TheraSense: Deep Learning for Facial Emotion Analysis in Mental Health Teleconsultation

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Detalles Bibliográficos
Publicado en:	Electronics vol. 14, no. 3 (2025), p. 422
Autor principal:	Hadjar, Hayette
Otros Autores:	Vu, Binh, Hemmje, Matthias
Publicado:	MDPI AG
Materias:	Mental health Therapists Deep learning Performance evaluation Affective computing Health care policy Communication Telemedicine Feedback Mental health care Workflow Mental disorders Young adults Health services Emotions Patient satisfaction Physicians Qualitative analysis Machine learning Emotion recognition Neural networks Decision making Project management Design Video data Real time
Acceso en línea:	Citation/Abstract Full Text + Graphics Full Text - PDF
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Descripción
Resumen:	Background: This paper presents TheraSense, a system developed within the Supporting Mental Health in Young People: Integrated Methodology for cLinical dEcisions and evidence (Smile) and Sensor Enabled Affective Computing for Enhancing Medical Care (SenseCare) projects. TheraSense is designed to enhance teleconsultation services by leveraging deep learning for real-time emotion recognition through facial expressions. It integrates with the Knowledge Management-Ecosystem Portal (SenseCare KM-EP) platform to provide mental health practitioners with valuable emotional insights during remote consultations. Method: We describe the conceptual design of TheraSense, including its use case contexts, architectural structure, and user interface layout. The system’s interoperability is discussed in detail, highlighting its seamless integration within the teleconsultation workflow. The evaluation methods include both quantitative assessments of the video-based emotion recognition system’s performance and qualitative feedback through heuristic evaluation and survey analysis. Results: The performance evaluation shows that TheraSense effectively recognizes emotions in video streams, with positive user feedback on its usability and integration. The system’s real-time emotion detection capabilities provide valuable support for mental health practitioners during remote sessions. Conclusions: TheraSense demonstrates its potential as an innovative tool for enhancing teleconsultation services. By providing real-time emotional insights, it supports better-informed decision-making in mental health care, making it an effective addition to remote telehealth platforms.
ISSN:	2079-9292
DOI:	10.3390/electronics14030422
Fuente:	Advanced Technologies & Aerospace Database