IoT-Based Wearable System for Early Detection of Parkinson\'s Disease Symptoms Using ESP32, MPU6050, and MAX30102
IoT-Based Wearable System for Early Detection of Parkinson\'s Disease Symptoms Using ESP32, MPU6050, and MAX30102
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Original abstract
Parkinson's disease is a progressive neurological disorder whose earliest motor symptom — a resting hand tremor — is frequently missed during short clinical appointments. This paper presents a low-cost, IoT-based wearable monitoring device designed to continuously detect and log physiological signals that may indicate early neurological abnormality. The system integrates an MPU6050 inertial measurement unit for three-axis motion and tremor analysis, and a MAX30102 optical pulse oximeter for heart rate (BPM) and blood oxygen saturation (SpO2) measurement. An ESP32 microcontroller performs on-device signal processing using a variance-threshold algorithm that identifies repetitive oscillation patterns in the clinically significant 3–6 Hz Parkinson's tremor frequency range. Multi-parameter decision logic combines motion and cardiac data before raising an alert, reducing false positives. Sensor data is transmitted wirelessly over Wi-Fi to a cloud dashboard accessible by doctors and caregivers. Local feedback is delivered via an OLED display and audible buzzer. Testing confirmed correct alert behaviour across all operating states, with approximately 98% Wi-Fi packet delivery rate and 8–10 hours of battery life per charge. The device is intended as a screening and monitoring tool — not a clinical diagnostic instrument — and all flagged results must be reviewed by a qualified medical professional.