Jun-Wen Tan

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About

Jun-Wen Tan is a pioneering researcher in affective computing and human-computer interaction, with a focus on decoding emotional states through physiological signals. Their key research areas include facial electromyographic (EMG) activity analysis, age-related differences in emotion recognition, and the development of empathetic digital systems. Tan's most cited work, "Recognition of Intensive Valence and Arousal Affective States via Facial Electromyographic Activity in Young and Senior Adults" (2016, 28 citations), represents a significant contribution to the field by demonstrating how subtle facial muscle activations can reliably distinguish between high-intensity emotional states across different age groups. This study bridges the gap between human emotional experience and machine understanding, advancing the goal of creating computers that can genuinely empathize with users. By addressing the often-overlooked variable of age in affective computing, Tan's research has important implications for designing inclusive technologies that serve diverse populations, from younger digital natives to older adults. Their work lays crucial groundwork for next-generation human-computer interfaces that respond not just to commands, but to the emotional context of their users.