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To address the deterioration of reading comprehension among older adults, this investigation presents a novel intelligent reading system that utilizes facial expression recognition to achieve multi-modal feature adaptation, grounded in immersion theory. The development of this system was motivated by the increasing prevalence of reading difficulties in the aging population, particularly concerning digital content comprehension and engagement. Through systematic web-based data mining and comprehensive literature review, contrasting imagery vocabularies and specific feature elements were compiled from existing product specifications and user reviews, encompassing data from multiple databases with a focus on both cognitive ergonomics and user interface design parameters specifically tailored for older adults. The methodology comprised three primary phases: an experimental reading simulation protocol was implemented with elderly participants, wherein Principal Component Analysis (PCA) was employed to identify and extract significant facial expression features during reading tasks; these features were then systematically catalogued in a comprehensive database, establishing robust correlations between expression patterns and immersion levels; this empirical foundation subsequently informed the construction of a mathematically-driven Kansei Engineering System, incorporating both cognitive and emotional parameters. The study culminated in an optimized design framework leveraging facial expression recognition neural networks for feature element modification, incorporating deep learning algorithms trained on age-specific facial expression datasets. Empirical results demonstrated that this integrated model significantly enhanced reading comprehension among older adults through multi-modal compensatory mechanisms, with participants showing significant improvements in both reading comprehension scores and sustained reading engagement. The proposed intelligent reading system design offers a practical solution for addressing age-related reading challenges through real-time facial expression monitoring and adaptive content presentation.

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Multi-Modal Feature Adaptation for Elderly Reading Enhancement: An Immersion-Based Facial Expression Recognition Approach

  • 会议论文

  • 发表日期：

    2026年

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  • Xu Huang

摘要

To address the deterioration of reading comprehension among older adults, this investigation presents a novel intelligent reading system that utilizes facial expression recognition to achieve multi-modal feature adaptation, grounded in immersion theory. The development of this system was motivated by the increasing prevalence of reading difficulties in the aging population, particularly concerning digital content comprehension and engagement. Through systematic web-based data mining and comprehensive literature review, contrasting imagery vocabularies and specific feature elements were compiled from existing product specifications and user reviews, encompassing data from multiple databases with a focus on both cognitive ergonomics and user interface design parameters specifically tailored for older adults. The methodology comprised three primary phases: an experimental reading simulation protocol was implemented with elderly participants, wherein Principal Component Analysis (PCA) was employed to identify and extract significant facial expression features during reading tasks; these features were then systematically catalogued in a comprehensive database, establishing robust correlations between expression patterns and immersion levels; this empirical foundation subsequently informed the construction of a mathematically-driven Kansei Engineering System, incorporating both cognitive and emotional parameters. The study culminated in an optimized design framework leveraging facial expression recognition neural networks for feature element modification, incorporating deep learning algorithms trained on age-specific facial expression datasets. Empirical results demonstrated that this integrated model significantly enhanced reading comprehension among older adults through multi-modal compensatory mechanisms, with participants showing significant improvements in both reading comprehension scores and sustained reading engagement. The proposed intelligent reading system design offers a practical solution for addressing age-related reading challenges through real-time facial expression monitoring and adaptive content presentation.