AI Caretaker: An Intelligent Voice-Activated Support System for Dementia Care

Introduction Dementia care frequently requires continuous supervision, a demand that becomes difficult to meet when caregivers must balance outside responsibilities, particularly in home-based care, where unsupervised periods expose dementia patients to wandering, missed medication, loneliness, and a multitude of injuries. Existing tools often depend on manual patient input, an unrealistic expectation for individuals with impaired memory and judgment. This study presents the AI Caretaker, a voice-interactive support system that assists patients through natural conversation rather than commands or visual interfaces, and evaluates its technical feasibility. Methods Implemented as a tabletop device, the system continuously listens, interprets patient speech, classifies it as daily activity, medication intake, emergency, or casual conversation, logs detections to a relational database, and, when an emergency is identified, assigns a severity score and alerts the caregiver. The system was evaluated using 12 scripted scenarios spanning clear emergencies, nonemergency controls, ambiguous statements, and extended multiturn interactions, each executed across 10 trials, yielding 120 total trials and 440 turn-level statements processed through the complete pipeline. Results Speech recognition achieved a mean word accuracy of 94.6% (SD 15.8 percentage points). Binary emergency classification in clear scenarios was correct in 46 of 60 trials (76.7%) under a strict criterion requiring a correct classification on every statement (sensitivity: 26 of 30 trials, 86.7%; specificity: 20 of 30 trials, 66.7%), although every clear emergency trial generated at least one emergency flag. All 10 false-positive trials arose from emotional-distress statements and received uniformly low severity scores (median 10 of 100), indicating that the binary emergency flag and the continuous severity score were not calibrated with one another. Mean severity scores differentiated the three clear emergency types (79.0, 50.8, and 34.8 for fall with injury, progressive medical distress, and nighttime confusion, respectively). However, severity did not accumulate consistently across multiturn conversations, as the AI isolated the most recent statement rather than interpreting the dialogue as a whole. Mean end-to-end latency was 3.24 seconds against a 2.0-second target. Conclusions These findings support the feasibility of passive conversational monitoring in home-based dementia care and identify the flag-severity calibration needed to limit alert-fatigue risk, together with the temporal reasoning and latency improvements required before real-world deployment.