Using body movement and posture for emotion detection in non-acted scenarios

In this paper, we explored the use of features that represent body posture and movement for automatically detecting people's emotions in non-acted standing scenarios. We focused on four emotions that are often observed when people are playing video games: triumph, frustration, defeat, and concentration. The dataset consists of recordings of the rotation angles of the player's joints while playing Wii sports games. We applied various machine learning techniques and bagged them for prediction. When body pose and movement features are used we can reach an overall accuracy of 66.5% for differentiating between these four emotions. In contrast, when using the raw joint rotations, limb rotation movement, or posture features alone, we were only able to achieve accuracy rates of 59%, 61%, and 62% respectively. Our results suggest that features representing changes in body posture can yield improved classification rates over using static postures or joint information alone.