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Automatic Multimodal Descriptors of Rhythmic Body Movement

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If you have a question about this talk, please contact Jingjing Shen .

Prolonged durations of rhythmic body gestures were proved to be correlated with diff erent types of psychological disorders. To-date, there is no automatic descriptor that can robustly detect those behaviours. In this talk, we propose a cyclic gestures descriptor that can detect and localise rhythmic body movements by taking advantage of both intensity and depth modalities. We show experimentally how our rhythmic descriptor can successfully localise the rhythmic gestures as: hands fidgeting, legs fidgeting or rocking, significantly higher than the majority vote classification baseline. Our experiments also demonstrate the importance of fusing both modalities, with a significant increase in performance when compared to individual modalities.

This talk is part of the Rainbow Group Seminars series.

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Average on weekdays: 8971.69178082 kWh Sackler lecture theatre level 7, Cambridge Insititute for Medical Research LR5, Department of Engineering / Online (Zoom link: https://us06web.zoom.us/j/87986687566?pwd=MGJScmMwd2lwT0tVMHNmWmxSa05XZz09)

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