Emotion detection from handwriting and drawing samples using an attention-based transformer model.

ABSTRACT: Emotion detection (ED) involves the identification and understanding of an individual's emotional state through various cues such as facial expressions, voice tones, physiological changes, and behavioral patterns. In this context, behavioral analysis is employed to observe actions and behaviors for emotional interpretation. This work specifically employs behavioral metrics like drawing and handwriting to determine a person's emotional state, recognizing these actions as physical functions integrating motor and cognitive processes. The study proposes an attention-based transformer model as an innovative approach to identify emotions from handwriting and drawing samples, thereby advancing the capabilities of ED into the domains of fine motor skills and artistic expression. The initial data o

SUBMITTER: Khan ZA 

PROVIDER: S-EPMC11041987 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature