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dc.contributor.authorAich S.-
dc.contributor.authorPradhan, Pyari Mohan-
dc.contributor.authorPark J.-
dc.contributor.authorKim H.-C.-
dc.identifier.citationInternational Journal of Engineering and Technology(UAE) (2018), 7(3): 153-156-
dc.description.abstractIn recent times the adverse impact of Parkinson's disease (PD) getting worse and worse with the increasing rate of old age population through out the world. This disease is the second common neurological disorder and has a tremendous economical and social impact because the cost associated with the healthcare as well as service is extremely high. The diagnosis process of this disease mostly done by closely observing the patient in the clinic as well as using the rating scale. However, this kind of diagnosis is subjective in nature and usually takes long time and assessment of this disease is complicated and cannot replicated in other patients. This kind of diagnosis method is also not suitable for the early detection of the PD. So, with this shortcoming it is necessary to find a suitable method that can automate the process as well as useful in the initial phase of diagnosis of PD. Recently with the invention of motion capture equipment's and artificial intelligent technique, the feasibility of the objective nature-based diagnosis is getting lot of attention, especially the objective quantification of gait parameters. Shuffling of gait is one of the important characteristics of PD patients and it is usually defined y shorter stride length and low foot clearance. In this study a novel method is proposed to quantify the gait parameters using 3D motion captures and then various feature selection algorithm have used to select the effective features and finally machine learning based techniques were implemented to automate the classification process of two groups composed of PD patients as well as older adults. We have found maximum accuracy of 98.54 %by using support vector machine (SVM) classifier with radial basis function coupled with minimum redundancy and maximum relevance (MRMR) algorithm-based feature set. Our result showed that the proposed method can help the clinicians to distinguish PD patients from the older adults. This method helps to detect the PD at early stage. © 2018 Satyabrata Aich et. al.-
dc.publisherScience Publishing Corporation Inc-
dc.relation.ispartofInternational Journal of Engineering and Technology(UAE)-
dc.subjectFeature selection-
dc.subjectMachine learning-
dc.subjectParkinson's disease-
dc.subjectShuffling gait-
dc.subjectWearable sensor-
dc.titleA machine learning approach to distinguish Parkinson's disease (PD) patient's with shuffling gait from older adults based on gait signals using 3D motion analysis-
dc.affiliationAich, S., Department of Computer Engineering/IDA, Inje University, Gimhae, South Korea-
dc.affiliationPradhan, P.M., Department of Electronics and Communication Engineering, IIT, Roorkee, India-
dc.affiliationPark, J., Department of Neurology, INJE University College of Medicine, Busan, South Korea-
dc.affiliationKim, H.-C., Department of Computer Engineering/IDA, Inje University, Gimhae, South Korea-
dc.description.correspondingauthorAich, S.; Department of Computer Engineering/IDA, Inje UniversitySouth Korea; email:
Appears in Collections:Journal Publications [ECE]

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