Jing-ya Hao and Ying Wu
Internet of Things, emotional perception, user experience, adaptive interaction, high confidence experience design, edge computing
User emotional states play a critical role in shaping interaction quality and overall satisfaction with mobile applications. This paper proposes an IoT-based framework for real-time user emotion perception and adaptive APP interface optimisation, aimed at enhancing high-confidence experience design. By integrating wearable and environmental sensors, the system continuously captures physiological and behavioural data to infer emotional states using lightweight deep learning models. Based on these insights, the APP dynamically adjusts the visual layout, interaction process, and content delivery strategy to match the user’s emotional background, thereby reducing the “emotional friction” caused by interface mismatch (i.e., the negative experience generated by users due to the lack of coordination between emotions and interface interaction), improving users’ trust in the adaptive interface (i.e., users’ subjective recognition of system response and recommendation reliability), and achieving high confidence experience design (referring to high reliability user experience design under the dual guarantee of emotionalisation and personalisation). The proposed system employs edge computing for real-time responsiveness and ensures data integrity through secure communication protocols. Experimental validation demonstrates improved user engagement, reduced emotional friction, and increased trust in the adaptive interface. This approach presents a novel pathway toward personalised and emotionally intelligent user experience systems in IoT-connected environments.
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