Human Emotion Recognition in Collaborative Tasks Using Virtual Reality Games

Collaboration enhances work performance and efficiency, yet certain collaborative actions may induce stress in individuals. This study aims to examine variations in stress levels among participants during collaborative tasks and explore changes in their emotional status based on different collaboration styles with their co-workers (such as slow response, poor success rate, and lack of clarity). To achieve this, a Long Short-Term Memory (LSTM) network is trained in this paper to predict human stress. For the experimental validation, participants are invited to safely collaborate on finishing a shared task in virtual reality (VR). Physiological data, including blood volume pulse (BVP), electrodermal activity (EDA), skin temperature, and motion intensity signals, are streamed through a medical wearable watch for stress analysis by the developed LSTM model. The experimental results demonstrate the effectiveness of the system in identifying stress during collaborative VR tasks. In addition to the area of VR games, the research findings from this paper can be extended to a broader area, such as human-machine interaction, intelligent medical assistance, and psychological studies.