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@article{192391,
        author = {Sarvesh Naik and Manashree Pakhare and Purva Karpe and Chetan Kawale},
        title = {A Review and Comparative Analysis of Smart Medicine Dispenser Technologies},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {1769-1774},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=192391},
        abstract = {This paper presents advanced robotics, machine vision, and IoT-based communication to ensure the safe, accurate, and personalized delivery of medicines in real time. Utilizing a microcontroller, line-following, and obstacle detection sensors, the robot moves independently through designated routes to each patient’s location. A facial recognition module confirms patient identity before dispensing the prescribed medicine, ensuring secure and error-free delivery. Cloud connectivity links the robot to the hospital database for real-time updates and tracking of medication schedules. The proposed research also includes a literature review of twelve referenced papers that collectively focus on IoT- and AI-enabled medication and pill dispensing systems that aim to improve adherence, safety, and independence for elderly or disabled patients by automating dose scheduling, identification, and delivery. They span hardware-software designs for smart dispensers and accessories, integrate features such as facial recognition and sensor fusion for patient identification and error reduction, and include human-centered and systematic reviews that evaluate usability, reliability, and workflow impact in real healthcare contexts.},
        keywords = {Smart medicine dispenser, facial recognition, Internet of Things (IoT), healthcare automation, medication adherence, autonomous mobile robot, patient identification, sensor-based dispensing, cloud connectivity, assistive healthcare technology.},
        month = {February},
        }