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

التفاصيل البيبلوغرافية
العنوان: Using Kinect to classify Parkinson’s disease stages related to severity of gait impairment
المؤلفون: Alfredo Goñi, Lacramioara Dranca, Lopez de Abetxuko Ruiz de Mendarozketa, Manuel Delgado Alvarado, Arantza Illarramendi, María Cruz Rodríguez-Oroz, Irene Navalpotro Gomez
المصدر: Addi. Archivo Digital para la Docencia y la Investigación
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BMC Bioinformatics
BMC Bioinformatics, Vol 19, Iss 1, Pp 1-15 (2018)
سنة النشر: 2018
مصطلحات موضوعية: Male, 030506 rehabilitation, Gait kinematics, Computer science, lcsh:Computer applications to medicine. Medical informatics, Accelerometer, Biochemistry, 03 medical and health sciences, Gait problems, 0302 clinical medicine, Structural Biology, Humans, lcsh:QH301-705.5, Molecular Biology, Gait, Aged, Probability, Kinect, business.industry, Gait Disturbance, Applied Mathematics, Pattern recognition, Bayes Theorem, Trunk, Computer Science Applications, Parkinson disease, Bayesian networks, lcsh:Biology (General), lcsh:R858-859.7, Female, Artificial intelligence, Classification methods, 0305 other medical science, business, Classifier (UML), 030217 neurology & neurosurgery, Algorithms, Software, Research Article
الوصف: Background Parkinson’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. Methods In 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. Results The 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. Conclusions In 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.
وصف الملف: application/pdf
اللغة: English
الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ae03e420f2e38c4c54dd2471296c24b8Test
http://hdl.handle.net/10810/30783Test
حقوق: OPEN
رقم الانضمام: edsair.doi.dedup.....ae03e420f2e38c4c54dd2471296c24b8
قاعدة البيانات: OpenAIRE