was approved by the Institutional Review Board at Chapman University with IRB number: IRB-22-250.
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Qi, T.D., Browen, I., Zhang, D., Camarillo-Abad, H.M., Cibrian, F.L. (2024). MoViAn: Advancing Human Motion Analysis with 3D Visualization and Annotation. In: Bravo, J., Nugent, C., Cleland, I. (eds) Proceedings of the International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2024). UCAmI 2024. Lecture Notes in Networks and Systems, vol 1212. Springer, Cham. https://doi.org/10.1007/978-3-031-77571-0_2
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