AI identifies human emotion based on walking style
Researchers are working on a new data-driven model and algorithm to identify the perceived emotions of individuals based on their walking styles
Researchers at the University of North Carolina at Chapel Hill and the University of Maryland at College Park are working on a new data-driven model and algorithm to identify the perceived emotions of individuals based on their walking styles.
Exploiting gait features to classify emotional state
Through RGB videos of an individual walking, the team extracted his/her walking gait in the form of a series of 3D poses. The aim was to exploit the gait features to classify the emotional state of the human into one of four emotions: happy, sad, angry, or neutral. The researchers’ perceived emotion recognition approach is based on using deep features learned via long short-term memory (LSTM) on labeled emotion datasets.
Moreover, the team combined these features with affective features computed from the gaits utilizing posture and movement cues. Such…