Motion tracking and gait feature estimation for recognising Parkinson's disease using MS Kinect.
BACKGROUND
Analysis of gait features provides important information during the treatment of neurological disorders, including Parkinson's disease. It is also used to observe the effects of medication and rehabilitation. The methodology presented in this paper enables the detection of selected gait attributes by Microsoft (MS) Kinect image and depth sensors to track movements in three-dimensional space.


METHODS
The experimental part of the paper is devoted to the study of three sets of individuals: 18 patients with Parkinson's disease, 18 healthy aged-matched individuals, and 15 students. The methodological part of the paper includes the use of digital signal-processing methods for rejecting gross data-acquisition errors, segmenting video frames, and extracting gait features. The proposed algorithm describes methods for estimating the leg length, normalised average stride length (SL), and gait velocity (GV) of the individuals in the given sets using MS Kinect data.


RESULTS
The main objective of this work involves the recognition of selected gait disorders in both the clinical and everyday settings. The results obtained include an evaluation of leg lengths, with a mean difference of 0.004 m in the complete set of 51 individuals studied, and of the gait features of patients with Parkinson's disease (SL: 0.38 m, GV: 0.61 m/s) and an age-matched reference set (SL: 0.54 m, GV: 0.81 m/s). Combining both features allowed for the use of neural networks to classify and evaluate the selectivity, specificity, and accuracy. The achieved accuracy was 97.2 %, which suggests the potential use of MS Kinect image and depth sensors for these applications.


CONCLUSIONS
Discussion points include the possibility of using the MS Kinect sensors as inexpensive replacements for complex multi-camera systems and treadmill walking in gait-feature detection for the recognition of selected gait disorders.
Department of Computing and Control Engineering, University of Chemistry and Technology in Prague, Technická 5, 166 28, Prague 6, Czech Republic. Ondrej.Tupa@vscht.cz.
Ondřej
O
Ťupa
Department of Computing and Control Engineering, University of Chemistry and Technology in Prague, Technická 5, 166 28, Prague 6, Czech Republic. A.Prochazka@ieee.org.|Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University, Zikova 1903/4, 166 36, Prague 6, Czech Republic. A.Prochazka@ieee.org.
Aleš
A
Procházka
Department of Computing and Control Engineering, University of Chemistry and Technology in Prague, Technická 5, 166 28, Prague 6, Czech Republic. Vysatao@gmail.com.|Department of Neurology, Charles University, Sokolská 581, 500 05, Hradec Kralove, Czech Republic. Vysatao@gmail.com.
Oldřich
O
Vyšata
Department of Computing and Control Engineering, University of Chemistry and Technology in Prague, Technická 5, 166 28, Prague 6, Czech Republic. Martin.Schatz@vscht.cz.
Martin
M
Schätz
Department of Computing and Control Engineering, University of Chemistry and Technology in Prague, Technická 5, 166 28, Prague 6, Czech Republic. Jan.Mares@vscht.cz.
Jan
J
Mareš
Department of Neurology, Charles University, Sokolská 581, 500 05, Hradec Kralove, Czech Republic. Valimar@seznam.cz.
Martin
M
Vališ
Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University, Zikova 1903/4, 166 36, Prague 6, Czech Republic. marik@labe.felk.cvut.cz.
Vladimír
V
Mařík
D000054:Acceleration; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D016022:Case-Control Studies; D005260:Female; D005684:Gait; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008297:Male; D008875:Middle Aged; D009415:Nerve Net; D010300:Parkinson Disease / Q000503:physiopathology*