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

“Forest” by Simon Lehmann (Pixabay)

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.

A representation of the novel algorithm to identify the perceived emotions of individuals based on their walking styles. Given an RGB video of an individual walking (top), the researchers extracted his/her walking gait as a series of 3D poses (bottom). The used a combination of deep features learned via an LSTM and affective features computed using posture and movement cues and classify using a Random Forest Classifier into basic emotions (e.g., happy, sad, etc.). Credit: Randhavane et al., Fair Use.

Moreover, the team combined these features with affective features computed from the gaits utilizing posture and movement cues. Such…