Abstract: Alzheimer's disease is a progressive neurodegenerative condition that significantly affects cognitive abilities, resulting in memory impairment, confusion, and challenges in carrying out everyday tasks. A significant hurdle for individuals with Alzheimer’s is their difficulty in recognizing family members and caregivers, which can lead to emotional distress and increased dependency. Moreover, the failure to remember to take prescribed medications exacerbates their health issues. To tackle these challenges, this research introduces a laptop-based assistive system that combines deep learning facial recognition technology with a medication reminder feature, aimed at improving the quality of life for those with Alzheimer’sThe system utilizes a laptop's webcam to capture real-time facial images, which are then analyzed by a deep learning model to identify familiar faces. Upon recognizing a known individual, the system announces their name audibly, aiding the patient in recalling and recognizing their loved ones. Furthermore, it includes a medication reminder function that notifies patients at specific times to promote adherence to their prescribed treatment regimen. This solution is designed to be standalone and user-friendly, negating the need for additional IoT devices, thus making it suitable for home environments.This research outlines the design, implementation, and assessment of the assistive system, focusing on its accuracy, usability, and effects on patient care. The facial recognition component employs Convolutional Neural Networks (CNNs) for accurate identification, while the medication reminder utilizes a structured scheduling system with audio-visual alerts. Experimental findings demonstrate that the system effectively aids patients in recognizing individuals and following their medication schedules.By merging AI-powered facial recognition with intelligent reminders, this assistive technology seeks to promote patient independence, alleviate caregiver stress, and enhance overall well-being. The study also considers potential future enhancements, including improved emotion detection capabilities.
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DOI:
10.17148/IJARCCE.2025.14492
