Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Iwendi, Celestine; Uwah, Salome Enoshi; Makinde, Babatope; Sharma, Vandana; Akande, Afeez; Orumwense, Austin Title A name given to the resource Emotionally Adaptive AI Companions for Supporting Routine Management in Autistic Adolescents Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived 2026 2nd International Conference on Cognitive Computing in Engineering, Communications, Sciences and Biomedical Health Informatics, IC3ECSBHI 2026;pp.795-800 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/IC3ECSBHI67834.2026.11469147" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/IC3ECSBHI67834.2026.11469147</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105038110049?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105038110049?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Iwendi C., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Uwah S.E., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Makinde B., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Sharma V., Christ University, Dept. of Computer Science, India; Akande A., University of Greater Manchester, Centre of Intelligence of Things, Bolton, United Kingdom; Orumwense A., Astrosec Ltd., Manchester, United Kingdom Description An account of the resource Autistic Adolescents usually experience difficulties in the management of emotions, routine transitions and social cue interpretation. Many existing tools that aim to fill in the gap are often non-personalise, static or lack real-time responsiveness in handling these challenges. This study conceptualises and empirically validates a prototype of an emotionally adaptive AI companion that focuses on reducing stress due to routine transition, emotional regulation and social cue interpretation while increasing personalised management by providing contextual support. A quasi-experimental, mixed methods design is adopted. The core of this system conducts facial multimodal emotion recognition through facial expression and simulated voice tone using transfer learning across three CNN architectures (ResNet-18, MobileNetV2, and EfficientNet-B0) as comparison tests. The resulting emotion output is feeds into a contextual engine for real-time personalised interventions which can also be continuously improved through critical feedback-in-the-loop control architecture based on caregiver logs. The key model trade-offs are validated, the findings established that ResNet18 possesses the highest accuracy of 48%, EfficientNet-B0 with a superior F1 Score of 0.31 and MobileNetV2 proves to be efficient but slightly lower performance compared to other architectures. Simulated user feedback validation resulted in high preliminary acceptability, as high as 87.5% acceptability for an intervention like 'Reassurance'. This validated the utility of this responsive system. This transfer-learning based, multi-modal pipeline is robust. The results of the comparative analysis uncovered a very profound and instructive trade-off between the complexity of models, their efficiency, and performance metrics relating to accuracy versus the F1-score. 2026 IEEE. Subject The topic of the resource Addaptive system; Affective computing; autism disorder; contextaware systems; emotion recognition; human-in-the-loop; multimodal sensing; reinforcement learning; transition support Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155691-4; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online