<![CDATA[The increasing prevalence of stress, anxiety, and emotional imbalance in digital society highlights the importance of designing computational systems that not only process information but also support psychological resilience, forming the background of this study. With this in mind, the objective of the research is to explore and develop affective and positive computing algorithms and interfaces capable of fostering emotional wellbeing through adaptive interaction strategies. To achieve this, the method combines literature review, algorithmic design, and prototype evaluation using affective data such as facial expression, voice tone, and physiological signals, which are analyzed through machine learning models and integrated into interactive interface prototypes. The results indicate that the proposed algorithms successfully recognize emotional states with higher accuracy than baseline models, while the interfaces provide feedback and adaptive interventions that enhance users’ sense of calmness, engagement, and positive affect during interaction sessions. Moreover, experimental validation suggests that the system can dynamically adjust its responses to individual emotional patterns, leading to more personalized and effective support for wellbeing. In conclusion, this research demonstrates that affective and positive computing can be meaningfully integrated into algorithmic frameworks and interface design to promote emotional health, offering not only theoretical contributions to the field of human-computer interaction but also practical implications for digital mental health tools that encourage resilience and positive experiences in everyday life.]]>
