Automated Gait Classification Using Spatio-Temporal and Statistical Gait Features

Author(s):  
Ratan Das ◽  
Preeti Khera ◽  
Somya Saxena ◽  
Neelesh Kumar
2008 ◽  
Vol 24 (1) ◽  
pp. 83-87 ◽  
Author(s):  
Jianning Wu ◽  
Jue Wang

In this technical note, we investigate a combination PCA with SVM to classify gait pattern based on kinetic data. The gait data of 30 young and 30 elderly participants were recorded using a strain gauge force platform during normal walking. The gait features were first extracted from the recorded vertical directional foot– ground reaction forces curve using PCA, and then these extracted features were adopted to develop the SVM gait classifier. The test results indicated that the performance of PCA-based SVM was on average 90% to recognize young– elderly gait patterns, resulting in a markedly improved performance over an artificial neural network–based classifier. The classification ability of the SVM with polynomial and radial basis function kernels was superior to that of the SVM with linear kernel. These results suggest that the proposed technique could provide an effective tool for gait classification in future clinical applications.


2018 ◽  
Vol 884 ◽  
pp. 105-112
Author(s):  
Amir Mukhtar ◽  
Michael J. Cree ◽  
Jonathan B. Scott ◽  
Lee Streeter

Gait classification is an effective and non-intrusive method for human identification and it has received significant attention in the recent years due to its applications in visual surveillance and monitoring systems. We analyse gait signatures using spatio-temporal motion characteristics of a person to answer the question ``is there a discriminating feature in the gait signal that can help to categorise a disabled person from healthy?''. The procedure has three steps: detection of a pedestrian using YOLO followed by the silhouette extraction using the Gaussian Mixture Model (GMM). Finally, skeletonization from the silhouette image to estimate head and torso locations and their angles with the vertical axis. Furthermore, velocity and acceleration signals were recorded to look for accelerating behaviour of person walking with a limp. Manual segmentations shows that the gait signal has information about unusual walking patterns but existing pedestrian detectors lack accuracy in extracting an accurate gait signal due to localization errors.


2018 ◽  
Vol 28 ◽  
pp. 120-127
Author(s):  
Amir Mukhtar ◽  
Michael J. Cree ◽  
Jonathan B. Scott ◽  
Lee Streeter

Gait classification is an effective and non-intrusive method for human identification and it has received significant attention in the recent years due to its applications in visual surveillance and monitoring systems. In this project, we analysed gait signatures using spatio-temporal motion characteristics of a person to answer the question ``is there a discriminating feature in gait signal that can help to categorise disable person from healthy?''. The procedure has three steps. detection of a pedestrian using YOLO followed by the silhouette extraction using the Gaussian Mixture Model (GMM). Finally, skeletonization from the silhouette image to estimate head and torso locations and their angles with the vertical axis. Furthermore, velocity and acceleration signals were recorded to look for accelerating behaviour of person walking with a limp.


2005 ◽  
Vol 41 ◽  
pp. 15-30 ◽  
Author(s):  
Helen C. Ardley ◽  
Philip A. Robinson

The selectivity of the ubiquitin–26 S proteasome system (UPS) for a particular substrate protein relies on the interaction between a ubiquitin-conjugating enzyme (E2, of which a cell contains relatively few) and a ubiquitin–protein ligase (E3, of which there are possibly hundreds). Post-translational modifications of the protein substrate, such as phosphorylation or hydroxylation, are often required prior to its selection. In this way, the precise spatio-temporal targeting and degradation of a given substrate can be achieved. The E3s are a large, diverse group of proteins, characterized by one of several defining motifs. These include a HECT (homologous to E6-associated protein C-terminus), RING (really interesting new gene) or U-box (a modified RING motif without the full complement of Zn2+-binding ligands) domain. Whereas HECT E3s have a direct role in catalysis during ubiquitination, RING and U-box E3s facilitate protein ubiquitination. These latter two E3 types act as adaptor-like molecules. They bring an E2 and a substrate into sufficiently close proximity to promote the substrate's ubiquitination. Although many RING-type E3s, such as MDM2 (murine double minute clone 2 oncoprotein) and c-Cbl, can apparently act alone, others are found as components of much larger multi-protein complexes, such as the anaphase-promoting complex. Taken together, these multifaceted properties and interactions enable E3s to provide a powerful, and specific, mechanism for protein clearance within all cells of eukaryotic organisms. The importance of E3s is highlighted by the number of normal cellular processes they regulate, and the number of diseases associated with their loss of function or inappropriate targeting.


2019 ◽  
Vol 47 (6) ◽  
pp. 1733-1747 ◽  
Author(s):  
Christina Klausen ◽  
Fabian Kaiser ◽  
Birthe Stüven ◽  
Jan N. Hansen ◽  
Dagmar Wachten

The second messenger 3′,5′-cyclic nucleoside adenosine monophosphate (cAMP) plays a key role in signal transduction across prokaryotes and eukaryotes. Cyclic AMP signaling is compartmentalized into microdomains to fulfil specific functions. To define the function of cAMP within these microdomains, signaling needs to be analyzed with spatio-temporal precision. To this end, optogenetic approaches and genetically encoded fluorescent biosensors are particularly well suited. Synthesis and hydrolysis of cAMP can be directly manipulated by photoactivated adenylyl cyclases (PACs) and light-regulated phosphodiesterases (PDEs), respectively. In addition, many biosensors have been designed to spatially and temporarily resolve cAMP dynamics in the cell. This review provides an overview about optogenetic tools and biosensors to shed light on the subcellular organization of cAMP signaling.


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