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40 results for “Human Walking”
Full-Body 3D Human Gait Dataset walking on flat ground
<p>This dataset contains full-body 3D gait data collected from 26 healthy participants (10 males, 16 females) with an average age of 28.19 ± 7.77 years. Data was captured using the Xsens Awinda MTw inertial measurement system, comprising 17 wireless sensors operating at a 60Hz sampling frequency.</p> <p>Key Features:</p> <ul> <li>Full-body motion data using MVN Analyze software's full-body model</li> <li>Anthropometric measurements: height (170.5 ± 8.61 cm), foot length (26.47 ± 1.88 cm), shoulder width (39.32 ± 7.79 cm), and wrist span (131.36 ± 8.85 cm)</li> <li>Four distinct walking paths: Mixed (straight and curved), Circle (3m diameter), Turn (180-degree turns), and Zigzag</li> <li>Total of 1,024,295 frames (17,071.58 seconds) of gait recordings</li> <li>Average of 3,568.97 ± 1,204.26 frames per recording (59.48 ± 20.07 seconds)</li> </ul> <p>The dataset includes various walking patterns designed to capture a wide range of gait characteristics, including straight walks, gentle curves, sharp turns, and zigzag movements. Participants were allowed some freedom in executing turns, particularly in the Zigzag and Mixed paths, to introduce natural variations in gait patterns.</p> <p>This comprehensive dataset is suitable for gait analysis, biomechanics research, and the development of motion synthesis algorithms, particularly those focused on normal walking patterns on a fixed surface with various turning scenarios.</p> <p>Dataset Structure:</p> <ol> <li>'<strong>participants.xlsx</strong>': An Excel file containing participant codes and their anthropometric data.</li> <li>'<strong>data</strong>' folder: Contains subdirectories named with participant codes. <ul> <li>Each participant subdirectory contains CSV files of different gait recordings for that participant.</li> </ul> </li> </ol> <p>This dataset was collected as part of the study:</p> <p><strong>Carneros-Prado, D., Dobrescu, C. C., Cabañero, L., Villa, L., Altamirano-Flores, Y. V., Lopez-Nava, I. H., … & Hervás, R. (2024). Synthetic 3D full-body skeletal motion from 2D paths using RNN with LSTM cells and linear networks. </strong><strong><em>Computers in Biology and Medicine, 180,</em></strong><strong> 108943.</strong></p>
Effect of hip abduction assistance on metabolic cost and balance during human walking
<p>The use of wearable robots to provide walking assistance has rapidly grown over the last decade, with significant advances made in robot design and control methods toward reducing physical effort while performing an activity. The reduction in the walking effort has mainly been achieved by assisting forward progression in the sagittal plane. Human gait, however, is a complex movement that combines motions in three planes, not only the sagittal but also the transverse and frontal planes. In the frontal plane, the hip joint plays a key role in gait, including balance. However, wearable robots targeting this motion have rarely been investigated. In this study, we developed a hip abduction assistance wearable robot by formulating the hypothesis that assistance that mimics the biological hip abduction moment or power could reduce the metabolic cost of walking and affect the dynamic balance. We found that hip abduction assistance with a biological moment second peak mimic profile reduced the metabolic cost of walking by 11.6% compared to the normal walking condition (p=0.009). The assistance also influenced balance-related parameters, including the margin of stability. Hip abduction assistance influenced the center-of-mass movement in the mediolateral direction. When the robot assistance was applied as the center of mass moved toward the opposite leg, the assistance replaced some of the efforts that would have otherwise been provided by a human. This indicates that hip abduction assistance can reduce physical effort during human walking while influencing balance.</p>
Fig. 13. Knuckle walk. A in Anatomical Study of the Right Forearm and Hand of One Western Gorilla (Gorilla gorilla) for Comparison with Humans with Respect to Motions of the Thumb and Fingers
Fig. 13. Knuckle walk. A. The trunk tilts backward because of longer forelimbs; B. The knuckle walk of gorillas is walking with MP joints in extension and PIP joints in deep flexion bearing the weight on the dorsal aspects of the middle phalanges of the hands. Sketches are drawn imitating the walking style shown in movies of NHK BS TV program (Yamagiwa, 2012).
