scholarly journals Gait Analysis Study of Runner Using Force Plate

2017 ◽  
Vol 6 (02) ◽  
pp. 125
Author(s):  
Flaviana Catherine ◽  
Risti Suryantari
Keyword(s):  
Gait Analysis ◽  
Reaction Force ◽  
Force Plate ◽  
Gait Pattern ◽  
Human Gait ◽  
Main Method ◽  
Analysis Study ◽  
Energy Cycle ◽  
Specific Setting ◽  
Human Gait Analysis

<pre>Humans do regular physical activities such as running. Gait is forward  propulsion of the human body using lower extremities as a thrust. Humans gait pattern is characterized by their limbs movement in terms of velocity, ground reaction force, work, kinetic energy and potential energy cycle . Human gait analysis is used to assess, to plan, and to deliver the treatment for individuals based on the conditions that affect their ability to move. Gait analysis is commonly used in running sport to improve the efficiency of athletes in running and to identify problems related to their posture or movement. The aim of this research is to do running gait analysis study of human, using force plate which equipped by track board. The benefit of this study is to provide information, ideas and new perspectives about running and its prevention over an injury. The main method that will be discussed in this study is system design of gait analysis with specific setting, hardware and software, in order to acquire data(s). </pre>

scholarly journals Human Gait Analysis as viewed from A/K and B/K Force Plate/Stick Figures.

1999 ◽  
Vol 11 (2) ◽  
pp. 79-85 ◽  
Author(s):  
Masataka Hosoda ◽  
Osamu Yoshimura ◽  
Kiyomi Takayanagi ◽  
Hiroshi Maejima ◽  
Ryuji Kobayashi ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Force Plate ◽  
Human Gait ◽  
Human Gait Analysis

DEVELOPMENT OF A SYSTEM FOR HUMAN GAIT ANALYSIS USING KINECT V2

2019 ◽  
Author(s):  
Ítalo Rodrigues ◽  
Jadiane Dionisio ◽  
Rogério Sales Gonçalves
Keyword(s):  
Gait Analysis ◽  
Human Gait ◽  
Kinect V2 ◽  
Human Gait Analysis

scholarly journals Human Gait Analysis in Neurodegenerative Diseases: a Review

2021 ◽  
pp. 1-1
Author(s):  
Grazia Cicirelli ◽  
Donato Impedovo ◽  
Vincenzo Dentamaro ◽  
Roberto Marani ◽  
Giuseppe Pirlo ◽  
...  
Keyword(s):  
Neurodegenerative Diseases ◽  
Gait Analysis ◽  
Human Gait ◽  
Human Gait Analysis

Human Gait Analysis Method Based on Sample Entropy Fusion AlphaPose Algorithm

2021 ◽  
Author(s):  
Xinyu Lv ◽  
Shengying Wang ◽  
Tao Chen ◽  
Jing Zhao ◽  
Desheng Chen ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Sample Entropy ◽  
Human Gait ◽  
Analysis Method ◽  
Human Gait Analysis

scholarly journals Human Gait-labeling Uncertainty and a Hybrid Model for Gait Segmentation

2021 ◽  
Author(s):  
Jiaen Wu ◽  
Henrik Maurenbrecher ◽  
Alessandro Schaer ◽  
Barna Becsek ◽  
Chris Awai Easthope ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Motion Capture ◽  
Ground Truth ◽  
Absolute Error ◽  
Human Gait ◽  
Heel Strike ◽  
Analysis Model ◽  
Flat Foot ◽  
Dynamic Time ◽  
Human Gait Analysis

<div><div><div><p>Motion capture systems are widely accepted as ground-truth for gait analysis and are used for the validation of other gait analysis systems.To date, their reliability and limitations in manual labeling of gait events have not been studied.</p><p><b>Objectives</b>: Evaluate human manual labeling uncertainty and introduce a new hybrid gait analysis model for long-term monitoring.</p><p><b>Methods</b>: Evaluate and estimate inter-labeler inconsistencies by computing the limits-of-agreement; develop a model based on dynamic time warping and convolutional neural network to identify a valid stride and eliminate non-stride data in walking inertial data collected by a wearable device; Gait events are detected within a valid stride region afterwards; This method makes the subsequent data computation more efficient and robust.</p><p><b>Results</b>: The limits of inter-labeler agreement for key</p><p>gait events of heel off, toe off, heel strike, and flat foot are 72 ms, 16 ms, 22 ms, and 80 ms, respectively; The hybrid model's classification accuracy for a stride and a non-stride are 95.16% and 84.48%, respectively; The mean absolute error for detected heel off, toe off, heel strike, and flat foot are 24 ms, 5 ms, 9 ms, and 13 ms, respectively.</p><p><b>Conclusions</b>: The results show the inherent label uncertainty and the limits of human gait labeling of motion capture data; The proposed hybrid-model's performance is comparable to that of human labelers and it is a valid model to reliably detect strides in human gait data.</p><p><b>Significance</b>: This work establishes the foundation for fully automated human gait analysis systems with performances comparable to human-labelers.</p></div></div></div>

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