000076967 001__ 76967
000076967 005__ 20201105083210.0
000076967 0247_ $$2doi$$a10.1186/s12859-018-2488-4
000076967 0248_ $$2sideral$$a109854
000076967 037__ $$aART-2018-109854
000076967 041__ $$aeng
000076967 100__ $$0(orcid)0000-0002-9169-5287$$aDranca, L.
000076967 245__ $$aUsing Kinect to classify Parkinson's disease stages related to severity of gait impairment
000076967 260__ $$c2018
000076967 5060_ $$aAccess copy available to the general public$$fUnrestricted
000076967 5203_ $$aBackground: 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 <sup/> 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.
000076967 536__ $$9info:eu-repo/grantAgreement/ES/MINECO-FEDER/TIN2016-78011-C4-2-R
000076967 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000076967 590__ $$a2.511$$b2018
000076967 591__ $$aMATHEMATICAL & COMPUTATIONAL BIOLOGY$$b8 / 59 = 0.136$$c2018$$dQ1$$eT1
000076967 591__ $$aBIOCHEMICAL RESEARCH METHODS$$b36 / 78 = 0.462$$c2018$$dQ2$$eT2
000076967 591__ $$aBIOTECHNOLOGY & APPLIED MICROBIOLOGY$$b79 / 162 = 0.488$$c2018$$dQ2$$eT2
000076967 592__ $$a1.374$$b2018
000076967 593__ $$aApplied Mathematics$$c2018$$dQ1
000076967 593__ $$aBiochemistry$$c2018$$dQ1
000076967 593__ $$aStructural Biology$$c2018$$dQ1
000076967 593__ $$aMolecular Biology$$c2018$$dQ1
000076967 593__ $$aComputer Science Applications$$c2018$$dQ1
000076967 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000076967 700__ $$ade Abetxuko Ruiz de Mendarozketa, L.
000076967 700__ $$aGoñi, A.
000076967 700__ $$aIllarramendi, A.
000076967 700__ $$aNavalpotro Gomez, I.
000076967 700__ $$aDelgado Alvarado, M.
000076967 700__ $$aCruz Rodríguez-Oroz, M.
000076967 773__ $$g19, 1 (2018), 471 [15 pp]$$pBMC bioinformatics$$tBMC BIOINFORMATICS$$x1471-2105
000076967 8564_ $$s1222311$$uhttps://zaguan.unizar.es/record/76967/files/texto_completo.pdf$$yVersión publicada
000076967 8564_ $$s8227$$uhttps://zaguan.unizar.es/record/76967/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000076967 909CO $$ooai:zaguan.unizar.es:76967$$particulos$$pdriver
000076967 951__ $$a2020-11-05-08:20:47
000076967 980__ $$aARTICLE