Abstract
Human body movements can convey a variety of emotions and even create advantages in some special life situations. However, how emotion is encoded in body movements has remained unclear. One reason is that there is a lack of public human body kinematic dataset regarding the expressing of various emotions. Therefore, we aimed to produce a comprehensive dataset to assist in recognizing cues from all parts of the body that indicate six basic emotions (happiness, sadness, anger, fear, disgust, surprise) and neutral expression. The present dataset was created using a portable wireless motion capture system. Twenty-two semi-professional actors (half male) completed performances according to the standardized guidance and preferred daily events. A total of 1402 recordings at 125 Hz were collected, consisting of the position and rotation data of 72 anatomical nodes. To our knowledge, this is now the largest emotional kinematic dataset of the human body. We hope this dataset will contribute to multiple fields of research and practice, including social neuroscience, psychiatry, computer vision, and biometric and information forensics.
Highlights
Background & SummaryRecognizing human emotions is crucial to people’s survival and social communication
The classical theories of emotion have highlighted the significance of body movements since the 19th century[4,5], as de Gelder[1] said, “bodily expressions never occupied centre stage in emotion research.”
Psychologists have taken up this issue and found that body movements can provide comparable recognition accuracy relative to facial expression, regardless of static and dynamic conditions[6,7]
Summary
Background & SummaryRecognizing human emotions is crucial to people’s survival and social communication. The relevant existing works in both psychology[1] and computer science[2,3] mainly focus on human faces and voices as well as emotional scene (context, situations, and conditions). Psychologists have taken up this issue and found that body movements can provide comparable recognition accuracy relative to facial expression, regardless of static and dynamic conditions[6,7]. When someone is experiencing the most intense emotion of her/his life (e.g., winning the Olympic gold medal), others cannot recognize his/her emotion from just the face and have to use body cues[8]. Complexities in various situations exist due to the emotional interactions with voice[9] and scene[10,11]. Human body indicators are essential for thorough emotion recognition
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