Affective Computing and Emotion-Sensing Technology for Emotion Recognition in Mood Disorders

Sinha, N. (2021) Affective Computing and Emotion-Sensing Technology for Emotion Recognition in Mood Disorders. In: Enhanced Telemedicine and e-Health. 14349922, 410 . Springer International Publishing, Cham, pp. 337-360.

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Abstract

Emotions are species-typical patterns and can be a window to describe the human mind in action. Understanding emotion can provide invaluable insights into various mood disorders, including depression, which makes up the leading cause of disability worldwide. While emotions are not directly observable, they can be inferred via multiple components, including action intention, cognitive appraisals, physiological changes, and subjective feelings. Through various emotion-sensing technologies, the Internet of Things (IoT) is further enhancing the way technology can help perceive human emotions. Thus, the advances in such technologies could indeed provide future directions of assessment, diagnosis, and treatment of various mood disorders. With an introduction to the theories of emotion, this chapter will extend the conceptual foundations and approaches of the emotion-sensing technology in IoT, further introducing readers about emotion and attention-related biases in mood disorders. An extensive review of literature in emotion-sensing technology will provide empirical instances to existing technologies, which can help readers understand emotion analysis and extraction methods in detail, especially when used in the mental health domains. Finally, this chapter will provide practical applications, limitations, and future directions for advancing and humanizing affective computing and the IoT, and may help clinicians make informed decisions about the appropriate method for human emotion evaluation and analysis. This work, therefore, aims to enlighten mental health experts, clinicians, interface designers, and research scientists on existing emotion recognition methods and how to incorporate the IoT in emotion-sensing and further improve its methodology by considering various complexities of emotions and their interactions with other cognitive faculties and measurement variables. In short, this chapter discusses the-state-of-progress of emotion-sensing technology in the IoT with an impetus on mood disorders.

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