Affective Computing in Mood Disorders: Beyond Conventional Diagnostic Tools to Modern Technologies

The increasing prevalence of mental disorders among youth presents significant challenges for healthcare systems worldwide. Affective disorders, such as depression and anxiety, are primarily characterized by emotional biases that significantly impact individuals’ well-being and overall quality of life. To better understand and address these disorders, the existing field of mental health systems needs to move beyond traditional paper-pencil self-reports, such as BDI-II (Beck Depression Inventory-II) or PHQ-9, as they may miss out on subtle cues that affected persons may be unaware of. Affective computing is one such field that can effectively contribute to our holistic understanding of mental disorders, especially in developing robust diagnostic systems. The use of affective computing techniques offers the promise of capturing subtle emotional cues and biases that may not be readily observable through traditional clinical assessments. By introducing empirical studies and statistical tools such as principal component analysis (PCA), we propose that incorporating objective measures of emotions can enhance the diagnostic accuracy and reliability of mental health assessments. In the present chapter, we present an empirical study that demonstrates that the existing robustness of the current diagnostic systems can be enhanced by identifying patterns and correlations within multidimensional emotion data. The chapter also discusses the potential of integrating affective computing and statistical analysis techniques as a promising approach to revolutionizing mental health assessments. The present chapter thus highlights the potential of affective computing to contribute to developing effective, evidence-based interventions and improve mental health care on a broader scale.