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Human motion recognition is a challenging task, especially when motion capture data is huge. Existing approaches for this task focused mainly on how to extract features from motion capture data to achieve high recognition performance. However, due to the presence of redundant features and the high dimensionality of data, these approaches may not achieve the optimal performance. In order to rapidly...
Biosignals-based affective computing plays an important role in human-computing interaction. In recent years, a lot of works have been successfully implemented for emotion recognition with biological signals. However most of them are computationally expensive due to the complexity of models. In this paper, we present a multi-label learning (MLL) method to map biological signals to an affective model...
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