scholarly journals Survey of Human Gait Analysis and Recognition for Different Sensor Modalities

2021 ◽  
Vol 13 (6) ◽  
pp. 0-0
Keyword(s):  
Gait Analysis ◽  
Gait Recognition ◽  
Research Area ◽  
Human Gait ◽  
Data Sets ◽  
Medical Laboratories ◽  
Crime Detection ◽  
Skeleton Image ◽  
Few Data ◽  
Human Gait Analysis

Gait is a behavioural biometric which sometimes changes due to diseases but it is still a strong identification metric that is widely used in forensic works, state biometric preserve sectors, and medical laboratories. Gait analysis sometimes helps to identify person’s present mental state which reflects on physiological therapy for improved biological system. There are various gait measurement forms which expand the research area from crime detection to medical enhancement. Many research works have been done so far for gait recognition. Many researchers focused on skeleton image of people to extract gait features and many worked on stride length. Various sensors have been used to detect gait in various light forms. This paper is a brief survey of works on gait recognition, collected from various sources of science and technology literature. We have discussed few efficient models that worked best as well as we have discussed about few data sets available.

Systematic Review on Human Gait Analysis using Deep Learning Models

2021 ◽  
pp. 09-22
Author(s):  
Piyush Kumar Shukla ◽  
◽  
Prashant Kumar Shukla ◽  
Keyword(s):  
Deep Learning ◽  
Gait Analysis ◽  
Gait Recognition ◽  
The Body ◽  
Human Gait ◽  
Learning Models ◽  
Gait Patterns ◽  
Learning Techniques ◽  
The Individual ◽  
Human Gait Analysis

Human Gait is known as a behavioral characteristic of humans, compared with the other biometrics gait is found to be a difficult process to conceal. Human gait analysis is usually done by extracting the features from the body. Analysis of gait involves evaluating the individual by means of kinematic analysis while walking along a surface. The main objective and the purpose of gait recognition is to give the best method where risks are recognized in places where there is a need for high security in any public place and to detect diseases like Parkinson’s. In order to acquire a normal person’s identification and validation performance, various Deep Learning techniques are totally studied and modeled the biometrics of gait which is based on walking data. It is reviewed that among various essential metrics that are used, deep learning convolution neural networks are typically better Machine Learning models. The main objective of the present study was to examine in detail individual gait patterns. Finally, this paper recommends deep learning methods and suggests the directions for future gait analysis and also for its applications.

Publicly Accessible Wearable Motion Databases for Human Gait Studies

2020 ◽  
Vol 64 (1) ◽  
pp. 1718-1722
Author(s):  
Yue Luo ◽  
Sai Ouyang ◽  
Caroline Lockwood ◽  
Maria D. Ferraz ◽  
Boyi Hu
Keyword(s):  
Gait Analysis ◽  
Activity Recognition ◽  
Microelectromechanical Systems ◽  
Human Gait ◽  
Smart Devices ◽  
Data Sets ◽  
Improve Model ◽  
Human Effort ◽  
Human Gait Analysis ◽  
Areas Of Interest

As emerging areas of interest in gait analysis research, gait authentication and activity recognition have drawn significant attention recently, facilitated by microelectromechanical systems (MEMS) and smart devices. Machine learning models have been developed to reduce human effort and improve model accuracy in these areas. Sufficient amounts of data become critical for these applications. Unlike other fields such as image processing, in which massive data are easy to collect, human gait data is difficult to collect in large amounts, which makes publicly accessible databases in this area even more valuable. This paper aimed to summarize publicly accessible IMU-based gait databases by surveying the recent literature in human gait analysis. 199 papers were manually evaluated with sixteen data sets included (seven for gait authentication and nine for activity recognition). The detailed technical contents were described and compared in the survey to assist the audience on how to better utilize the data sets.

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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