From 'What' to 'How' and 'Why': Sharing LLM-Generated Retrospective Summaries of Older Adults' Passive Tracking Data with Remote Family Members
arXiv:2606.03876v1 Announce Type: cross Abstract: With the growing prevalence of modern ubiquitous computing technologies, multi-modal tracking systems hold promise for providing timely awareness and reassurance to stakeholders such as remote family members (RFMs) of older adults, who play a central role in care coordination. However, combining heterogeneous data streams into high-level, meaningful content - such as retrospective summaries - remains challenging. While recent work has demonstrated the promise of large language models (LLMs) for interpreting multi-modal tracking data, less atten
The proliferation of ubiquitous computing and advanced LLMs is making it feasible to derive meaningful insights from complex, heterogeneous data streams, especially in elder care.
This development highlights the growing capability of AI, specifically LLMs, to translate raw data into actionable, high-level intelligence for critical human needs like care coordination.
The ability to generate coherent 'retrospective summaries' from passive tracking data fundamentally changes how remote family members can engage with and understand the well-being of older adults, moving beyond raw data alerts.
- · Elder care technology providers
- · Families with remote elderly relatives
- · LLM developers
- · Healthcare data analytics firms
- · Traditional manual monitoring services
- · Overwhelmed family caregivers without AI assistance
- · Companies relying on simple data alerts
Remote family members gain deeper, more nuanced understanding of their elderly relatives' daily lives.
Increased adoption of multi-modal tracking systems powered by LLMs in elder care, leading to better early intervention and proactive care.
LLMs become an embedded standard in personal health and wellness ecosystems, expanding beyond elder care to other demographic segments requiring continuous monitoring and interpretive summaries.
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Read at arXiv cs.AI