Application of Floquet Theory to Human Gait Kinematics and Dynamics

2021 ◽  
pp. 1-35
Author(s):  
Sandesh G. Bhat ◽  
Susheelkumar Cherangara Subramanian ◽  
Thomas S Sugar ◽  
Sangram Redkar
Keyword(s):  
Floquet Theory ◽  
Gait Pattern ◽  
Human Gait ◽  
Reduced Order ◽  
Oscillator System ◽  
Walking Gait ◽  
Gait Kinematics ◽  
Dimension Analysis ◽  
Time Trace ◽  
Original Domain

Abstract In this work, the lower extremity physiological parameters are recorded during normal walking gait, and the dynamical systems theory is applied towards its stability analysis. The human walking gait pattern of kinematic and dynamical data is approximated to periodic behavior. The embedding dimension analysis of the kinematic variable's time trace and use of Taken's theorem allows us to compute a reduced-order time series that retains the essential dynamics. In conjunction with Floquet Theory, this approach can help study the system's stability characteristics. The Lyapunov-Floquet (L-F) Transformation application results in constructing an invariant manifold resembling the form of a simple oscillator system. It is also demonstrated that the simple oscillator system, when re-mapped back to the original domain, reproduces the original system's time evolution (hip angle or knee angle, for example). A re-initialization procedure is suggested that improves the accuracy between the processed data and actual data. The theoretical framework proposed in this work is validated with the experiments using a motion capture system.

Design of a 6-DOF Robotic Gait Training System With Closed-Chain Foot Initiated Kinematics Control

2015 ◽  
Author(s):  
Wei Liu ◽  
John Kovaleski ◽  
Marcus Hollis
Keyword(s):  
Three Dimensional ◽  
Computer Software ◽  
Gait Training ◽  
Gait Pattern ◽  
Training System ◽  
Normal Walking ◽  
Robot Assisted ◽  
Walking Gait ◽  
The Right ◽  
Joint Motions

Robotic assisted rehabilitation, taking advantage of neuroplasticity, has been shown to be helpful in regaining some degree of gait performance. Robot-applied movement along with voluntary efferent motor commands coordinated with the robot allows optimization of motion training. We present the design and characteristics of a novel foot-based 6-degree-of-freedom (DOF) robot-assisted gait training system where the limb trajectory mirrored the normal walking gait. The goal of this study was to compare robot-assisted gait to normal walking gait, where the limb moved independently without robotics. Motion analysis was used to record the three-dimensional kinematics of the right lower extremity. Walking motion data were determined and transferred to the robotic motion application software for inclusion in the robotic trials where the robot computer software was programmed to produce a gait pattern in the foot equivalent to the gait pattern recorded from the normal walking gait trial. Results demonstrated that ankle; knee and hip joint motions produced by the robot are consistent with the joint motions in walking gait. We believe that this control algorithm provides a rationale for use in future rehabilitation, targeting robot-assisted training in people with neuromuscular disabilities such as stroke.

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 Online Biped Walking Pattern Generation with Contact Consistency

2015 ◽  
Vol 4 (1) ◽  
pp. 19
Author(s):  
Wenqi Hou ◽  
Jian Wang ◽  
Jianwen Wang ◽  
Hongxu Ma
Keyword(s):  
Biped Robot ◽  
Gait Pattern ◽  
Pattern Generation ◽  
Task Space ◽  
Biped Walking ◽  
Foot Placement ◽  
Walking Gait ◽  
Walking Pattern ◽  
Generating Method ◽  
Stable Periodic

In this paper, a novel online biped walking gait pattern generating method with contact consistency is proposed. Generally, it’s desirable that there is no foot-ground slipping during biped walking. By treating the hip of the biped robot as a linear inverted pendulum (LIP), a foot placement controller that takes the contact consistency into account is proposed to tracking the desired orbit energy. By selecting the hip’s horizontal locomotion as the parameter, the trajectories in task space for walking are planned. A task space controller without calculating the inversion of inertial matrix is presented. Simulation experiments are implemented on a virtual 5-link point foot biped robot. The results show the effectiveness of the walking pattern generating method which can realize a stable periodic gait cycle without slipping and falling even suffering a sudden disturbance.

scholarly journals Human Gait Cycle Analysis Using Kinect V2 Sensor

2020 ◽  
Vol 15 (3) ◽  
pp. 3-14
Author(s):  
Péter Müller ◽  
Ádám Schiffer
Keyword(s):  
Gait Cycle ◽  
Medical Condition ◽  
Measurement Data ◽  
Human Movement ◽  
Gait Pattern ◽  
Lower Limbs ◽  
Human Gait ◽  
Skeleton Model ◽  
Kinect V2 ◽  
Reduced Mobility

