Markerless Gait Classification Employing 3D IR-UWB Physiological Motion Sensing

• Main Authors: Dey, M. (Author), Dudley, S. (Author), Ghavami, M. (Author), Rana, S.P (Author)
• Format: Article
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2022
• Subjects: Bland and altman plot; Bland and Altman plot; Cameras; deep multilayer perceptron; Deep multilayer perceptron; Gait; Gait abnormalities; Impulse noise; Impulse Radio; Impulse radio ultra-wide band; impulse radio ultra-wide band (IR-UWB); Kinect xbox sensor; Kinect Xbox sensor; knee angle extraction; Knee angle extraction; machine learning; Markerless; Multilayer neural networks; Multilayers; Multilayers perceptrons; Radio; Support vector machines; Ultra-wideband (UWB)
• Online Access: View Fulltext in Publisher

 Human gait refers to the propulsion achieved by the effort of human limbs, a reflex progression resulting from the rhythmic reciprocal bursts of flexor and extensor activity. Several quantitative models are followed by health professionals to diagnose gait abnormality. Marker-based gait quantification is considered a gold standard by the research and health communities. It reconstructs motion in 3D and provides parameters to measure gait. But, it is an expensive and intrusive technique, limited to soft tissue artefact, prone to incorrect marker positioning, and skin sensitivity problems. Hence, markerless, swiftly deployable, non-intrusive, camera-less prototypes would be a game changing possibility, and an example is proposed here. This paper illustrates a 3D gait motion analyser employing impulse radio ultra-wide band (IR-UWB) wireless technology. The prototype can measure 3D motion and determine quantitative parameters considering anatomical reference planes. Knee angles have been calculated from the gait by applying vector algebra. Simultaneously, the model has been corroborated with the popular markerless camera based 3D motion capturing system, the Kinect sensor. Bland and Altman (BA) statistics has been applied to the proposed prototype and Kinect sensor results to verify the measurement agreement. Finally, the proposed prototype has been incorporated with popular supervised machine learning such as,    -nearest neighbour (    NN   ), support vector machine (SVM) and the deep learning technique deep neural multilayer perceptron (DMLP) network to automatically recognize gait abnormalities, with promising results presented. © 2001-2012 IEEE.