Intelligent IoT Healthcare Assistant for Alzheimer's Disease with Fall Detection Airbag

Internet of Things (IoT) and Artificial Intelligence in Healthcare

Alzheimer's disease is a progressive neurological condition that affects memory, recognition and daily activities, leading to risks such as missed medication, wandering, falls and difficulty recognizing unfamiliar people. This paper presents an Intelligent IoT Healthcare Assistant that combines IoT sensing, location tracking, computer vision and automated alerts into a single caregiver-oriented platform. The system uses an ESP32 microcontroller with heartbeat and temperature sensors for physiological monitoring, an NEO-6M GPS module for location tracking, and an ADXL345 accelerometer for fall detection. A laptop camera performs face recognition to distinguish known family members from unknown persons. A buzzer and prototype airbag mechanism provide a local emergency response, and medicine and food reminders support routine adherence. Sensor data and events are sent to a Flask-based web dashboard (HTML, CSS, JavaScript) with SQLite/Firebase and ThingSpeak for storage and visualization, while Python with OpenCV and NumPy handles the vision processing. The prototype shows how sensing, edge processing, communication and caregiver interaction can work together to enable continuous monitoring, fast emergency alerts and reduced caregiver burden.

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