Design of a Multi-Sensors Wearable System for Continuous Home Monitoring of People With Parkinson’s

Parkinson’s disease (PD) is a progressive neurological disorder that impairs motor control, balance, and cognitive function. Continuous monitoring of individuals with PD at home can help identify abnormal events at an early stage, reduce complications, and improve overall quality of life. This project proposes a multi-sensor wearable system for real-time monitoring of Parkinson’s patients. The system is built around an Arduino Mega and integrates multiple sensors, including a MEMS-based blood pressure (BP) sensor for BP measurement and fall detection, a pulse oximeter (SpO₂) for monitoring oxygen saturation, and an electrooculography (EOG) sensor for analyzing eye movements, currently at the demonstration stage. Sensor data are stored locally and processed using machine learning techniques to detect irregularities in vital signs and movement patterns. When a critical condition is detected, a buzzer provides an audible alert and an automated email notification, including GPS location, is sent to caregivers or medical personnel. All collected data are uploaded to an IoT platform using Python. The proposed system provides a cost-effective and real-time solution for remote monitoring of Parkinson’s patients, enabling timely intervention and improving patient safety.

Keywords:

·         Parkinson’s Disease

·         Multi-Sensor Wearable System

·         MEMS Accelerometer

·         Cloud Monitoring

·         Home Healthcare

·         Continuous Health Monitoring