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.

Human body movements are important for emotion recognition and social communication and have received extensive attention from researchers. In this field, emotional biological motion stimuli, as depicted by point-light displays, are widely used. However, the number of stimuli in the existing material library is small, and there is a lack of standardized indicators, which subsequently limits experimental design and conduction. Therefore, based on our prior kinematic dataset, we constructed the Dalian Emotional Movement Open-source Set (DEMOS) using computational modeling. The DEMOS has three views (i.e., frontal 0°, left 45°, and left 90°) and in total comprises 2664 high-quality videos of emotional biological motion, each displaying happiness, sadness, anger, fear, disgust, and neutral. All stimuli were validated in terms of recognition accuracy, emotional intensity, and subjective movement. The objective movement for each expression was also calculated. The DEMOS can be downloaded for free from https:// osf. io/ 83fst/. To our knowledge, this is the largest multi-view emotional biological motion set based on the whole body. The DEMOS can be applied in many fields, including affective computing, social cognition, and psychiatry.

In daily life, individuals need to recognize and update emotional information from others' changing body expressions. However, whether emotional bodies can enhance working memory (WM) remains unknown. In the present study, participants completed a modified n‐back task, in which they were required to indicate whether a presented image of an emotional body matched that of an item displayed before each block (0‐back) or two positions previously (2‐back). Each block comprised only fear, happiness, or neutral. We found that in the 0‐back trials, when compared with neutral body expressions, the participants took less time and showed comparable ceiling effects for accuracy in happy bodies followed by fearful bodies. When WM load increased to 2‐back, both fearful and happy bodies significantly facilitated WM performance (i.e., faster reaction time and higher accuracy) relative to neutral conditions. In summary, the current findings reveal the enhancement effect of emotional body expressions on WM and highlight the importance of emotional action information in WM.