Using Kinect to classify Parkinson’s disease stages related to severity of gait impairment

Abstract

BackgroundParkinson’s Disease (PD) is a chronic neurodegenerative disease associated with motor problems such as gait impairment. Different systems based on 3D cameras, accelerometers or gyroscopes have been used in related works in order to study gait disturbances in PD. Kinect Ⓡ has also been used to build these kinds of systems, but contradictory results have been reported: some works conclude that Kinect does not provide an accurate method of measuring gait kinematics variables, but others, on the contrary, report good accuracy results.MethodsIn this work, we have built a Kinect-based system that can distinguish between different PD stages, and have performed a clinical study with 30 patients suffering from PD belonging to three groups: early PD patients without axial impairment, more evolved PD patients with higher gait impairment but without Freezing of Gait (FoG), and patients with advanced PD and FoG. Those patients were recorded by two Kinect devices when they were walking in a hospital corridor. The datasets obtained from the Kinect were preprocessed, 115 features identified, some methods were applied to select the relevant features (correlation based feature selection, information gain, and consistency subset evaluation), and different classification methods (decision trees, Bayesian networks, neural networks and K-nearest neighbours classifiers) were evaluated with the goal of finding the most accurate method for PD stage classification.ResultsThe classifier that provided the best results is a particular case of a Bayesian Network classifier (similar to a Naïve Bayesian classifier) built from a set of 7 relevant features selected by the correlation-based on feature selection method. The accuracy obtained for that classifier using 10-fold cross validation is 93.40%. The relevant features are related to left shin angles, left humerus angles, frontal and lateral bents, left forearm angles and the number of steps during spin.ConclusionsIn this paper, it is shown that using Kinect is adequate to build a inexpensive and comfortable system that classifies PD into three different stages related to FoG. Compared to the results of previous works, the obtained accuracy (93.40%) can be considered high. The relevant features for the classifier are: a) movement and position of the left arm, b) trunk position for slightly displaced walking sequences, and c) left shin angle, for straight walking sequences. However, we have obtained a better accuracy (96.23%) for a classifier that only uses features extracted from slightly displaced walking steps and spin walking steps. Finally, the obtained set of relevant features may lead to new rehabilitation therapies for PD patients with gait problems.

Computer-Vision Based Diagnosis of Parkinson’s Disease via Gait: A Survey

“…The use of Bayesian classification successfully yielded an accuracy of 94.1% for PD identification. Moving on the way to Kinect sensor for vision-based gait capture, another by Dranca et al [52] proposed to develop a Kinect based system to compare and differentiate the severity levels of 30 PD affected patients. They applied two Kinect sensors to acquire PD gait with a sampling rate of 30fps and about 115 related features were determined.…”

“…Minimum redundancy maximum relevance (MRMR) [77] filter method enhances the relevance and reduces redundancy in every class using mutual info and linear relation for both categorical and continuous variables and provides low error rates for the feature selection process. Correlation-based feature selection (CBFS) [52] method works on the concept of heuristic merit to minimize the cost of feature selection. CFS takes into account each feature individually, identifies their predictive value and amount of correlation.…”

Analysis and Classification of Motor Dysfunctions in Arm Swing in Parkinson’s Disease

“…Our System IMU sensors 93.3% [12] Kinect camera 90% [17] Kinect, Bayesian network 93.4% [18] Kinect and e-Motion capture program 96.23% [15] Gyroscope 90%…”

“…However, the analysis of the arm movement is also important for the assessment of a gait disorder. Stationary systems that use cameras or ultrasound [11][12][13][14][15][16][17][18][19] and mobile systems with inertial sensors [20][21][22] are used to measure the arm swing.…”

“…A Kinect system was used in [16] to detect the differences in speed, amplitude, and symmetry in arm movement between healthy people and people in the early stages of Parkinson's disease. In [17], it was investigated which model method provided the best results when using a Kinect to detect Parkinson's disease stages. The best results with an accuracy of 93.4% were obtained with a special Bayesian network classifier using 10-fold cross-validation.…”