Advanced Wearable Sensors for Early Parkinson's Disease Tremor Detection using Accelerometers and Gyroscopes

This paper presents the design and implementation of a wearable hand tremor detection system to analyze and quantify hand tremors in real time. The system integrates an Arduino UNO microcontroller, an MPU-6050 sensor for measuring 3-axis acceleration and angular velocity, and an ESP8266 module for wireless data transmission. Participants performed three tasks—hand opening, writing, and holding—with the device mounted on their dominant hand, ensuring precise measurement of motion parameters. Data collected in real time was filtered using a moving average algorithm and analyzed using statistical and frequency domain techniques. The results were visualized to identify tremor patterns and severity. The system’s lightweight and non-invasive design ensures user comfort and ease of use. Validation through controlled experiments demonstrated its reliability, with results aligning closely with clinical benchmarks. This work highlights the potential of affordable, wearable devices in biomedical applications, particularly in monitoring and assessing tremor-related conditions.