Precision Analysis and Calibration of the MPU6050 Sensor for Tremor Measurement in Parkinson's Disease

Abstract

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor symptoms such as resting tremors, which can significantly impact patients' quality of life. The ELENA Project aims to develop an Internet of Things (IoT)-based system for monitoring motor symptoms in individuals with PD. To achieve this, it is essential to use precise and calibrated measurement sensors. This study analyzes the accuracy of the MPU6050 sensor, a low-cost accelerometer and gyroscope, through a process of calibration, experimental testing, and validation. A six-position calibration was implemented to correct the sensor offset and improve measurement reliability. Subsequently, tests were conducted using a uniaxial vibration table, where the sensor's response to controlled frequencies and amplitudes was evaluated. Additionally, the results obtained were compared with those of an Apple Watch, a commercially available device, to assess the accuracy of the MPU6050 in tremor detection. The results showed that the MPU6050, after calibration, was capable of accurately recording simulated tremors in the laboratory, with an average error of less than 1% in most measurements. This confirms the importance of an appropriate calibration process to ensure reliable measurements in tremor monitoring systems for PD patients. Finally, the experimental validation suggests that the MPU6050 can be used as a low-cost alternative within the ELENA Project, facilitating the detection and remote monitoring of motor symptoms in individuals with Parkinson’s disease.