Parkinson’s disease tremor assessment: Leveragingsmartphones for symptom measurement

Abstract: Parkinson's disease (PD) is a progressive, chronic neurodegenerative disorder that impacts patients' quality of life. Hand tremor is a hallmark motor symptom of PD. However, current clinical tremor assessment methods are time-consuming and expensive and may not capture the full extent of tremor fluctuations. The built-in sensors in smartphones offer an accessible and cost-effective alternative for objective tremor assessment. This study presents a systematic approach to developing a quantitative algorithm for Parkinson's disease tremor assessment using Inertial Measurement Unit (IMU) data. This study begins with a comprehensive data visualisation and understanding phase, leading to the design decision to implement a multiple linear regression model for tremor severity prediction. The IMU data, collected from 10 patients, is pre-processed and normalised to ensure consistency and account for varying degrees of tremor severity. Feature extraction is conducted based on insights from literature, resulting in 16 unique features. These unique features are extracted for each of the acceleration and rotation rate data, resulting in 582 total features over both hands and all three tremor types. Recursive Feature Elimination with Cross-Validation (RFECV) is employed for feature selection, identifying the most relevant features contributing to tremor severity prediction. A multiple linear regression model is implemented and trained using the Leave-One-Out with Cross-Validation (LOOCV) method. The model's performance is evaluated resulting in a mean MSE of 0.88, a mean MAE of 0.69, and an R² of 0.88. The results indicate a strong correlation between predicted and actual tremor severity, suggesting the model's high validity. The selected features show a high correlation with the patient's MDS-UPDRS scores, further validating their relevance in predicting tremor severity. Greater results could be achieved, but sample size was the greatest limitation during this study. This study demonstrates the potential of using IMU data and multiple linear regression modelling for accurate PD tremor assessment within Mobistudy, contributing to the field of quantitative PD analysis.