Empowering cognitive disabilities in transit: an explainable, emotion-aware ITS framework
Rattachement africain : sa, us, Égypte. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
People with disabilities need ongoing support and a balanced lifestyle. Smart cities like NEOM are emerging worldwide. The Saudi government has implemented several disability accessibility programs in public spaces and transportation. This article addresses a critical yet often neglected challenge: accurately recognizing and interpreting facial emotions in individuals with cognitive disabilities to foster better social integration. Current emotion detection systems frequently overlook the unique needs of this demographic (slower response times, difficulty interpreting subtle cues, and varied attention spans) and provide limited transparency, undermining trust and hindering real-time applicability in complex, dynamic contexts. To overcome these limitations, we present a novel, comprehensive framework that utilizes the Internet of Things, fog computing, and advanced You Only Look Once (YOLO)v8-based deep learning models. Our approach incorporates adaptive feedback mechanisms to tailor interactions to each user’s cognitive profile, ensuring accessible, user-centric guidance in diverse real-world scenarios. Besides, we introduce EigenCam-based explainability techniques, which offer intuitive visualizations of the decision-making process, enhancing interpretability and trust for both users and caregivers. Seamless integration with assistive technologies, including augmented reality devices and mobile applications, further supports real-time, on-the-go interventions in therapeutic and educational contexts. Experimental results on benchmark datasets (RAF-DB, AffectNet, and CK+48) demonstrate the framework’s robust performance, achieving up to 95.8% accuracy and excelling under challenging conditions. The EigenCam outputs confirm that the model’s attention aligns with meaningful facial features, reinforcing the system’s interpretability and cultural adaptability. By delivering accurate, transparent, and context-aware emotion recognition tailored to cognitive disabilities, this research sets a promising step for inclusive artificial intelligence (AI)-driven solutions, ultimately promoting independence, reducing stigma, and improving quality of life.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Empowering cognitive disabilities in transit: an explainable, emotion-aware ITS framework
- Date Crossref
- 04/11/2025
- Éditeur
- PeerJ
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Taibah University pays non établi dans la noticeUniversité ou école supérieure
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King Salman Center for Disability Research pays non établi dans la noticeOrganisation à but non lucratif
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Taif University pays non établi dans la noticeUniversité ou école supérieure
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University of Louisville Bioengineering Department pays non établi dans la noticeUniversité ou école supérieure
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Mansoura University Mansoura University, Égypte (code pays fourni par la source)Université ou école supérieure
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College of Computer Science and Engineering Department of Computer Science pays non établi dans la noticeUniversité ou école supérieure
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Applied College Computer Science and Information Department pays non établi dans la noticeUniversité ou école supérieure
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College of Engineering Department of Electrical Engineering pays non établi dans la noticeUniversité ou école supérieure
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Faculty of Engineering Mansoura University, Égypte (pays nommé en fin d’affiliation)Université ou école supérieure
Taibah University, King Salman Center for Disability Research et Taif University, avec 6 autres affiliations. Pays d’affiliation : Égypte.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.