DS6.SSSA-02. Human_Walking_Dataset_at_SSSA. Dataset for characterizing the walking behavior of subjects and identification of changes in the motion patterns, based on RGB-D cameras.
<p>This dataset is used for characterizing the wakling behavior of subjects. It is based on RGB-D camerasand obtained through data collection experiments at the premises of the Percro Labotory, TeCIP Intitute, Scuola Superiore Sant'Anna (Pisa, Italy). Data are collected for the gait patterns of 9 healthy participants.</p>
Data from: Adaptive multi-objective control explains how humans make lateral maneuvers while walking
<p>To successfully traverse their environment, humans often perform maneuvers to achieve desired task goals while simultaneously maintaining balance. Humans accomplish these tasks primarily by modulating their foot placements. As humans are more unstable laterally, we must better understand how humans modulate lateral foot placement. We previously developed a theoretical framework and corresponding computational models to describe how humans regulate lateral stepping during straight-ahead continuous walking. We identified goal functions for step width and lateral body position that define the walking task and determine the set of all possible task solutions as Goal Equivalent Manifolds (GEMs). Here, we used this framework to determine if humans can regulate lateral stepping during non-steady-state lateral maneuvers by minimizing errors consistent with these goal functions. Twenty young healthy adults each performed four lateral lane-change maneuvers in a virtual reality environment. Extending our general lateral stepping regulation framework, we first re-examined the requirements of such transient walking tasks. Doing so yielded new theoretical predictions regarding how steps during any such maneuver should be regulated to minimize error costs, consistent with the goals required at each step and with how these costs are adapted at each step during the maneuver. Humans performed the experimental lateral maneuvers in a manner consistent with our theoretical predictions. Furthermore, their stepping behavior was well modeled by allowing the parameters of our previous lateral stepping models to adapt from step to step. To our knowledge, our results are the first to demonstrate humans might use evolving cost landscapes in real time to perform such an adaptive motor task and, furthermore, that such adaptation can occur quickly – over only one step. Thus, the predictive capabilities of our general stepping regulation framework extend to a much greater range of walking tasks beyond just normal, straight-ahead walking.</p>
Effect of hip abduction assistance on metabolic cost and balance during human walking
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Data from: Human walking biomechanics on sand substrates of varying foot sinking depth
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Data from: Adaptive multi-objective control explains how humans make lateral maneuvers while walking
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Different functional networks underlying human walking with pulling force fields acting in forward or backward directions
<p><span>Walking with pulling force fields acting at the body center of mass (in </span><span>the </span><span>forward or backward directions) is compatible with inclined walking and is used in clinical practice for gait training. From the perspective of known differences in the motor strategies that underlie walking with the respective force fields, the present study elucidated whether the adaptation acquired by walking on a split-belt treadmill with either one of the force fields affects subsequent walking in other directions. Walking with the force field induced an adaptive and de-adaptive behavior of the subjects</span><span>, with the aspect evident </span><span>in the anterior breaking and posterior propulsive impulses of the ground reaction force as parameters. In the parameters, </span><span>the </span><span>adaptation acquired during walking with </span><span>a force field </span><span>acting in one direction was transferred to that in </span><span>the opposite direction only partially. </span><span>Furthermore, </span><span>the adaptation that occurred </span><span>while walking in </span><span>a force field </span><span>in one direction was rarely washed out by subsequent walking in </span><span>a force field </span><span>in </span><span>the opposite direction</span> <span>and thus was maintained independently of the other</span><span>. These results demonstrated possible independence in the neural functional networks capable of controlling walking in each movement task with </span><span>an opposing force field</span><span>.</span></p>
