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.

Modeling of Gait Adaptations to Minimize Plantar Tissue Strain During Walking

2010 ◽  
Author(s):  
J. P. Halloran ◽  
M. Ackermann ◽  
A. Erdemir ◽  
A. J. van den Bogert
Keyword(s):  
Computational Models ◽  
Neuromuscular Control ◽  
Cost Effective ◽  
Element Model ◽  
Gait Pattern ◽  
Human Gait ◽  
Successful Implementation ◽  
Cost Effective Approach ◽  
Musculoskeletal Systems ◽  
Musculoskeletal Simulations

Computational models often represent the most cost-effective approach to predict the behavior of musculoskeletal systems. Historically, dynamic musculoskeletal simulations have simplified representations of soft tissue structures, which makes it impossible to investigate the relationship between neuromuscular control and tissue loading. It is possible to overcome this limiting assumption by coupling a finite element model, e.g. of a foot, directly with a musculoskeletal model, e.g. of the lower extremity [1]. The goal of the current study was to apply this concept to the control of human gait and demonstrate that it is possible to have a gait pattern that minimizes internal foot deformation while satisfying an overall movement goal, e.g. minimal deviations from normal gait. Successful implementation will have wide-ranging implications such as finding therapeutic and rehabilitative movement patterns that relieve localized tissue loading.

scholarly journals Robust Step Detection from Different Waist-Worn Sensor Positions: Implications for Clinical Studies

2020 ◽  
Vol 4 (1) ◽  
pp. 50-58
Author(s):  
Matthias  Tietsch ◽  
Amir Muaremi ◽  
Ieuan Clay ◽  
Felix Kluge ◽  
Holger Hoefling ◽  
...  
Keyword(s):  
Clinical Study ◽  
Clinical Studies ◽  
Neurological Diseases ◽  
Human Gait ◽  
Robust Solution ◽  
Step Detection ◽  
Study Protocols ◽  
Study Results ◽  
Wide Range ◽  
The Impact

Analyzing human gait with inertial sensors provides valuable insights into a wide range of health impairments, including many musculoskeletal and neurological diseases. A representative and reliable assessment of gait requires continuous monitoring over long periods and ideally takes place in the subjects’ habitual environment (real-world). An inconsistent sensor wearing position can affect gait characterization and influence clinical study results, thus clinical study protocols are typically highly proscriptive, instructing all participants to wear the sensor in a uniform manner. This restrictive approach improves data quality but reduces overall adherence. In this work, we analyze the impact of altering the sensor wearing position around the waist on sensor signal and step detection. We demonstrate that an asymmetrically worn sensor leads to additional odd-harmonic frequency components in the frequency spectrum. We propose a robust solution for step detection based on autocorrelation to overcome sensor position variation (sensitivity = 0.99, precision = 0.99). The proposed solution reduces the impact of inconsistent sensor positioning on gait characterization in clinical studies, thus providing more flexibility to protocol implementation and more freedom to participants to wear the sensor in the position most comfortable to them. This work is a first step towards truly position-agnostic gait assessment in clinical settings.

scholarly journals The Quality of Statistical Reporting and Data Presentation in Predatory Dental Journals Was Lower Than in Non-Predatory Journals

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 468
Author(s):  
Pentti Nieminen ◽  
Sergio E. Uribe
Keyword(s):  
Open Access ◽  
Peer Review ◽  
Scientific Journals ◽  
Mean Values ◽  
Data Presentation ◽  
Predatory Journals ◽  
Study Results ◽  
The Mean ◽  
Highly Cited

Proper peer review and quality of published articles are often regarded as signs of reliable scientific journals. The aim of this study was to compare whether the quality of statistical reporting and data presentation differs among articles published in ‘predatory dental journals’ and in other dental journals. We evaluated 50 articles published in ‘predatory open access (OA) journals’ and 100 clinical trials published in legitimate dental journals between 2019 and 2020. The quality of statistical reporting and data presentation of each paper was assessed on a scale from 0 (poor) to 10 (high). The mean (SD) quality score of the statistical reporting and data presentation was 2.5 (1.4) for the predatory OA journals, 4.8 (1.8) for the legitimate OA journals, and 5.6 (1.8) for the more visible dental journals. The mean values differed significantly (p < 0.001). The quality of statistical reporting of clinical studies published in predatory journals was found to be lower than in open access and highly cited journals. This difference in quality is a wake-up call to consume study results critically. Poor statistical reporting indicates wider general lower quality in publications where the authors and journals are less likely to be critiqued by peer review.

scholarly journals Usability Evaluation of Slanted Computer Mice

2021 ◽  
Vol 18 (8) ◽  
pp. 3854
Author(s):  
Miguel L. Lourenço ◽  
Fátima Lanhoso ◽  
Denis A. Coelho
Keyword(s):  
Ease Of Use ◽  
Perceived Ease Of Use ◽  
Slant Angle ◽  
Mean Values ◽  
Random Subset ◽  
Hand Size ◽  
Preference Rank ◽  
Size Groups ◽  
Subjective Usability ◽  
Conventional Device