Examining a human movement can provide a wealth of information about a patient’s medical condition. The examination process can be used to diagnose abnormal changes (lesions), ability development and monitor the rehabilitation process of people with reduced mobility. There are several approaches to monitor people, among other things with sensors and various imaging and processing devices. In this case a Kinect V2 sensor and a self-developed LabView based application was used, to examine the movement of the lower limbs. The ideal gait pattern was recorded in the RoboGait training machine and the measured data was used to identify the phases of the human gait. During the evaluation, the position of the skeleton model, the associated body joints and angles can be calculated. The pre-recorded ideal and natural gait cycle can be compared.With the self-developed method the pre-recorded ideal and natural gait cycle can be compared and processed for further evaluation. The evaluated measurement data confirm that a reliable and mobile solution for gait analysis has been created.

Gait Abnormality Detection Using Deep Convolution Network

2021 ◽  
pp. 363-372
Author(s):  
Saikat Chakraborty ◽  
Tomoya Suzuki ◽  
Abhipsha Das ◽  
Anup Nandy ◽  
Gentiane Venture
Keyword(s):  
Low Cost ◽  
Gait Pattern ◽  
Human Gait ◽  
Kinect Sensor ◽  
Gait Abnormality ◽  
Depth Images ◽  
Abnormal Gait ◽  
Unsteady Gait ◽  
Deep Convolution Network ◽  
Human Gait Analysis

Human gait analysis plays a significant role in clinical domain for diagnosis of musculoskeletal disorders. It is an extremely challenging task for detecting abnormalities (unsteady gait, stiff gait, etc.) in human walking if the prior information is unknown about the gait pattern. A low-cost Kinect sensor is used to obtain promising results on human skeletal tracking in a convenient manner. A model is created on human skeletal joint positions extracted using Kinect v2 sensor in place using Kinect-based color and depth images. Normal gait and abnormal gait are collected from different persons on treadmill. Each trial of gait is decomposed into cycles. A convolutional neural network (CNN) model was developed on this experimental data for detection of abnormality in walking pattern and compared with state-of-the-art techniques.

scholarly journals Multimodal Few-Shot Learning for Gait Recognition

2020 ◽  
Vol 10 (21) ◽  
pp. 7619
Author(s):  
Jucheol Moon ◽  
Nhat Anh Le ◽  
Nelson Hebert Minaya ◽  
Sang-Il Choi
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Gait Recognition ◽  
Gait Pattern ◽  
Recognition Problem ◽  
Human Gait ◽  
Training Dataset ◽  
Support Vector ◽  
Open Set ◽  
Latent Space

A person’s gait is a behavioral trait that is uniquely associated with each individual and can be used to recognize the person. As information about the human gait can be captured by wearable devices, a few studies have led to the proposal of methods to process gait information for identification purposes. Despite recent advances in gait recognition, an open set gait recognition problem presents challenges to current approaches. To address the open set gait recognition problem, a system should be able to deal with unseen subjects who have not included in the training dataset. In this paper, we propose a system that learns a mapping from a multimodal time series collected using insole to a latent (embedding vector) space to address the open set gait recognition problem. The distance between two embedding vectors in the latent space corresponds to the similarity between two multimodal time series. Using the characteristics of the human gait pattern, multimodal time series are sliced into unit steps. The system maps unit steps to embedding vectors using an ensemble consisting of a convolutional neural network and a recurrent neural network. To recognize each individual, the system learns a decision function using a one-class support vector machine from a few embedding vectors of the person in the latent space, then the system determines whether an unknown unit step is recognized as belonging to a known individual. Our experiments demonstrate that the proposed framework recognizes individuals with high accuracy regardless they have been registered or not. If we could have an environment in which all people would be wearing the insole, the framework would be used for user verification widely.

scholarly journals Bilateral Foot Orthoses Elicit Changes in Gait Kinematics of Adolescents with Down Syndrome with Flatfoot

2020 ◽  
Vol 17 (14) ◽  
pp. 4994
Author(s):  
Daniele Galafate ◽  
Sanaz Pournajaf ◽  
Claudia Condoluci ◽  
Michela Goffredo ◽  
Gabriella Di Girolamo ◽  
...  
Keyword(s):  
Down Syndrome ◽  
Video Recording ◽  
Gait Pattern ◽  
Rank Test ◽  
Foot Orthoses ◽  
Optoelectronic System ◽  
Flat Foot ◽  
Gait Kinematics ◽  
Gait Quality ◽  
The Impact

