AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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You can expand the Abstract, Links and BibTex record for each paper.
2025
Song, T.; Liu, Z.; Zhao, R.; Fu, J.
ElderEase AR: Enhancing Elderly Daily Living with the Multimodal Large Language Model and Augmented Reality Proceedings Article
In: ICVRT - Proc. Int. Conf. Virtual Real. Technol., pp. 60–67, Association for Computing Machinery, Inc, 2025, ISBN: 979-840071018-6 (ISBN).
Abstract | Links | BibTeX | Tags: Age-related, Assisted living, Augmented Reality, Augmented reality technology, Daily Life Support, Daily living, Daily-life supports, Elderly, Elderly users, Independent living, Independent living systems, Language Model, Modeling languages, Multi agent systems, Multi-modal, Multimodal large language model
@inproceedings{song_elderease_2025,
title = {ElderEase AR: Enhancing Elderly Daily Living with the Multimodal Large Language Model and Augmented Reality},
author = {T. Song and Z. Liu and R. Zhao and J. Fu},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105001924899&doi=10.1145%2f3711496.3711505&partnerID=40&md5=4df693735547b505172657a73359f3ca},
doi = {10.1145/3711496.3711505},
isbn = {979-840071018-6 (ISBN)},
year = {2025},
date = {2025-01-01},
booktitle = {ICVRT - Proc. Int. Conf. Virtual Real. Technol.},
pages = {60–67},
publisher = {Association for Computing Machinery, Inc},
abstract = {Elderly individuals often face challenges in independent living due to age-related cognitive and physical decline. To address these issues, we propose an innovative Augmented Reality (AR) system, “ElderEase AR”, designed to assist elderly users in their daily lives by leveraging a Multimodal Large Language Model (MLLM). This system enables elderly users to capture images of their surroundings and ask related questions, providing context-aware feedback. We evaluated the system’s perceived ease-of-use and feasibility through a pilot study involving 30 elderly users, aiming to enhance their independence and quality of life. Our system integrates advanced AR technology with an intelligent agent trained on multimodal datasets. Through prompt engineering, the agent is tailored to respond in a manner that aligns with the speaking style of elderly users. Experimental results demonstrate high accuracy in object recognition and question answering, with positive feedback from user trials. Specifically, the system accurately identified objects in various environments and provided relevant answers to user queries. This study highlights the powerful potential of AR and AI technologies in creating support tools for the elderly. It suggests directions for future improvements and applications, such as enhancing the system’s adaptability to different user needs and expanding its functionality to cover more aspects of daily living. © 2024 Copyright held by the owner/author(s).},
keywords = {Age-related, Assisted living, Augmented Reality, Augmented reality technology, Daily Life Support, Daily living, Daily-life supports, Elderly, Elderly users, Independent living, Independent living systems, Language Model, Modeling languages, Multi agent systems, Multi-modal, Multimodal large language model},
pubstate = {published},
tppubtype = {inproceedings}
}
Elderly individuals often face challenges in independent living due to age-related cognitive and physical decline. To address these issues, we propose an innovative Augmented Reality (AR) system, “ElderEase AR”, designed to assist elderly users in their daily lives by leveraging a Multimodal Large Language Model (MLLM). This system enables elderly users to capture images of their surroundings and ask related questions, providing context-aware feedback. We evaluated the system’s perceived ease-of-use and feasibility through a pilot study involving 30 elderly users, aiming to enhance their independence and quality of life. Our system integrates advanced AR technology with an intelligent agent trained on multimodal datasets. Through prompt engineering, the agent is tailored to respond in a manner that aligns with the speaking style of elderly users. Experimental results demonstrate high accuracy in object recognition and question answering, with positive feedback from user trials. Specifically, the system accurately identified objects in various environments and provided relevant answers to user queries. This study highlights the powerful potential of AR and AI technologies in creating support tools for the elderly. It suggests directions for future improvements and applications, such as enhancing the system’s adaptability to different user needs and expanding its functionality to cover more aspects of daily living. © 2024 Copyright held by the owner/author(s).