Prevention of musculoskeletal disorders is supported by use of slanted rather than horizontal pointing devices, but user acceptance of the former may be compromised due to lower perceived ease of use. This study compares subjectively rated usability (N = 37) for three sizes of slanted computer mice and includes a horizontal small conventional device as a reference. For a random subset of the sample (n = 10), objective usability parameters were also elicited. Participants followed a standard protocol which is based on executing graphical pointing, steering, and dragging tasks generated by a purpose-built software. Subjective ratings were collected for each of the four pointing devices tested. The three slanted devices differed in size but were chosen because of an approximately similar slant angle (around 50–60 degrees relative to the horizontal plane). Additionally, effectiveness and efficiency were objectively calculated based on data recorded for the graphical tasks’ software for a random subset of the participants (n = 10). The results unveil small differences in preference in some of the subjective usability parameters across hand size groups. This notwithstanding, the objective efficiency results are aligned with the subjective results, indicating consistency with the hypothesis that smaller slanted devices relative to the user’s hand size are easier to use than larger ones. Mean values of weighted efficiency recorded in the study range from 68% to 75%, with differences across devices coherent with preference rank orders.

scholarly journals Antler growth in male roe deer in field hunting grounds in Vojvodina: Effect of age on trophy value

2006 ◽  
pp. 109-122
Author(s):  
Dragan Gacic
Keyword(s):  
Old Age ◽  
Representative Sample ◽  
Roe Deer ◽  
Age Groups ◽  
Capreolus Capreolus ◽  
Mean Values ◽  
Antler Growth ◽  
Study Results ◽  
Effect Of Age ◽  
Healthy Males

Antler growth in male roe deer (Capreolus capreolus L) was studied on the representative sample consisting of 546 trophies (227 from Backa and 319 from Banat) hunted in the period 19982005. No significant differences in antler characters and trophy values were noted between Backa and Banat (except weight of antlers for 5 year old males), and the data for both regions were pooled. Antler growth is a curvilinear function of age. Mean values of length, weight and volume of antlers, and total trophy score varied significantly between the males in different age groups. The study results prove that in Vojvodina field hunting grounds, healthy males attain the culmination in antler growth and trophy value at the age of six years but already after the age of seven years, they show the first sign of old age and decline.

scholarly journals Somatic development and some motor performances of young girls based on age and birth season

2013 ◽  
Vol 7 (1) ◽  
pp. 117-123
Author(s):  
Eszter Szabó ◽  
Danica Keczeli ◽  
István Farmosi ◽  
Sándorné Gaál ◽  
Katalin Keresztesi
Keyword(s):  
Body Weight ◽  
Body Height ◽  
Physical Development ◽  
Mean Value ◽  
Birth Season ◽  
Mean Values ◽  
Somatic Development ◽  
Young Girls ◽  
Study Results ◽  
Continuous Running

There are numerous publications in the literature reporting physical development and motor performances of children of different ages based on sex and various environmental factors. However, there are not many publications on the birth season effect. The aim of the study was to evaluate the differences among children in physical development and motor performances based on age and birth season. Physical development described by body height and body weight, in addition to motor performance indicators including the twenty-metre dash, standing broad jump, six minutes of continuous running, throwing with a stuffed ball, and obstacle race-tests were studied. The survey included the participation of 426 girls. From the group the seven-, eight-, and nine-year olds numbered 148, 191, and 87 respectively. The group of girls who were born in winter, spring, summer and autumn numbered 114, 110, 89 and119 respectively. The tested data were evaluated with unitrate analyses of variance using SPSS statistical package. Mean value, standard error, standard deviation and coefficient of variation were calculated. The significance of differences between mean values was evaluated using “t” test. Differences with an error below 5% were considered to be significant. Furthermore, a correlation analysis was used to evaluate the relationship between season of birth, body development and motor-related performance data. Age, body height, body weight, throwing a stuffed ball in one hand, twenty-metre dash, six minutes of continuous running, and obstacle race-test are interdependent variables of development and motor performances of young girls of this age. Data from the study results show that the children group included in the tests was quite homogenous in body height, but heterogeneous in body weight and motor performances. Physical development and four of the five evaluated sport skills were affected by the birth season. Development and motor performances of the summer- and autumn-born girls are generally better than those born in winter or spring. Differences are significant except for the obstacle race-test. Age, body height, body weight, throwing with a stuffed ball in one hand, twentymetre dash, six minutes of continuous running, and the obstacle race-test seem to be interdependent variables of development and motor performances of young girls of this age.

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.

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.

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.

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