The language of gait: interpreting emotional states through gait videos.

INTRODUCTION

Gait integrates sensorimotor and affective processes, serving both locomotor function and emotional expression. This embodiment framework has clinical relevance in neurodegenerative disorders and suggests potential for emotion-based modulation of gait.

OBJECTIVES

This study developed and tested a range of emotion-specific gait videos to assess whether healthy individuals could correctly recognize different emotions, in order to identify standardized stimuli for future research on emotion recognition and embodied simulation in neurological patients.

METHODS

We created a video questionnaire featuring an actress walking with gait patterns meant to convey eight emotions: happiness, surprise, fear, anxiety, disgust, sadness, anger, and neutral. Her facial expressions were either visible or blurred to focus attention on body movements. Participants selected the recognized emotion from a list and rated its valence and intensity. Recognition accuracy, specificity, sensitivity, valence, and intensity scores were used to identify the most effective video for each emotion.

RESULTS

110 healthy subjects (aged 25-40 years) participated in the questionnaire. Most emotions-neutral, happiness, sadness, fear, and anger-were recognized with high accuracy (> 90%), specificity and sensitivity in blurred-face videos. Disgust and surprise were harder to identify, and anxiety was often confused with fear. Valence and intensity ratings aligned with the intended emotions for both blurred and visible faces, although they were generally higher for visible facial expressions.

CONCLUSION

This study validates emotional gait videos as reliable stimuli for emotion recognition in healthy adults, laying the groundwork for their use in research on emotional embodiment in neurological disorders. These stimuli offer a tool for exploring emotion-gait interactions and developing emotion-based neurorehabilitation strategies.