Detecting the Parkinson's Disease Through the Simultaneous Analysis of Data From Wearable Sensors and Video

Machine Learning (ML) algorithms is an emerging tool helping automatically solve detection and classification tasks in myriads of applications. ML is extensively used in medical applications and, in particular, for detecting the Parkinson's Disease (PD). However, the inference is typically made relying on a single data source. This work explores the results of combining more than one data source type for the diagnosis of PD. Data from the Commercial Off-the-Shelf (COTS) wearable sensors (accelerometer, gyroscope and magnetometer), along with video recordings from 83 patients completing a series of 15 tasks was analyzed with the use of ML methods. Statistical and frequency features were extracted and used to train Random Forest and XGBoost Classifiers. We investigate two use cases on classifying (i) healthy individuals and individuals with the PD, and (ii) PD and essential tremor. The experiment showed that using both the data from wearable sensors and video provided the increase of f1 score up to 18% for differentiating between healthy and PD classes and 21% for differentiating PD and essential tremor classes. At the same time, usage of COTS and ML opens wide vista for patient driven data acquisition and healthcare.