Wearable Sensors and Machine Learning Fusion for Parkinson's Disease Assessment

Parkinson's disease (PD) causes physical activity loss, tremors, stiffness, and coordination-related issues. The state-of-the-art studies utilize complex experimental testbeds for data acquisition and consequent application of Machine learning (ML) methods for the PD diagnosis. However, their performance is still the subject to improve. This research aims to distinguish PD from healthy control (HC) using four wearable sensors and ML, where two sensors are placed on each hand, wrist, and dorsum. The dataset of 54 patients, 21 with HC and 33 with PD, was collected in a hospital while the patient performed 11 exercises under the supervision of neurologists. The dataset was preprocessed and segmented into overlapping windows, and ML algorithms were used in terms of analysis of frequency ranges, features, and exercises. This study demonstrates a simple and accurate method for detecting PD in clinical or home settings with an average of 0.936
$f 1_{{micro }}$
and 0.935 ROC while the performance of ‘best’ exercises achieves 100%
${f}1_{micro}$
.