An Alternative Approach to Distinguish Movements of Parkinson Disease Patients

Abstract The set of the motion mass parameters, describing the amount and smoothness of movements, is employed as a main tool to demonstrate the differences between the patients with Parkinson disease and the group of healthy individuals, performing the Up-and-Go test. Unlike many existing results in this area, which are based on some features associated to the particular time instance, motion mass parameters are computed for the certain time interval and therefore describe movement in general. The main goal of the present study is to demonstrate that the motion mass parameters associated with the segments of the Up-and-Go test significantly differ between patients with Parkinson disease and healthy individuals.

[1]  Ahmad Ihsan Mohd Yassin,et al.  Statistical analysis of parkinson disease gait classification using Artificial Neural Network , 2011, 2011 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).

[2]  María Teresa Arredondo,et al.  Proposal of a KinectTM-based system for gait assessment and rehabilitation in Parkinson's disease , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[3]  P. Olivier,et al.  Accuracy of the Microsoft Kinect sensor for measuring movement in people with Parkinson's disease. , 2014, Gait & posture.

[4]  Yosuke Kurihara,et al.  Accelerometry-Based Gait Analysis and Its Application to Parkinson's Disease Assessment— Part 1: Detection of Stride Event , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[5]  J C Wall,et al.  The Timed Get-up-and-Go test revisited: measurement of the component tasks. , 2000, Journal of rehabilitation research and development.

[6]  A. Antonini,et al.  EFNS/MDS‐ES recommendations for the diagnosis of Parkinson's disease , 2013, European journal of neurology.

[7]  Christopher R. Harris,et al.  Accurate and Reliable Gait Cycle Detection in Parkinson's Disease , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[8]  Rupak K Banerjee,et al.  Pulsatile arterial wall-blood flow interaction with wall pre-stress computed using an inverse algorithm , 2015, Biomedical engineering online.

[9]  Martin Schätz,et al.  Motion tracking and gait feature estimation for recognising Parkinson’s disease using MS Kinect , 2015, BioMedical Engineering OnLine.

[10]  Antonios Gasteratos,et al.  Computer Vision Systems , 2015, Lecture Notes in Computer Science.

[11]  Richard W. Bohannon,et al.  Reference Values for the Timed Up and Go Test: A Descriptive Meta‐Analysis , 2006, Journal of geriatric physical therapy.

[12]  Sven Nomm,et al.  An alternative approach to measure quantity and smoothness of the human limb motions , 2013 .