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Devices that monitor seniors for safety are appealing to worried loved ones and underresourced home care agencies. It was January of 2026 in North Seattle, and my 86-year-old father was struggling to move around his house. "I'm stumbling around here," my 86-year-old father told a guest in his home this past January. "Oooh, ooh, careful," the guest replied. "Yeah, I almost fell down."

Niko is a robotic lift for people with limited mobility that doesn't require a caregiver's help ReviMo demonstrated the lift and transfer system at CES 2026. A startup called ReviMo has developed a robotic system that provides a way for people with limited mobility to lift and transfer themselves -- like from a bed to a wheelchair, or to the toilet -- without the assistance of a caregiver. ReviMo's Niko has two sets of arms: one that forms a scooping seat that slides underneath the person to lift them up, and the other encircling their torso and providing a backrest. It can be operated both by remote and using the controls on its dashboard. Niko in its current iteration can carry up to 250 pounds, but the team says it's working on a version that can support up to 400 pounds.

As more and more age tech hits the market, I've been testing the most innovative gadgets for older folks and caregivers. Age tech is a rapidly growing category focused on remote caregiving, improving quality of life, and enabling older folks to stay in their own homes for longer. The US Census Bureau says around 16 million elders (over 65) live alone. While the majority are healthy, with family and friends nearby, many lack support and may be battling physical and mental decline. Whether you're getting older or trying to help an aging loved one, there's an increasingly diverse range of gadgetry to choose from, but as a nascent category, it's tricky to know what will help.

Abstract--General-purpose large language models (LLMs) have demonstrated remarkable generative and reasoning capabilities but remain limited in healthcare and caregiving applications due to two key deficiencies: factual unreliability and a lack of empathetic communication. These shortcomings pose significant risks in sensitive contexts where users, particularly nonprofessionals and caregivers, seek medically relevant guidance or emotional reassurance. T o address these challenges, we introduce a Direct Preference Optimization (DPO)-based alignment framework designed to improve factual correctness, semantic coherence, and human-centric qualities such as empathy, politeness, and simplicity in caregiver-patient dialogues. Our approach fine-tunes domain-adapted Large Language Models (LLMs) using pairwise preference data, where preferred responses reflect supportive and accessible communication styles while rejected ones represent prescriptive or overly technical tones. Empirical evaluations across multiple open and proprietary LLMs show that our DPO-tuned models achieve higher semantic alignment, improved factual accuracy, and stronger human-centric evaluation scores compared to baseline and commercial alternatives such as Google's medical dialogue systems. These improvements demonstrate that preference-based alignment offers a scalable and transparent pathway toward developing trustworthy, empathetic, and clinically informed AI assistants for caregiver and healthcare communication. Caring for individuals with chronic or neuro-degenerative conditions such as Alzheimer's disease and dementia requires not only clinical coordination but also constant emotional resilience. Family caregivers and care partners often become the primary interpreters of medical information, navigating complex treatment decisions, behavioral changes, and communication challenges on a daily basis. LLMs have rapidly become integrated into everyday life. They can explain complex ideas in plain language, adjust to a user's tone, and offer a sense of understanding that static websites cannot. For caregivers seeking clear, kind, and quick answers, these systems can feel like an always-available companion in moments of doubt or stress.