Background: Subjects with Down Syndrome (DS) are characterized by specific physiological alterations, including musculoskeletal abnormalities. Flat Foot (FF), caused by hypotonia and ligament laxity, represents one of the most common disabling disorders in this population. Conservative treatments promote the use of orthopaedic insoles and plantar supports. The aim of this study was to evaluate the impact of Foot Orthoses (FOs) on the gait pattern of subjects with DS, assessing the biomechanical effects associated with their use. Methods: Twenty-nine subjects were screened under two conditions—walking barefoot (WB); with shoes and insoles (WSI), during three trials for each. Assessments were performed through the 3D gait analysis, using an optoelectronic system, force platforms, and video recording. Specifically, synthetic indices of gait kinematics, i.e., gait profile score (GPS) and gait variable score (GVS) were calculated and compared with Wilcoxon signed-rank test, to evaluate between-conditions. Results: Significant variations were found in GVS foot progression index, representative of foot rotation during walking, in adolescents only. Conclusions: Bilateral FOs has a positive immediate impact on gait quality in adolescents with DS, as confirmed by quantitative analysis. FOs prescription is an evidence-based early approach to slow down biomechanical abnormalities and prevent relative symptoms.

scholarly journals Skeleton based gait recognition for long and baggy clothes

2019 ◽  
Vol 277 ◽  
pp. 03005
Author(s):  
Abrar Alharbi ◽  
Fahad Alharbi ◽  
Eiji Kamioka
Keyword(s):  
Gait Recognition ◽  
User Interaction ◽  
Microsoft Kinect ◽  
Human Gait ◽  
Kinect Sensor ◽  
Recognition Method ◽  
Walking Gait ◽  
Biometric Feature ◽  
Gait Database ◽  
Microsoft Kinect Sensor

Human gait is a significant biometric feature used for the identification of people by their style of walking. Gait offers recognition from a distance at low resolution while requiring no user interaction. On the other hand, other biometrics are likely to require a certain level of interaction. In this paper, a human gait recognition method is presented to identify people who are wearing long baggy clothes like Thobe and Abaya. Microsoft Kinect sensor is used as a tool to establish a skeleton based gait database. The skeleton joint positions are obtained and used to create five different datasets. Each dataset contained different combination of joints to explore their effectiveness. An evaluation experiment was carried out with 20 walking subjects, each having 25 walking sequences in total. The results achieved good recognition rates up to 97%.

Terrain Characterization Using Modified RANSAC Analysis of Human Gait Data

2012 ◽  
Author(s):  
Christopher Sullivan ◽  
Elizabeth A. DeBartolo ◽  
Kathleen Lamkin-Kennard
Keyword(s):  
Side Effects ◽  
Surface Profile ◽  
Lower Leg ◽  
Gait Pattern ◽  
Test Subject ◽  
Foot Drop ◽  
Human Gait ◽  
Post Stroke ◽  
The Many ◽  
Foot Orthotic

One of the many lasting side effects of a stroke can be foot drop, or an inability to dorsiflex the foot. In order to remedy this, many people wear an ankle-foot orthotic (AFO) post-stroke. One of the many troubles these individuals face is in dealing with obstacles such as stairs and ramps, because the AFO limits the plantarflexion that is natural in navigating these obstacles [1,2]. The end goal of this research is to create an active AFO that adapts to changing ground terrain, providing a more natural gait pattern. This paper presents the first part of this work: a means for identifying terrain in order to control an AFO. This has been accomplished using an infrared (IR) range sensor attached to the lower leg, used to measure the surface profile of the ground just ahead of a test subject. Using a modified RANSAC technique to fit experimental gait data, standardized gait profiles for different terrain have been quantified and shown to be reproducible, indicating the utility of the technique for terrain identification and AFO control.

Gait analysis

2013 ◽  
Author(s):  
Sophie Roberts ◽  
Sharon Dixon
Keyword(s):  
Gait Analysis ◽  
Motion Analysis ◽  
Visual Analysis ◽  
Three Dimensional ◽  
Human Gait ◽  
Two Dimensional ◽  
3D Motion ◽  
Walking Gait ◽  
3D Motion Analysis ◽  
Technological Analysis

Gait analysis describes the process of systematically quantifying mechanical aspects of walking or running to aid in the examination of a patient/client. In the publication Gait Analysis: An Introduction, Whittle (2002) identifies the eye as being the first tool in this assessment, with technology being available to supplement this visual analysis. Technological analysis tools include two-dimensional (2D) video, three-dimensional (3D) motion analysis, pressure plates, and pressure insoles. The application of technology has increased our understanding of human gait substantially. This chapter introduces the basic tools of gait analysis and highlights specific considerations when selecting appropriate tools for the assessment of walking gait. Details of running gait are provided in Chapter 1.8....

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