A simple method reveals minimum time required to quantify steady-rate metabolism and net cost of transport for human walking
<p>The U-shaped net cost of transport (COT) curve of walking has helped scientists understand the biomechanical basis that underlies energy minimization during walking. However, to produce an individual's net COT curve, data must be analyzed during periods of steady-rate metabolism. Traditionally, studies analyze the last few minutes of a 6–10 min trial, assuming that steady-rate metabolism has been achieved. Yet, it is possible that an individual achieves steady rates of metabolism much earlier. However, there is no consensus on how to objectively quantify steady-rate metabolism across a range of walking speeds. Therefore, we developed a simple slope method to determine the minimum time needed for humans to achieve steady rates of metabolism across slow to fast walking speeds.We hypothesized that a shorter time window could be used to produce a net COT curve that is comparable to the net COT curve created using traditional methods. We analyzed metabolic data from 21 subjects who completed several 7 min walking trials ranging from 0.50 to 2.00 m s−1. We partitioned the metabolic data for each trial into moving 1, 2 and 3 min intervals and calculated their slopes. We statistically compared these slope values with values derived from the last 3 min of the 7 min trial, our 'gold' standard comparison. We found that a minimum of 2 min is required to achieve steady-rate metabolism and that data from 2–4 min yields a net COT curve that is not statistically different from the one derived from experimental protocols that are generally accepted in the field.</p>
Multi-objective control in human walking: insight gained through simultaneous degradation of energetic and motor regulation systems
<p>See ReadMe.txt</p>
Energetics of human locomotion near the walk-run transition speed.
<p><strong>Energetics of human locomotion near the walk-run transition speed.</strong></p> <p> </p> <p>This dataset includes the raw metabolic and mechanical data of 28 young subjects during locomotion at variable speed, walking and running on a treadmill at different speeds and gaits.</p> <p>Characteristics of the experimental group:</p> <p>- Sex: 28 Males - Age: 32. 53 (10.99SD)- height: 175.0 cm (0.008 SD)- weight: 72.96 kg (9.51 SD)</p> <p>Equipment:</p> <p>- Cosmed K5 portable metabolic analyzer- Cosmed Omnia Software v.1.6.5</p> <p>- Vicon Nexus 2.14 (Vicon Motion Systems Ltd, Oxford, UK)</p> <p>Experimental Design: The walking stroke transition speed (W-R Ts) was determined experimentally. Each subject was asked to perform 3 trials on a treadmill (GE T2100, General Electric, USA), with an escalating speed ladder protocol. The ramp was designed to start with a comfortable ride (3.0 km h-1), and to increase speed by 0.5 km.h-1 every 15 s. When the subject began to run, the ramp stopped and the speed was marked on a worksheet. The mean or modal transition speed was taken as the T of the subject. All treadmill tests were performed at the Biomechanics and Motion Analysis Research Laboratory (LIBiAM) of the University of the Republic in Paysandú (Uruguay), at a controlled temperature of 25ºC.</p> <p>The theoretical transition velocity tTs was calculated according to the Froude number equation (Alexander. 1976): v = (nFr g LL) 0.5, where v is the theoretical velocity, g is gravity, LL is the leg length, and nFr the Froude number, which was set to the constant value of 0.5, corresponding to the W-R transition (Alexander & Jayes, 1983; Alejandro, 2003; Bona et al., 2019).