Description: Affective computing wearables are technological devices designed to monitor and analyze users’ emotional responses, providing real-time feedback. These devices use advanced sensors to capture biometric data, such as heart rate, skin conductance, and movement patterns, which correlate with specific emotional states. Affective computing aims to understand and respond to human emotions, allowing wearables not only to record information but also to offer personalized recommendations to enhance emotional well-being. For instance, a wearable may alert the user to elevated stress levels and suggest relaxation techniques or breathing exercises. The integration of artificial intelligence in these devices enables deeper analysis of the collected data, facilitating a more intuitive and adaptive interaction. In a world where emotional well-being is increasingly valued, affective computing wearables are positioned as innovative tools that can help individuals manage their emotions and improve their quality of life.
History: Affective computing was first conceptualized in 1995 by Rosalind Picard, who published the book ‘Affective Computing’. Since then, research in this field has grown, driving the development of technologies that enable machines to recognize and respond to human emotions. Over the years, advancements in biometric sensors and machine learning algorithms have allowed the creation of wearables that can capture and analyze users’ emotions in real-time.
Uses: Affective computing wearables are used in various areas, including mental health, personal well-being, and education. In mental health, they can help therapists monitor their patients’ emotional states. In personal well-being, these devices allow users to manage stress and anxiety through emotional feedback. In education, they can be used to adapt learning content based on students’ emotions.
Examples: Examples of affective computing wearables include devices like the ‘Empatica E4’, which measures heart rate and skin conductance, and applications that help users track their emotional state and provide personalized recommendations. Another example is ‘Feel’, a wrist-worn device that allows users to log their emotions throughout the day.
