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Medication non-adherence remains a persistent global health bottleneck, contributing to nearly 50% of treatment failures and elevating preventable hospitalizations. Conventional smart dispensers often lack robust multi-patient access control, fail to verify physical drug release, or rely on loose-pill architectures prone to cross-contamination and mechanical jamming. This paper presents PillBot, an automated, multi-profile Internet of Things (IoT) medication adherence system designed for multi-patient domestic healthcare. The electromechanical framework utilizes four independent circular medicine trays, each partitioned into seven distinct compartments. Tray positioning and rotational indexing are driven by 28BYJ-48 5V stepper motors via ULN2003 drivers, calibrated with A3144 Hall-effect sensors and neodymium magnets to eliminate positional drift. The processing pipeline is governed by dual ESP32 microcontrollers, incorporating an integrated 3.5-inch 480×320 LVGL-driven touchscreen display for intuitive on-device scheduling alongside a battery-backed DS3231 Real-Time Clock (RTC) module to maintain offline scheduling integrity. Biometric security is enforced via an R307 optical fingerprint scanner, restricting medicine access strictly to authorized patient profiles. To prevent dispensing errors, released medication passes through a guided funnel assembly into a collection cup, where physical delivery is validated via dual-stage verification combining an optical IR break-beam sensor and an HX711-interfaced load cell. Multimodal alarms incorporate a MAX98357A I2S Class-D amplifier driving a 5W speaker alongside an active buzzer, while onboard Wi-Fi connectivity dispatches automated alerts to remote caregivers in the event of missed doses. Experimental testing confirms that PillBot provides a dependable, secure, and verifiable hardware-software solution for domestic medication management. Keywords—Internet of Things, medication adherence, biometric authentication, ESP32, automated dispenser, stepper motor, Hall-effect sensor, healthcare technology.
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Danapla et al. (2026) studied this question.
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