Preliminary Validation of a Facial-Expression and Movement Analysis Algorithm During Emotion Elicitation Tasks in Healthy Adults.

This protocol was designed to preliminarily validate Emoscape, a proprietary machine-learning algorithm based on the Navarasa framework, by testing whether its emotion scores change in concordance with standardized emotion-elicitation tasks in healthy adults. Seventy-two healthy adults were recruited after providing written informed consent. Participants completed the Patient Health Questionnaire-9 and Generalized Anxiety Disorder-7, then viewed facial emotion stimuli from the TRENDS set and Navarasa images, while the FEA algorithm recorded upper-body micro-movements throughout the task. Participants also identified the emotions displayed. Changes in the FEA algorithm scores from baseline were analyzed during exposure to neutral, happy, fear, and anger stimuli from TRENDS, as well as during exposure to Navarasa images. Receiver operating characteristic analysis was used to estimate sensitivity and specificity for discrimination across emotional categories. The FEA algorithm scores showed significant deviations from baseline for TRENDS images mapped to corresponding emotions (Neutral-Shanta, Happy-Haasya, Fear-Bhayanaka, and Anger-Raudra). Concordant changes were also observed in Navarasa images, with all mean differences statistically significant (p < 0.001). Area under the curve (AUC) values varied across categories. The FEA algorithm showed modest sensitivity for pleasant emotions (70-80%) and unpleasant emotions (60-70%). These findings provide preliminary support for the protocol and for the use of the FEA algorithm to assess emotion-related responses during controlled emotion-elicitation tasks in healthy adults.