</p> <p>Experimental speed ramp:</p> <p>-A custom ascending and descending speed ramp was designed, focused on the transition speed and varied from (Ts = Transition Speed) Ts-20% to Ts+20%, each step with a duration of 5 s. Each ramp cycle lasted 50 s, and was repeated 5 times, for a total test time of 250 s. The trial was repeated twice.</p> <p> </p> <p><em>Mechanical Work (Mechanical cost of transport)</em></p> <p>The time course of the trajectory by <em>BcoM</em> was used to infer changes in the mechanical energies (potential and kinetics) involved. The horizontal work (<em>W<sub>h</sub></em>) was defined as the sum of the increments of the kinetic energy of the <em>BcoM</em> along the forward and mediolateral axes; the vertical work (<em>W<sub>v</sub></em>) was determined by the sum of the increments of gravitational potential energy and kinetic energy along the vertical axis; the external work (<em>W</em><sub>ext</sub> ), the mechanical work done to lift and accelerate the <em>BcoM</em>, was computed as the sum of the increments of the total mechanical energy of the <em>BcoM</em> (potential plus kinetic) (Cavagna et al., 1976; Willems et al., 1995). The internal work (<em>W</em><sub>int</sub>), the work necessary to accelerate the body segments with respects to the <em>BcoM</em>, was estimated with the methodology proposed by Cavagna & Kaneko (1977). <em>W</em><sub>int</sub> and <em>W</em><sub>ext</sub> were summed to give the total mechanical work (<em>W<sub>tot</sub></em>) (Cavagna & Kaneko, 1977; Willems et al., 1995). </p> <p>During locomotion cycles, especially in W, part of the potential energy of the <em>BcoM</em> is converted into kinetic energy, and vice versa, so that the sum of <em>W<sub>h</sub></em> and <em>W<sub>v</sub></em> is greater than the actual work done (<em>W<sub>ext</sub></em>). The difference, expressed as percentage, corresponds to the energy recovery R% (Cavagna et al., 1976), which formula is:</p> <p>R% = (<em>W</em><sub>h</sub> +<em> Wv</em> -<em> W</em><sub>ext</sub>) (<em>W</em><sub>h</sub> + <em>Wv</em>)</p> <p><em> Cost of transport (Metabolic transport cost)</em></p> <p>Oxygen uptake and respiratory quotient were measured breath-by-breath by a portable metabolimeter (K5, Cosmed, Italy). Reference resting values were measured during 5 min in orthostatic quiet position. Each trial was started when the metabolic parameters were near the reference resting values.</p> <p>The <sub>2</sub> (mlO<sub>2</sub>.kg<sup>-1</sup>.min<sup>-1</sup>) and RQ of the last 50 s of each recorded trial, corresponding to the last complete ramp, were averaged. The reference resting <sub>2</sub> was subtracted to the measured one to obtain the net oxygen uptake. VO<sub>2NET</sub> was then converted to mass-specific metabolic rate (W kg<sup>-1</sup>) using a RQ based energetic equivalent(P. E. Di Prampero et al., 2015). The C (J kg<sup>-1</sup> m<sup>-1</sup>) was finally obtained by dividing the metabolic rate for the average speed: </p> <p> </p> <p> (2)</p> <p> </p> <p><em>Apparent Mechanical Efficiency (AE)</em></p> <p>The AE was calculated as proposed by Cavagna and Kaneko, ie,</p> <p>AE = <em>W</em><sub>to</sub> C</p> <p>where <em>W</em><sub>tot</sub> is the total mechanical work and C the cost of transport (G. A. Cavagna & Kaneko, 1977).</p> <p> </p> <p><em>Data processing and calculation</em></p> <p>Image preprocessing was performed in Vicon Nexus 2.14 (Vicon Motion Systems Ltd, Oxford, UK), kinematic variable calculation performed with Python 2.7 and ProCalc 1.6 (Vicon Motion Systems Ltd, Oxford, UK), the calculation of mechanical variables was implemented in MatLab (The MathWorks, Inc., California, USA). The calculation of C was performed in Microsoft Excel (Microsoft Office 365).</p> <p> </p> <p>Note: Not all subjects performed the entire protocol. In particular, some data lack follow-up.</p> <p>Analysis of the cost of transportation:</p> <p>All participants signed an informed consent. The protocol was approved by the University's Ethics Committee (#311170-000921-19).<br> </p> <p>The legend of the dataset.</p> <p>There are 3 excel sheets where each row is associated with subjects from 1 to 28.