Comparison of Functional Regression and Nonfunctional Regression Approaches to the Study of the Walking Velocity Effect in Force Platform Measures

This article describes a public data set with the three-dimensional kinematics of the whole body and the ground reaction forces (with a dual force platform setup) of subjects standing still for 60 s in different conditions, in which the vision and the standing surface were manipulated. Twenty-seven young subjects and 22 old subjects were evaluated. The data set comprises a file with metadata plus 1,813 files with the ground reaction force (GRF) and kinematics data for the 49 subjects (three files for each of the 12 trials plus one file for each subject). The file with metadata has information about each subject’s sociocultural, demographic, and health characteristics. The files with the GRF have the data from each force platform and from the resultant GRF (including the center of pressure data). The files with the kinematics have the three-dimensional position of the 42 markers used for the kinematic model of the whole body and the 73 calculated angles. In this text, we illustrate how to access, analyze, and visualize the data set. All the data is available at Figshare (DOI: 10.6084/m9.figshare.4525082 ), and a companion Jupyter Notebook (available at https://github.com/demotu/datasets ) presents the programming code to generate analyses and other examples.

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A data set with kinematic and ground reaction forces of human balance

10.7287/peerj.preprints.2865v1 ◽

2017 ◽

Author(s):

Damiana A dos Santos ◽

Claudiane A Fukuchi ◽

Reginaldo K Fukuchi ◽

Marcos Duarte

Keyword(s):

Center Of Pressure ◽

Reaction Force ◽

Kinematic Model ◽

Three Dimensional ◽

Force Platform ◽

Whole Body ◽

Ground Reaction Forces ◽

Data Set ◽

Public Data ◽

Reaction Forces

This article describes a public data set with the three-dimensional kinematics of the whole body and the ground reaction forces (with a dual force platform setup) of subjects standing still for 60 s in different conditions, in which the vision and the standing surface were manipulated. Twenty-seven young subjects and 22 old subjects were evaluated. The data set comprises a file with metadata plus 1,813 files with the ground reaction force (GRF) and kinematics data for the 49 subjects (three files for each of the 12 trials plus one file for each subject). The file with metadata has information about each subject’s sociocultural, demographic, and health characteristics. The files with the GRF have the data from each force platform and from the resultant GRF (including the center of pressure data). The files with the kinematics have the three-dimensional position of the 42 markers used for the kinematic model of the whole body and the 73 calculated angles. In this text, we illustrate how to access, analyze, and visualize the data set. All the data is available at Figshare (DOI: 10.6084/m9.figshare.4525082 ), and a companion Jupyter Notebook (available at https://github.com/demotu/datasets ) presents the programming code to generate analyses and other examples.

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A data set with kinematic and ground reaction forces of human balance

PeerJ ◽

10.7717/peerj.3626 ◽

2017 ◽

Vol 5 ◽

pp. e3626 ◽

Cited By ~ 5

Author(s):

Damiana A. dos Santos ◽

Claudiane A. Fukuchi ◽

Reginaldo K. Fukuchi ◽

Marcos Duarte

Keyword(s):

Center Of Pressure ◽

Reaction Force ◽

Three Dimensional ◽

Force Platform ◽

Ground Reaction Forces ◽

Data Set ◽

Public Data ◽

Human Balance ◽

Reaction Forces ◽

Young Subjects

This article describes a public data set containing the three-dimensional kinematics of the whole human body and the ground reaction forces (with a dual force platform setup) of subjects who were standing still for 60 s in different conditions, in which the subjects’ vision and the standing surface were manipulated. Twenty-seven young subjects and 22 old subjects were evaluated. The data set comprises a file with metadata plus 1,813 files with the ground reaction force (GRF) and kinematics data for the 49 subjects (three files for each of the 12 trials plus one file for each subject). The file with metadata has information about each subject’s sociocultural, demographic, and health characteristics. The files with the GRF have the data from each force platform and from the resultant GRF (including the center of pressure data). The files with the kinematics contain the three-dimensional positions of 42 markers that were placed on each subject’s body and 73 calculated joint angles. In this text, we illustrate how to access, analyze, and visualize the data set. All the data is available at Figshare (DOI:10.6084/m9.figshare.4525082), and a companion Jupyter Notebook presents programming code to access the data set, generate analyses and other examples. The availability of a public data set on the Internet that contains these measurements and information about how to access and process this data can potentially boost the research on human postural control, increase the reproducibility of studies, and be used for training and education, among other applications.

