ElderEase AR: Enhancing Elderly Daily Living with the Multimodal Large Language Model and Augmented Reality
Tianyu Song, Zhengyi Liu, Ruibin Zhao, Jie Fu · 2024
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.