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Movement Analysis and Decomposition with the Continuous Wavelet Transform

Jules Françoise 1 Gabriel Meseguer-Brocal 2 Frédéric Bevilacqua 2 
1 AMI - Architectures et Modèles pour l'Interaction
LISN - Laboratoire Interdisciplinaire des Sciences du Numérique, IaH - Interaction avec l'Humain
Abstract : Human movements support communication, and can be used to imitate actions or physical phenomenons. Observing gestural imitations of short sounds, we found that such gestures can be categorized by their frequency content. To analyse such movements, we propose an analysis method based on wavelet analysis for clustering or recognizing movement characteristics. Our technique draws upon the continuous wavelet transform to derive a time-frequency representation of movement information. We propose several global descriptors based on statistical descriptors, frequency tracking, or non-negative matrix factorization, that can be used for recognition or clustering to highlight relevant movement qualities. Additionally, we propose a real-time implementation of the continuous wavelet transform based on a set of approximations, that enables its use in interactive applications. Our method is evaluated on a database of gestures co-executed with vocal imitations of recorded sounds.
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Submitted on : Friday, July 1, 2022 - 11:51:57 AM
Last modification on : Friday, August 5, 2022 - 9:27:30 AM


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Jules Françoise, Gabriel Meseguer-Brocal, Frédéric Bevilacqua. Movement Analysis and Decomposition with the Continuous Wavelet Transform. MOCO '22: 8th International Conference on Movement and Computing, Jul 2022, Chicago, France. pp.1-13, ⟨10.1145/3537972.3537998⟩. ⟨hal-03711293⟩



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