MemPal: Leveraging Multimodal AI and LLMs for Voice-Activated Object Retrieval in Homes of Older Adults
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arXiv
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| Format: | Preprint |
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2025
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| author | Maniar, Natasha Chan, Samantha W. T. Zulfikar, Wazeer Ren, Scott Xu, Christine Maes, Pattie |
| author_facet | Maniar, Natasha Chan, Samantha W. T. Zulfikar, Wazeer Ren, Scott Xu, Christine Maes, Pattie |
| contents | Older adults have increasing difficulty with retrospective memory, hindering their abilities to perform daily activities and posing stress on caregivers to ensure their wellbeing. Recent developments in Artificial Intelligence (AI) and large context-aware multimodal models offer an opportunity to create memory support systems that assist older adults with common issues like object finding. This paper discusses the development of an AI-based, wearable memory assistant, MemPal, that helps older adults with a common problem, finding lost objects at home, and presents results from tests of the system in older adults' own homes. Using visual context from a wearable camera, the multimodal LLM system creates a real-time automated text diary of the person's activities for memory support purposes, offering object retrieval assistance using a voice-based interface. The system is designed to support additional use cases like context-based proactive safety reminders and recall of past actions. We report on a quantitative and qualitative study with N=15 older adults within their own homes that showed improved performance of object finding with audio-based assistance compared to no aid and positive overall user perceptions on the designed system. We discuss further applications of MemPal's design as a multi-purpose memory aid and future design guidelines to adapt memory assistants to older adults' unique needs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_01801 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MemPal: Leveraging Multimodal AI and LLMs for Voice-Activated Object Retrieval in Homes of Older Adults Maniar, Natasha Chan, Samantha W. T. Zulfikar, Wazeer Ren, Scott Xu, Christine Maes, Pattie Human-Computer Interaction F.2.2, I.2.7 Older adults have increasing difficulty with retrospective memory, hindering their abilities to perform daily activities and posing stress on caregivers to ensure their wellbeing. Recent developments in Artificial Intelligence (AI) and large context-aware multimodal models offer an opportunity to create memory support systems that assist older adults with common issues like object finding. This paper discusses the development of an AI-based, wearable memory assistant, MemPal, that helps older adults with a common problem, finding lost objects at home, and presents results from tests of the system in older adults' own homes. Using visual context from a wearable camera, the multimodal LLM system creates a real-time automated text diary of the person's activities for memory support purposes, offering object retrieval assistance using a voice-based interface. The system is designed to support additional use cases like context-based proactive safety reminders and recall of past actions. We report on a quantitative and qualitative study with N=15 older adults within their own homes that showed improved performance of object finding with audio-based assistance compared to no aid and positive overall user perceptions on the designed system. We discuss further applications of MemPal's design as a multi-purpose memory aid and future design guidelines to adapt memory assistants to older adults' unique needs. |
| title | MemPal: Leveraging Multimodal AI and LLMs for Voice-Activated Object Retrieval in Homes of Older Adults |
| topic | Human-Computer Interaction F.2.2, I.2.7 |
| url | https://arxiv.org/abs/2502.01801 |