Machine Learning Classifiers to Evaluate Data From Gait Analysis With Depth Cameras in Patients With Parkinson’s Disease

Abstract

IntroductionThe assessments of the motor symptoms in Parkinson’s disease (PD) are usually limited to clinical rating scales (MDS UPDRS III), and it depends on the clinician’s experience. This study aims to propose a machine learning technique algorithm using the variables from upper and lower limbs, to classify people with PD from healthy people, using data from a portable low-cost device (RGB-D camera). And can be used to support the diagnosis and follow-up of patients in developing countries and remote areas.MethodsWe used Kinect®eMotion system to capture the spatiotemporal gait data from 30 patients with PD and 30 healthy age-matched controls in three walking trials. First, a correlation matrix was made using the variables of upper and lower limbs. After this, we applied a backward feature selection model using R and Python to determine the most relevant variables. Three further analyses were done using variables selected from backward feature selection model (Dataset A), movement disorders specialist (Dataset B), and all the variables from the dataset (Dataset C). We ran seven machine learning models for each model. Dataset was divided 80% for algorithm training and 20% for evaluation. Finally, a causal inference model (CIM) using the DoWhy library was performed on Dataset B due to its accuracy and simplicity.ResultsThe Random Forest model is the most accurate for all three variable Datasets (Dataset A: 81.8%; Dataset B: 83.6%; Dataset C: 84.5%) followed by the support vector machine. The CIM shows a relation between leg variables and the arms swing asymmetry (ASA) and a proportional relationship between ASA and the diagnosis of PD with a robust estimator (1,537).ConclusionsMachine learning techniques based on objective measures using portable low-cost devices (Kinect®eMotion) are useful and accurate to classify patients with Parkinson’s disease. This method can be used to evaluate patients remotely and help clinicians make decisions regarding follow-up and treatment.

Markerless Motion Capture to Quantify Functional Performance in Neurodegeneration: Systematic Review (Preprint)

“…Several studies included in the review (n=10) reported machine learning classifier outcomes for identifying people living with dementia, MCI or PD from control [19,25,[29][30][31]33,[35][36][37]46] whereas several others (n=8) reported models that computed clinical assessment scores [17,18,24,34,[38][39][40][41]. Although these are useful outcomes, it is important to note that models that help detect gait impairment and predict falls (n=5) [14][15][16]27,32] could potentially be more useful in practical applications for assessing functional performance.…”

“…Of the 18 studies that included PD patients, 10 reported the use of the Kinect sensor for analysing gait, including its feasibility to extract relevant features [26,27] ability to detect PD [28][29][30][31] , and ability to measure clinical disease severity [32][33][34] . Alternative MMC models using image processing for pose estimation from videos recorded with RGB cameras have also been used with PD patients demonstrating the feasibility of these models in quantifying gait impairment and disease severity [18,[35][36][37][38][39][40][41].…”

“…Notably, several studies [32,34,36,42] found that stride length and cadence are commonly affected in those with Parkinson's disease. Other studies [15,24,33] highlighted the potential of MMC for predicting fall risk and discriminating between individuals with PD and controls.…”