scholarly journals A Practical Review of the Biomechanical Parameters Commonly Used in the Assessment of Human Gait

2021 ◽  
Author(s):  
◽  
Juan Carlos Arellano-González
Keyword(s):  
Gait Analysis ◽  
Human Gait ◽  
Scientific Databases ◽  
Work Related ◽  
Gait Parameters ◽  
Comprehensive Search ◽  
Biomechanical Parameters ◽  
Human Gait Analysis ◽  
Gait Study ◽  
Selection Of

The analysis of human gait is a potential diagnostic instrument for the early and timely identification of pathologies and disorders. It can also supply valuable data for the development of biomedical devices such as prostheses, orthoses, and rehabilitation systems. Although various research papers in the literature have used human gait analyses, few studies have focused on the biomechanical parameters used. This paper presents an extensive review and analysis of the main biomechanical parameters commonly used in the human gait study. The aim is to provide a practical guide to support and understand of the choices and selection of the most appropriate biomechanical parameters for gait analysis. A comprehensive search in scientific databases was conducted to identify, review and analyze the academic work related to human gait analysis. From this search, the main biomechanical parameters used in healthy and pathological gait studies were identified and analyzed. The results have revealed that the spatiotemporal and angular gait parameters are the most used in the assessment of healthy and pathological human gait.

scholarly journals Two-dimensional video-based analysis of human gait using pose estimation

2020 ◽  
Author(s):  
Jan Stenum ◽  
Cristina Rossi ◽  
Ryan T. Roemmich
Keyword(s):  
Gait Analysis ◽  
Motion Capture ◽  
Pose Estimation ◽  
Gold Standard ◽  
Three Dimensional ◽  
Human Gait ◽  
Large Set ◽  
Two Dimensional ◽  
Gait Parameters ◽  
Human Gait Analysis

ABSTRACTWalking is the primary mode of human locomotion. Accordingly, people have been interested in studying human gait since at least the fourth century BC. Human gait analysis is now common in many fields of clinical and basic research, but gold standard approaches – e.g., three-dimensional motion capture, instrumented mats or footwear, and wearables – are often expensive, immobile, data-limited, and/or require specialized equipment or expertise for operation. Recent advances in video-based pose estimation have suggested exciting potential for analyzing human gait using only two-dimensional video inputs collected from readily accessible devices (e.g., smartphones, tablets). However, we currently lack: 1) data about the accuracy of video-based pose estimation approaches for human gait analysis relative to gold standard measurement techniques and 2) an available workflow for performing human gait analysis via video-based pose estimation. In this study, we compared a large set of spatiotemporal and sagittal kinematic gait parameters as measured by OpenPose (a freely available algorithm for video-based human pose estimation) and three-dimensional motion capture from trials where healthy adults walked overground. We found that OpenPose performed well in estimating many gait parameters (e.g., step time, step length, sagittal hip and knee angles) while some (e.g., double support time, sagittal ankle angles) were less accurate. We observed that mean values for individual participants – as are often of primary interest in clinical settings – were more accurate than individual step-by-step measurements. We also provide a workflow for users to perform their own gait analyses and offer suggestions and considerations for future approaches.

scholarly journals Two-dimensional video-based analysis of human gait using pose estimation

2021 ◽  
Vol 17 (4) ◽  
pp. e1008935
Author(s):  
Jan Stenum ◽  
Cristina Rossi ◽  
Ryan T. Roemmich
Keyword(s):  
Gait Analysis ◽  
Motion Capture ◽  
Pose Estimation ◽  
Mean Absolute Error ◽  
Three Dimensional ◽  
Absolute Error ◽  
Human Gait ◽  
Two Dimensional ◽  
Gait Parameters ◽  
Human Gait Analysis

Human gait analysis is often conducted in clinical and basic research, but many common approaches (e.g., three-dimensional motion capture, wearables) are expensive, immobile, data-limited, and require expertise. Recent advances in video-based pose estimation suggest potential for gait analysis using two-dimensional video collected from readily accessible devices (e.g., smartphones). To date, several studies have extracted features of human gait using markerless pose estimation. However, we currently lack evaluation of video-based approaches using a dataset of human gait for a wide range of gait parameters on a stride-by-stride basis and a workflow for performing gait analysis from video. Here, we compared spatiotemporal and sagittal kinematic gait parameters measured with OpenPose (open-source video-based human pose estimation) against simultaneously recorded three-dimensional motion capture from overground walking of healthy adults. When assessing all individual steps in the walking bouts, we observed mean absolute errors between motion capture and OpenPose of 0.02 s for temporal gait parameters (i.e., step time, stance time, swing time and double support time) and 0.049 m for step lengths. Accuracy improved when spatiotemporal gait parameters were calculated as individual participant mean values: mean absolute error was 0.01 s for temporal gait parameters and 0.018 m for step lengths. The greatest difference in gait speed between motion capture and OpenPose was less than 0.10 m s−1. Mean absolute error of sagittal plane hip, knee and ankle angles between motion capture and OpenPose were 4.0°, 5.6° and 7.4°. Our analysis workflow is freely available, involves minimal user input, and does not require prior gait analysis expertise. Finally, we offer suggestions and considerations for future applications of pose estimation for 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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