TY - JOUR
T1 - Bodily expressed emotion understanding through integrating Laban movement analysis
AU - Wu, Chenyan
AU - Davaasuren, Dolzodmaa
AU - Shafir, Tal
AU - Tsachor, Rachelle
AU - Wang, James Z.
N1 - Publisher Copyright:
© 2023 The Authors
PY - 2023/10/13
Y1 - 2023/10/13
N2 - Bodily expressed emotion understanding (BEEU) aims to automatically recognize human emotional expressions from body movements. Psychological research has demonstrated that people often move using specific motor elements to convey emotions. This work takes three steps to integrate human motor elements to study BEEU. First, we introduce BoME (body motor elements), a highly precise dataset for human motor elements. Second, we apply baseline models to estimate these elements on BoME, showing that deep learning methods are capable of learning effective representations of human movement. Finally, we propose a dual-source solution to enhance the BEEU model with the BoME dataset, which trains with both motor element and emotion labels and simultaneously produces predictions for both. Through experiments on the BoLD in-the-wild emotion understanding benchmark, we showcase the significant benefit of our approach. These results may inspire further research utilizing human motor elements for emotion understanding and mental health analysis.
AB - Bodily expressed emotion understanding (BEEU) aims to automatically recognize human emotional expressions from body movements. Psychological research has demonstrated that people often move using specific motor elements to convey emotions. This work takes three steps to integrate human motor elements to study BEEU. First, we introduce BoME (body motor elements), a highly precise dataset for human motor elements. Second, we apply baseline models to estimate these elements on BoME, showing that deep learning methods are capable of learning effective representations of human movement. Finally, we propose a dual-source solution to enhance the BEEU model with the BoME dataset, which trains with both motor element and emotion labels and simultaneously produces predictions for both. Through experiments on the BoLD in-the-wild emotion understanding benchmark, we showcase the significant benefit of our approach. These results may inspire further research utilizing human motor elements for emotion understanding and mental health analysis.
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U2 - 10.1016/j.patter.2023.100816
DO - 10.1016/j.patter.2023.100816
M3 - Article
C2 - 37876902
AN - SCOPUS:85173221101
SN - 2666-3899
VL - 4
JO - Patterns
JF - Patterns
IS - 10
M1 - 100816
ER -