</p> <p>Energy sheet:</p> <p>Subject: Subject</p> <p>Age</p> <p>Weigth</p> <p>Heigth(m)</p> <p>IMC</p> <p>Km x week: kilometers per week</p> <p>Background: history of injuries</p> <p>INT1 km/h: attempt 1</p> <p>INT2 km/h: attempt 2</p> <p>INT3 km/h: Attempt 3</p> <p>Average transition (km/h): average walk-race transition speed</p> <p>Froude estimated PST(m/s)</p> <p>Froude Estimated PST(km/h)</p> <p>Basal Vo2: Basal oxygen consumption in orthostasis</p> <p>VO2/kg/min: Oxygen consumption in the test</p> <p>RQ: RQ in the test</p> <p>VO2 Net VO2kg/min): VO2 net in the test</p> <p>VO2/kg/s</p> <p>J/kg/s = W/kg</p> <p>C (J/kg/m): transport cost obtained in the test</p> <p> </p> <p>Test: test performed</p> <p>Gait: type of gait that has been evaluated</p> <p>ASC/DESC: place on the ramp (ascending or descending)</p> <p>Stride: stride identification for each type of gait</p> <p>Duty Factor_tr: duty factor</p> <p>Stride Frequency_tr: stride frequency</p> <p>Stride Time_tr: stride time</p> <p>Time: time in the stride</p> <p>Speed in treadmill : speed that occurs in treadmill</p> <p>Distance: distance traveled by the stride</p> <p>Step frequency: frequency of passage</p> <p>Wext: External Work</p> <p>Rec: recovery</p> <p>Wv: trabajo vertical</p> <p>Wh: horizontal work</p> <p>WintTOT: Total internal work</p> <p> </p> <p> </p>
A Dataset of Human Body Tracking of Walking Actions Captured Using Two Azure Kinect Sensors
<p>A dataset of body tracking information is presented. The dataset consists of 315 captured walking sequences. Each sequence is simultaneously captured by two Azure Kinect devices. The two captures are interleaved to effectively double the frame rate. Fifteen participants partook in this experiment. Each experiment consists of seven walking actions, and having three predefined trajectories per experiment. That results in 21 sequences per participant. The data were collected using the Azure Kinect Sensor SDK. They were later processed using the official tools and libraries provided by Microsoft. For each sequence and trajectory, the positions and orientations of thirty-two tracked joints were obtained and saved.</p> <p>The dataset is structured as follows. The experiments from each subject are saved in a single directory. Each directory contains multiple JSON files of timestamped body tracking information to enable the fusion of the two device streams. A calibration file is also provided, enabling the mapping of the coordinates between the two Azure Kinect devices capturing the data (mapping the coordinates of the device known as the Subordinate device to the Master device coordinate system). This data can be used to train neural networks for human motion prediction tasks or test pre-existing algorithms on Azure Kinect data. This dataset could also aid in gait recognition and analysis, as well as in performing action recognition and other surveillance activities.<br> <br> <strong>Journal Publication Citation:</strong><br> Charli Posner, Adrián Sánchez-Mompó, Ioannis Mavromatis, Mustafa Al-Ani,<br> A dataset of human body tracking of walking actions captured using two Azure Kinect sensors,<br> Data in Brief,<br> Volume 49,<br> 2023,<br> 109334,<br> ISSN 2352-3409,<br> https://doi.org/10.1016/j.dib.2023.109334.<br> (https://www.sciencedirect.com/science/article/pii/S2352340923004523)</p>
Cost of step time asymmetry and step length asymmetry in human walking
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Data from: Individual responses of GPS-tagged geese scared off crops by drones or walking humans
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A simple method reveals minimum time required to quantify steady-rate metabolism and net cost of transport for human walking
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Different functional networks underlying human walking with pulling force fields acting in forward or backward directions
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Data for: Quantifying human adaptation to a novel split-belt walking condition after broad experience at different belt speeds
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Humans trade-off energetic cost with fatigue avoidance while walking
<p>See ReadMe.txt</p>
Humans trade-off whole-body energy cost to avoid overburdening muscles while walking
<p>See ReadMe_MATLAB.txt and ReadMe_Excel.txt for more information.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.