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Functional Data Analysis in Sport Science: Example of Swimmers’ Progression Curves Clustering

Applied Sciences ◽

10.3390/app8101766 ◽

2018 ◽

Vol 8 (10) ◽

pp. 1766 ◽

Cited By ~ 2

Author(s):

Arthur Leroy ◽

Andy MARC ◽

Olivier DUPAS ◽

Jean Lionel REY ◽

Servane Gey

Keyword(s):

Data Analysis ◽

Functional Data Analysis ◽

Functional Data ◽

Clustering Algorithms ◽

Principal Component ◽

Continuous Functions ◽

Functional Principal Component Analysis ◽

Data Set ◽

Functional Clustering ◽

Sport Science

Many data collected in sport science come from time dependent phenomenon. This article focuses on Functional Data Analysis (FDA), which study longitudinal data by modelling them as continuous functions. After a brief review of several FDA methods, some useful practical tools such as Functional Principal Component Analysis (FPCA) or functional clustering algorithms are presented and compared on simulated data. Finally, the problem of the detection of promising young swimmers is addressed through a curve clustering procedure on a real data set of performance progression curves. This study reveals that the fastest improvement of young swimmers generally appears before 16 years old. Moreover, several patterns of improvement are identified and the functional clustering procedure provides a useful detection tool.

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Machine Learning-Based Estimation of Ground Reaction Forces and Knee Joint Kinetics from Inertial Sensors While Performing a Vertical Drop Jump

Sensors ◽

10.3390/s21227709 ◽

2021 ◽

Vol 21 (22) ◽

pp. 7709

Author(s):

Serena Cerfoglio ◽

Manuela Galli ◽

Marco Tarabini ◽

Filippo Bertozzi ◽

Chiarella Sforza ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Knee Joint ◽

Field Analysis ◽

Ground Reaction Forces ◽

Drop Jump ◽

Joint Moments ◽

Data Set ◽

Joint Kinetics ◽

Reaction Forces

Nowadays, the use of wearable inertial-based systems together with machine learning methods opens new pathways to assess athletes’ performance. In this paper, we developed a neural network-based approach for the estimation of the Ground Reaction Forces (GRFs) and the three-dimensional knee joint moments during the first landing phase of the Vertical Drop Jump. Data were simultaneously recorded from three commercial inertial units and an optoelectronic system during the execution of 112 jumps performed by 11 healthy participants. Data were processed and sorted to obtain a time-matched dataset, and a non-linear autoregressive with external input neural network was implemented in Matlab. The network was trained through a train-test split technique, and performance was evaluated in terms of Root Mean Square Error (RMSE). The network was able to estimate the time course of GRFs and joint moments with a mean RMSE of 0.02 N/kg and 0.04 N·m/kg, respectively. Despite the comparatively restricted data set and slight boundary errors, the results supported the use of the developed method to estimate joint kinetics, opening a new perspective for the development of an in-field analysis method.

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Effects of Velocity and Weight Support on Ground Reaction Forces and Metabolic Power during Running

Journal of Applied Biomechanics ◽

10.1123/jab.24.3.288 ◽

2008 ◽

Vol 24 (3) ◽

pp. 288-297 ◽

Cited By ~ 63

Author(s):

Alena M. Grabowski ◽

Rodger Kram

Keyword(s):

Body Weight ◽

Normal Weight ◽

Treadmill Running ◽

Ground Reaction Forces ◽

Metabolic Power ◽

Data Set ◽

Weight Support ◽

Scientific Goal ◽

Reaction Forces ◽

Support Body Weight

The biomechanical and metabolic demands of human running are distinctly affected by velocity and body weight. As runners increase velocity, ground reaction forces (GRF) increase, which may increase the risk of an overuse injury, and more metabolic power is required to produce greater rates of muscular force generation. Running with weight support attenuates GRFs, but demands less metabolic power than normal weight running. We used a recently developed device (G-trainer) that uses positive air pressure around the lower body to support body weight during treadmill running. Our scientific goal was to quantify the separate and combined effects of running velocity and weight support on GRFs and metabolic power. After obtaining this basic data set, we identified velocity and weight support combinations that resulted in different peak GRFs, yet demanded the same metabolic power. Ideal combinations of velocity and weight could potentially reduce biomechanical risks by attenuating peak GRFs while maintaining aerobic and neuromuscular benefits. Indeed, we found many combinations that decreased peak vertical GRFs yet demanded the same metabolic power as running slower at normal weight. This approach of manipulating velocity and weight during running may prove effective as a training and/or rehabilitation strategy.

