Design of a Machine Learning-Assisted Wearable Accelerometer-Based Automated System for Studying the Effect of Dopaminergic Medicine on Gait Characteristics of Parkinson’s Patients

Abstract

In the last few years, the importance of measuring gait characteristics has increased tenfold due to their direct relationship with various neurological diseases. As patients suffering from Parkinson’s disease (PD) are more prone to a movement disorder, the quantification of gait characteristics helps in personalizing the treatment. The wearable sensors make the measurement process more convenient as well as feasible in a practical environment. However, the question remains to be answered about the validation of the wearable sensor-based measurement system in a real-world scenario. This paper proposes a study that includes an algorithmic approach based on collected data from the wearable accelerometers for the estimation of the gait characteristics and its validation using the Tinetti mobility test and 3D motion capture system. It also proposes a machine learning-based approach to classify the PD patients from the healthy older group (HOG) based on the estimated gait characteristics. The results show a good correlation between the proposed approach, the Tinetti mobility test, and the 3D motion capture system. It was found that decision tree classifiers outperformed other classifiers with a classification accuracy of 88.46%. The obtained results showed enough evidence about the proposed approach that could be suitable for assessing PD in a home-based free-living real-time environment.

Detection of Parkinson’s Disease from 3T T1 Weighted MRI Scans Using 3D Convolutional Neural Network

“…Moreover, Parkinson's Disease is progressive in nature, therefore, early detection and monitoring of the disease leads to improvement in the life of the patients. Also, as the aging population across the globe in increasing exponentially, a requirement for the development of suitable methods for the detection of Parkinson's Disease at a very early stage is indeed very important [1][2][3][4][5].…”

The Contribution of Machine Learning in the Validation of Commercial Wearable Sensors for Gait Monitoring in Patients: A Systematic Review

“…The number of studies related to the issue of validation on sensors used for patient monitoring has significantly increased since 2010, with a number of papers between 2017 and 2020, more than twice the number of papers between 2010 and 2017 (see Figure 2). Studies using machine learning as a validation method also became more numerous since 2010 [34][35][36]38,45,53,60,63,[68][69][70]77,[79][80][81]86,95,97], with a stable proportion compared to the total number of studies per year. Evolution of the number of papers considering the issue of validation for the use of commercial wearable devices in chronic disease monitoring, with a distinction between papers using machine learning (in red) or not (in blue).…”

Data Clustering Improves Siamese Neural Networks Classification of Parkinson’s Disease

“…Techniques that use sensor data are also employed in some studies for the diagnosis of PD. Aich et al [59] showed that decision trees outperformed other methods, obtaining an accuracy of 88.46% to study the effect of medicine on the e proposed DPRNN model achieved sensitivity, specificity, and precision of 84.84%, 91.81%, and 88.31%, respectively. Recently, [61] presented an automated gait differentiation procedure for the diagnosis of PD through a holistic, nonintrusive method that uses Vertical Ground Reaction Force (VGRF).…”