Abstract
BackgroundAnalysis 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.ResultsThe 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.ConclusionsDiscussion 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.
Highlights
Analysis of gait features provides important information during the treatment of neurological disorders, including Parkinson’s disease
Kinect [4, 5] allows for the recording of such data sets via its image and depth sensors and the subsequent transfer of these data to appropriate mathematical environments, such as MATLAB, for further processing
The depth sensor consists of an infrared projector and an infrared camera that uses the structured light principle [27, 28] to detect the distances between image pixels
Summary
Analysis of gait features provides important information during the treatment of neurological disorders, including Parkinson’s disease. The acquired data sets can be used to propose methods and algorithms for movement analyses [6], scene modelling [7], gesture and body recognition [8], rehabilitation [2], and posture reconstruction [9, 10]. These new devices, combined with motion sensors [11] and specific control units, are often used for objective gait analysis
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