[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-424660-105":3,"detail-sidebar-cat-0-en-105":80,"doc-detail-424660-en":130},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":73,"head_meta":75,"extra_data":77,"updated_unix":79},105,"en","connect-powered-by-brain-ai-driven-behavior-analysis-for-person-centered-dementia-programming","CONNECT POWERED BY BRAIN - AI-Driven Behavior Analysis for Person-Centered Dementia Programming","","CONNECT powered by BRAIN is an AI-driven platform designed to personalize and streamline person-centered activity programming for people living with dementia, reducing responsive behaviors while supporting meaning and purpose. The system uses a curated library of 1,000+ dementia-appropriate activities across 12 types, then tailors recommendations to cognitive profile and interests, scoring fit from 0–100 and showing the top seven to limit decision fatigue. 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POWERED BY BRAIN: HARNESSING  \nAI-DRIVEN BEHAVIOR ANALYSIS TO FOSTER PERSON-CENTERED DEMENTIA PROGRAMMING  \nMichael Skrajner1, Gregg Gorzelle1, John Zeisel1, Sucheendra Palaniappan2, and Vivek Saxena2, 1. Hopeful Aging, Winchester, Massachusetts, United States, 2. Vikasietum Tecknology LLC, Plano, Texas, United States  \nPerson-centered activity programming can reduce responsive behaviors and support meaning and purpose for persons living with dementia (PLwD; Chenoweth et al., 2009; Lee et al., 2012) . Yet tailoring activities to an individual’s cognitive level and interests typically depends on front-line staff, a time-intensive process often infeasible in long-term care. Hopeful Aging, through an NIA-funded SBIR Phase II project, is developing CONNECT powered by BRAIN, anAI-driven platform that personalizes and streamlines activity programming. The system draws from a growing, curated library of over 1,000 dementia-appropriate activities across 12 types. Based on a PLwD’s cognitive profile and interests, the platform generates personalized recommendations, each scored for fit (0–100). To minimize decision fatigue, the top seven recommendations appear on the first screen. Recommendations then adapt dynamically through AI-based behavioral engagement analysis derived from session recordings (with consent/assent). Engagement is quantified using behavioral markers—verbal communication, visual attention, pleasure, and hand and head gestures—and compared against the individual’s evolving baseline to refine subsequent suggestions. In Phase I, 22 participants (M age=83.7; 82% female; 73% Caucasian) tested an alpha prototype. A subsequent Model Training Study in the  \n1114 Innovation in Aging, 2025, Vol. 9, No. S2  \nongoing Phase II project with 26 participants (M age=84.5; 76% female; 68% Caucasian) refined behavioral analyses. Post-training F1 values were: Verbal Communication=90%; Visual Attention=87%; Pleasure=86%; Hand Gestures=82%; Head Gestures=76%, with a mean improvement of + 9% . Findings demonstrate the feasibility of automated AI engagement analyses for adaptive, person-centered activity programming. A forthcoming cluster-randomized trial will examine impacts on quality of life, responsive behaviors, and depression.","cbCaiuCBoizBsatl","https://ap.wps.com/l/cbCaiuCBoizBsatl","pdf",1917607,"English","# Abstract\n## Personalized activity recommendations\n## AI-based engagement analysis\n## Phase I and Phase II studies\n## Outcomes and upcoming trial","[{\"question\":\"How does CONNECT powered by BRAIN personalize dementia activity recommendations?\",\"answer\":\"It matches each person’s cognitive profile and interests to a curated library of dementia-appropriate activities, generating recommendations scored for fit (0–100) and displaying the top seven to reduce decision fatigue.\"},{\"question\":\"What data does the platform use to adapt activities over time?\",\"answer\":\"It uses AI-based behavioral engagement analysis derived from session recordings collected with consent/assent, quantified through behavioral markers such as verbal communication, visual attention, pleasure, and hand/head gestures.\"},{\"question\":\"What evidence is reported from the Phase I and Phase II work?\",\"answer\":\"An alpha prototype was tested in Phase I with 22 participants, followed by Model Training in ongoing Phase II with 26 participants, which refined engagement analyses and reported high post-training F1 values across engagement markers.\"}]","CONNECT POWERED BY BRAIN - AI-Driven Behavior Analysis for Person-Centered Dementia Programming | PDF",1790634973]