International Journal For Multidisciplinary Research

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Affective Computing in Mental Health: The Role of Facial Expression Recognition

Author(s) Pradeep Thakur, Inakshi Garg, Tarana Verma, Amarnath Yadav
Country India
Abstract Affective computing has emerged as one of the leading interdisciplinary disciplines that utilize technology to detect and interpret human emotions, and so it provides critical information for the mental health monitoring process. The present study addresses the facial expression recognition as
an important tool in the diagnosis and treatment of mental health disorders. By discussing the physiological and psychological implications that facial expressions may have, we analyze how FER systems can supplement the traditional approach to mental health evaluation by providing real-time feedback and timely intervention. Improved algorithms for machine learning and computer vision techniques in FER systems enhance the
accuracy and efficiency of FER systems, thus increasing their applicability in teletherapy, clinical assessment, and personal well-being applications. This discussion paper shall also touch on the issues and ethical concerns of FER technology, such as privacy and the potential for misinterpretation. Our results indicate that FER can be a meaningful contributor to care for personal mental health by continuous monitoring and understanding of emotional well-being.
Keywords Affective Computing, Facial Expression Recognition, Mental Health Monitoring, Emotion Analysis, Machine Learning, Teletherapy, Psychological Assessment, Real-Time Feedback, Ethical Considerations, Personalized Care
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 6, November-December 2024
Published On 2024-11-05
Cite This Affective Computing in Mental Health: The Role of Facial Expression Recognition - Pradeep Thakur, Inakshi Garg, Tarana Verma, Amarnath Yadav - IJFMR Volume 6, Issue 6, November-December 2024. DOI 10.36948/ijfmr.2024.v06i06.29870
DOI https://doi.org/10.36948/ijfmr.2024.v06i06.29870
Short DOI https://doi.org/g8qfwc
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