IntroductionAutomated emotion recognition systems often rely on acted datasets and categorical models that miss the nuance of spontaneous affect.MethodsThis work assembled a large corpus of authentic facial emotion expressions from naturalistic outpatient psychotherapy sessions, annotated with free-text descriptions by human labelers. These descriptions were embedded in a 768-dimensional semantic space using a fine-tuned German Sentence-BERT model. Transformer, BILSTM, and deep neural network architectures were trained to map facial landmark features to continuous emotion embeddings.ResultsLeave-one-out cross-validation showed model predictions closely matched human annotations with a mean z-score of 1.97. External evaluation against acted datasets (RAVDESS) confirmed strong recognition of joy, sadness, and fear.DiscussionTo enhance interpretability, a back-translation mechanism using cosine similarity was implemented and visualized with radar charts. All components were integrated into AFFECT, an open-source pipeline for analyzing emotional expressions in everyday video recordings.
Digital first primary care in NHS England: evaluating alignment with patient-centered care and implications for future practice
The Digital First Primary Care (DFPC) model, introduced by NHS England, aims to enhance healthcare accessibility and efficiency by leveraging digital tools such as telemedicine,