Motion tracking and gait feature estimation for recognising Parkinson’s disease using MS Kinect

• Published in: Biomedical engineering online Vol. 14; no. 1; p. 97
• Main Authors: Tupa, Ondrej, Prochazka, Ales, Vysata, Oldrich, Schatz, Martin, Mares, Jan, Valis, Martin, Marik, Vladimir
• Format: Journal Article
• Language: English
• Published: London BioMed Central 24.10.2015; BioMed Central Ltd; Springer Nature B.V
• Subjects: Acceleration; Adult; Aged; Aged, 80 and over; Algorithms; Biomaterials; Biomedical Engineering and Bioengineering; Biomedical Engineering/Biotechnology; Biotechnology; Care and treatment; Case-Control Studies; Complications and side effects; Computer software industry; Diagnosis; Engineering; Female; Gait; Humans; Imaging, Three-Dimensional - methods; Male; Middle Aged; Movement disorders; Nerve Net; Neural networks; Parkinson Disease - physiopathology; Risk factors; Walking; United States; Image and depth sensors; Video processing; MS Kinect; Classification; Parkinson’s disease; Gait disorders; Motion features
• ISSN: 1475-925X; 1475-925X
• DOI: 10.1186/s12938-015-0092-7

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.