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An algorithm to decompose ground reaction forces and moments from a single force platform in walking gait

Medical Engineering & Physics ◽

10.1016/j.medengphy.2014.08.002 ◽

2014 ◽

Vol 36 (11) ◽

pp. 1530-1535 ◽

Cited By ~ 8

Author(s):

David Villeger ◽

Antony Costes ◽

Bruno Watier ◽

Pierre Moretto

Keyword(s):

Force Platform ◽

Ground Reaction Forces ◽

Walking Gait ◽

Reaction Forces ◽

Single Force

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Obesity: Effects on Gait in an Osteoarthritic Population

Journal of Applied Biomechanics ◽

10.1123/jab.12.2.161 ◽

1996 ◽

Vol 12 (2) ◽

pp. 161-172 ◽

Cited By ~ 41

Author(s):

Stephen P. Messier ◽

Walter H. Ettinger ◽

Thomas E. Doyle ◽

Timothy Morgan ◽

Margaret K. James ◽

...

Keyword(s):

Older Adults ◽

High Speed ◽

Ground Reaction Forces ◽

Vertical Force ◽

Kinematic Data ◽

Knee Oa ◽

Significant Relationships ◽

Gait Mechanics ◽

Reaction Forces ◽

Walking Velocity

The purpose of our study was to examine the association between obesity and gait mechanics in older adults with knee osteoarthritis (OA). Subjects were 101 older adults (25 males and 76 females) with knee OA. High-speed video analysis and a force platform were used to record sagittal view lower extremity kinematic data and ground reaction forces. Increased body mass index (BMI) was significantly related to both decreases in walking velocity and knee maximum extension. There were no significant relationships between BMI and any of the hip or ankle kinematic variables. BMI was directly related to vertical force minimum and maximum values, vertical impulse, and loading rate. Increases in braking and propulsive forces were significantly correlated with increased BMI. Maximum medially and laterally directed ground reaction forces were positively correlated with BMI. Our results suggests that, in subjects with knee OA, obesity is associated with an alteration in gait.

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Force Measurements during Cross-Country Skiing

International Journal of Sport Biomechanics ◽

10.1123/ijsb.3.4.370 ◽

1987 ◽

Vol 3 (4) ◽

pp. 370-381 ◽

Cited By ~ 39

Author(s):

Paavo V. Komi

Keyword(s):

Functional Significance ◽

Force Platform ◽

Ground Reaction Forces ◽

Force Measurements ◽

Experimental Conditions ◽

Reaction Forces ◽

Cross Country Skiing ◽

Cross Country ◽

Platform System ◽

Component Force

To understand cross-country (X-C) siding it is important to record and identity forces of skis and poles separately and together. They both contribute to the forward progression, but their functional significance may be more complex than that of the ground reaction forces in running and walking. This report presents two methods to record forces on skis and poles during normal X-C skiing. A long force-platform system with four rows of 6-m long plates is placed under the snow track for recording of Fz and Fy forces of each ski and pole separately. This system is suitable especially for the study of diagonal technique under more strict experimental conditions. The second system consists of small lightweight Fz and Fy component force plates which are installed under the boot and binding. These plates can be easily changed from one ski to another, and telemetric recording allows free skiing over long distances and with different skiing techniques, including skating. The presentation emphasizes the integrated use of either system together with simultaneous cinematographic and electromyographic recordings.

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Identifying and Classifying Aberrant Response Patterns Through Functional Data Analysis

Journal of Educational and Behavioral Statistics ◽

10.3102/1076998620911941 ◽

2020 ◽

Vol 45 (6) ◽

pp. 719-749

Author(s):

Eduardo Doval ◽

Pedro Delicado

Keyword(s):

Data Analysis ◽

Functional Data Analysis ◽

Functional Data ◽

Simulated Data ◽

Real Data ◽

Response Patterns ◽

Fit Indices ◽

Person Fit ◽

Data Set ◽

Aberrant Response Patterns

We propose new methods for identifying and classifying aberrant response patterns (ARPs) by means of functional data analysis. These methods take the person response function (PRF) of an individual and compare it with the pattern that would correspond to a generic individual of the same ability according to the item-person response surface. ARPs correspond to atypical difference functions. The ARP classification is done with functional data clustering applied to the PRFs identified as ARP. We apply these methods to two sets of simulated data (the first is used to illustrate the ARP identification methods and the second demonstrates classification of the response patterns flagged as ARP) and a real data set (a Grade 12 science assessment test, SAT, with 32 items answered by 600 examinees). For comparative purposes, ARPs are also identified with three nonparametric person-fit indices (Ht, Modified Caution Index, and ZU3). Our results indicate that the ARP detection ability of one of our proposed methods is comparable to that of person-fit indices. Moreover, the proposed classification methods enable ARP associated with either spuriously low or spuriously high scores to be distinguished.

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