DCAEF v2.0 in Action
A Dignity-Centered Evaluation of
P.A.L.S.™ Technology
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P.A.L.S. is a LiDAR-based safety system for older adults who live at home with pets. It detects falls and pet-related trip hazards in real time, alerts designated care partners, and operates entirely without cameras or cloud storage. Designed by a gerontologist with fifteen years of direct practice across hospice, memory support, and assisted living, P.A.L.S. centers the human-animal bond as a source of strength — protecting independence rather than replacing it.
P.A.L.S.™ is evaluated here using the Dignity-Centered AI Evaluation Framework (DCAEF) v2.0 — a six-pillar standard developed by The Gerontechnology Group to assess whether an AI or technology product genuinely serves older adults with dignity, safety, and equity. This evaluation shows what a real DCAEF assessment produces: a structured scorecard, narrative findings, evaluator transparency, and a clear development roadmap.
LiDAR uses safe, invisible light pulses to map a room in three dimensions — sensing where people, pets, and furniture are without cameras or video. Think of it as spatial awareness without surveillance.
P.A.L.S.™ positions the older adult as the primary decision-maker throughout. The system detects and alerts without overriding the user's choices. Camera-free design preserves bodily dignity. Minor gap: fuller consent language during onboarding recommended.
Fall and trip-hazard detection addresses a genuine, documented harm risk in the home setting. LiDAR-based sensing eliminates the privacy harms associated with camera systems. Real-time alerting to care partners reduces response time. Opportunity: formalized clinical validation protocol in development.
The system's purpose and detection logic are communicated clearly to end users. The Alexa skill provides verbal feedback. Opportunity area: plain-language explanation of how the LiDAR algorithm distinguishes pets from persons, and what triggers an alert, would strengthen user trust and informed consent.
No images, no video — LiDAR point-cloud data cannot identify individuals visually. Edge processing keeps data local. A formal data retention and deletion policy, documented and user-accessible, would close the remaining gap and is recommended for v3 development.
The $399 hardware price point with a $9.99/month Alexa skill creates a meaningful access pathway compared to institutional alternatives. Bilingual (English/Spanish) interface in development. Opportunity: formal accessibility audit for users with low vision or limited dexterity, and an affordability pathway for lower-income households.
P.A.L.S.™ was designed from the ground up by a gerontologist — older adults are the primary intended beneficiary, not an afterthought. The product addresses a gap in the existing fall-prevention market by centering the human-animal bond as a protective factor rather than a risk to manage. Minor opportunity: structured co-design sessions with older adult users recommended for v3.
"My mom once tripped over her dog, Suzi. When we got one of the first P.A.L.S. prototypes, I saw it work with my own eyes — and I know this will help so many families once it's out in the world."
— Andrea Smith, Pilot Participant (Mom & Suzi), Dallas, TX
This evaluation was conducted by The Gerontechnology Group, the same organization that developed P.A.L.S.™. In the interest of full transparency, this conflict of interest is disclosed here. The DCAEF framework was applied with the same rigor used in all GTG organizational evaluations. Scoring intentionally identifies genuine gaps and opportunities — not to present a perfect system, but to model what honest, dignity-centered technology evaluation looks like. External peer review by GTG's Medical and Academic Advisory Board is ongoing.
— Dr. Melissa Mansfield, PhD, NAPG-C · Founder & CEO, The Gerontechnology Group
Develop clear, plain-language informed consent documentation for onboarding that covers data use, alert logic, and user rights — available in English and Spanish.
Partner with academic medical centers to conduct formal efficacy studies on fall and trip-hazard detection accuracy across diverse home environments.
Publish a plain-language explanation of how the LiDAR system differentiates pets from persons and what thresholds trigger an alert — for users, families, and clinicians.
Integrate structured co-design with older adult users — particularly those who are pet owners and aging in place — into the v3 hardware and app development process.
P.A.L.S.™ shows that a small, mission-driven gerontechnology company — built on peer-reviewed research and clinical expertise — can produce a system that meets or exceeds the DCAEF Exemplary threshold. The remaining gaps are honest, documented, and addressed in the v3 development roadmap.