Optical Marker- and Vision-Based Human Gait Biomechanical Analysis

2019 ◽  
pp. 275-291 ◽  
Author(s):  
Ganesh Roy ◽  
Thomas Jacob ◽  
Dinesh Bhatia ◽  
Subhasis Bhaumik
Keyword(s):  
Biomechanical Analysis ◽  
Human Gait

scholarly journals Biomechanical parameters characterising the foot during normal gait

2021 ◽  
Vol 21 (2) ◽  
pp. 87-104
Author(s):  
Arina SEUL ◽  
Aura MIHAI ◽  
Antonela CURTEZA ◽  
Mariana COSTEA ◽  
Bogdan SÂRGHIE
Keyword(s):  
Neuromuscular Control ◽  
Gait Pattern ◽  
Biomechanical Analysis ◽  
Human Gait ◽  
Foot Health ◽  
Mean Values ◽  
Study Results ◽  
Size Groups ◽  
Biomechanical Parameters ◽  
Extended Group

The biomechanical analysis allows to understand the normal and pathological gait, the mechanics of neuromuscular control, and last but not least, allows the visualisation of the effects of footwear on human gait or feet. Biomechanical analyses are very important for the footwear development process, as they can identify the incorrect loading of the foot or the incorrect gait pattern, thus avoiding the occurrence of deformations. This paper aims to create an average representative model of barefoot loading based on an extended group of participants by applying an optimal procedure for measuring biomechanical parameters. The variation of four basic biomechanical parameters, namely force, pressure, contact time and contact area, was measured using a pressure platform and a specialised software system. The data was collected from 32 healthy females, without particularities regarding foot health and the practice of performance sports, aged between 18 and 30 years, divided into three size groups – 36, 37 and 38. The T-Student test was applied to verify if there are significant differences between the left and right foot. Statistical indicators for each parameter were calculated, in order to characterize and establish the degree of variation of the obtained values, as follows: mean, standard deviation, minimum and maximum values, the amplitude of variation and coefficient of variation (CV). The study results confirm that the obtained mean values can be used as input data to load the foot and perform virtual simulations of footwear products.

scholarly journals Biomechanical analysis on human gait and posture for development of floating backpack

2020 ◽  
Vol 830 ◽  
pp. 032052
Author(s):  
N K Putra ◽  
H Khagi ◽  
A B Sriwarno ◽  
S Suprijanto
Keyword(s):  
Biomechanical Analysis ◽  
Human Gait

scholarly journals A Survey of Human Gait-Based Artificial Intelligence Applications

2022 ◽  
Vol 8 ◽  
Author(s):  
Elsa J. Harris ◽  
I-Hung Khoo ◽  
Emel Demircan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Gait Analysis ◽  
Human Motion ◽  
Biomechanical Analysis ◽  
Machine Learning Techniques ◽  
Human Gait ◽  
Pose Tracking ◽  
Learning Techniques ◽  
Applications Of Machine Learning

We performed an electronic database search of published works from 2012 to mid-2021 that focus on human gait studies and apply machine learning techniques. We identified six key applications of machine learning using gait data: 1) Gait analysis where analyzing techniques and certain biomechanical analysis factors are improved by utilizing artificial intelligence algorithms, 2) Health and Wellness, with applications in gait monitoring for abnormal gait detection, recognition of human activities, fall detection and sports performance, 3) Human Pose Tracking using one-person or multi-person tracking and localization systems such as OpenPose, Simultaneous Localization and Mapping (SLAM), etc., 4) Gait-based biometrics with applications in person identification, authentication, and re-identification as well as gender and age recognition 5) “Smart gait” applications ranging from smart socks, shoes, and other wearables to smart homes and smart retail stores that incorporate continuous monitoring and control systems and 6) Animation that reconstructs human motion utilizing gait data, simulation and machine learning techniques. Our goal is to provide a single broad-based survey of the applications of machine learning technology in gait analysis and identify future areas of potential study and growth. We discuss the machine learning techniques that have been used with a focus on the tasks they perform, the problems they attempt to solve, and the trade-offs they navigate.

Biomechanical Analysis of Human Gait with Inertial Sensors Using Neural Networks

2019 ◽  
pp. 213-221
Author(s):  
Bogart Yail Márquez ◽  
José Sergio Magdaleno-Palencia ◽  
Arnulfo Alanís-Garza ◽  
Karina Romero-Alvarado ◽  
Rosana Gutiérrez ◽  
...  
Keyword(s):  
Neural Networks ◽  
Inertial Sensors ◽  
Biomechanical Analysis ◽  
Human Gait

scholarly journals 3LP: A linear 3D-walking model including torso and swing dynamics

2017 ◽  
Vol 36 (4) ◽  
pp. 436-455 ◽  
Author(s):  
Salman Faraji ◽  
Auke J Ijspeert
Keyword(s):  
Reaction Force ◽  
Time Integration ◽  
Inequality Constraints ◽  
Biomechanical Analysis ◽  
Upper Body ◽  
Human Walking ◽  
Human Gait ◽  
Joint Torques ◽  
Double Support ◽  
Proposed Model

In this paper, we present a new mechanical model for biped locomotion, composed of three linear pendulums (one per leg and one for the whole upper body) to describe stance, swing and torso dynamics. In addition to a double support phase, this model has different actuation possibilities in the swing hip and stance ankle which produce a broad range of walking gaits. Without the need for numerical time-integration, closed form solutions help to find periodic gaits which could simply scale in certain dimensions to modulate the motion online. Thanks to linearity properties, the proposed model can potentially provide a computationally fast platform for model predictive controllers to predict the future and consider meaningful inequality constraints to ensure feasibility of the motion. Such a property comes from describing dynamics with joint torques directly and therefore, reflecting hardware limitations more precisely, even in the very abstract template space. The proposed model produces human-like torque and ground reaction force profiles, and thus, compared to point-mass models, it is more promising for the generation of dynamic walking trajectories. Despite being linear and lacking many features of human walking like center of mass excursion, knee flexion and ground clearance, we show that the proposed model can explain one of the main optimality trends in human walking, i.e. the nonlinear speed-frequency relationship. In this paper, we mainly focus on describing the model and its capabilities, comparing it with human data and calculating optimal human gait variables. Setting up control problems and advanced biomechanical analysis remains for future works.

Biomechanical Analysis of Tool Use

2012 ◽  
Vol 220 (1) ◽  
pp. 53-54 ◽  
Author(s):  
Elena Biryukova ◽  
Blandine Bril
Keyword(s):  
Tool Use ◽  
Biomechanical Analysis

Depth-dependent biomechanical analysis of articular cartilage in the murine DMM model

2020 ◽  
Author(s):  
L Fleischhauer ◽  
D Muschter ◽  
S Grässel ◽  
A Aszodi ◽  
H Clausen-Schaumann
Keyword(s):  
Articular Cartilage ◽  
Biomechanical Analysis

A Biomechanical Analysis of The Shot Put Performance

2012 ◽  
Vol 3 (5) ◽  
pp. 118-119 ◽  
Author(s):  
Dr.SUGUMAR.C Dr.SUGUMAR.C ◽  
Keyword(s):  
Biomechanical Analysis ◽  
Shot Put
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