This study was supported by the National Natural Science Foundation of China (12202216), Ningbo Natural Science Foundation (2023J128), and the &#34;Mechanics+&#34; Interdisciplinary Top Innovative Youth Fund Project of Ningbo University (GC2024006).

The study received approval from the Ethics Committee of the Research Institute at Ningbo University (Approval Number: ty2022001). All participants provided written informed consent after being briefed on the purpose, requirements, and procedures of the experiment. Details of the consumables, equipment, and software used are listed in the Table of Materials.
1. Experimental preparation
<ol>
	<li>Equipment preparation
	<ol>
		<li>Position eight infrared cameras in appropriate locations during equipment calibration and check for any reflections in the experimental area (e.g., reflective markers on participants&#39; clothing). Remove or cover the reflections, and ensure sufficient lighting.</li>
		<li>Insert the paired encryption dongle into the PC&#39;s parallel port. Start the motion capture cameras, tracking software, force platform amplifier, and ADC.</li>
		<li>Open the tracking software and wait for the eight cameras to initialize. Confirm the process is complete when the camera lights turn from red to green.</li>
		<li>Switch to Camera mode, and expand the System Resources panel on the left. Select all eight cameras.</li>
		<li>Adjust the settings in the left panel under Properties. Set the flash intensity to 0.95-1, gain to 1x, and grayscale mode to Auto. Under Centroid Fitting, set the threshold to 0.2-0.4, minimum circularity ratio to 0.5, and maximum blob height to 50.</li>
		<li>Position the T-frame with markers at the center of the motion capture area. Re-select all eight cameras from the toolbar on the left.</li>
		<li>Perform calibration in the Tools panel on the right. Select the 5 marker Wand &#38; T-Frame calibration object from the T-frame list.</li>
		<li>Click on the Stop button under the Mask Camera option in the System Preparation Tools panel. After the screen turns blue, click on the button again to revert it to the Stop state.</li>
		<li>Click on the Start button under the Calibrate Cameras option. Move the T-frame back and forth within the capture range, ensuring the swinging height matches the camera&#39;s focal height. Stop when the blue lights on the cameras stop flashing.</li>
		<li>Switch the view to 3D Perspective, and place the T-frame back at the center of the motion capture area. Ensure alignment with the force platform boundaries.</li>
		<li>Click on the Start button under the Set Volume Origin option in the right panel.</li>
	</ol>
	</li>
	<li>Pressure platform preparation
	<ol>
		<li>Position two embedded force plates in the center of the motion capture area and synchronize them at 1000 Hz.</li>
		<li>Mark six step widths on the force platform using different colored tapes to represent each condition, including the preferred step width and five variations (13% and 6.5% reductions, and 25%, 13%, and 6.5% increases in leg length). Connect the platform to the PC for data collection. 
		NOTE: Ensure that the system records the foot&#39;s anterior-posterior (Y-axis) coordinates upon contact, with yellow tape marking the preferred step width and other colors indicating variations.</li>
	</ol>
	</li>
	<li>Timing system preparation
	<ol>
		<li>Place a single-beam electronic timing gate on a tripod to record the participants&#39; running speed as they pass over the force plates. 
		NOTE: Ensure the distance between the electronic timing gates is 3 m (as shown in Figure 1).</li>
	</ol>
	</li>
</ol>
2. Participants preparation
<ol>
	<li>Obtain written informed consent from participants, briefly explain the study&#39;s purpose and procedures, and encourage questions. Ensure all participants are right-leg dominant to minimize variability. 
	NOTE: The present study involves 13 healthy Chinese males, with an average age of 22.7 years, weight of 70.9 kg, and height of 1.75 m. Ensure no lower limb injuries or foot deformities in the past six months. This sample selection reduces the influence of age and sex on biomechanical parameters, ensuring statistical validity and reproducibility. The sample size is sufficient for analyzing the effects of step width on biomechanics.</li>
	<li>Instruct participants to wear tight-fitting athletic pants and standardized experimental shoes to minimize gait interference. Remove excess clothing.</li>
	<li>Collect and record anthropometric data: height, weight, leg length, shoulder width, and hip width. 
	NOTE: Measure leg length from the anterior superior iliac spine to the lateral malleolus. Measure shoulder width and hip width as the direct distances between respective anatomical landmarks.</li>
	<li>Keep participants&#39; skin dry and attach reflective markers to anatomical landmarks using double-sided tape. 
	NOTE: Use 38 reflective markers with specific positions and label names detailed in Table 1. Ensure precise placement and secure attachment of the markers. For areas with high motion or uneven surfaces, reinforce the markers with muscle tape or skin-friendly adhesive. After placing the markers, take photographs from the front, back, and sides for documentation.</li>
	<li>Instruct participants to jog for 5 min as a warm-up before starting the static and dynamic recordings.</li>
</ol>
3. Static calibration
<ol>
	<li>Launch the tracking software and create a new database by selecting New Database from the toolbar. In the Data Management section, choose New Patient Classification, then New Patient, and finally, click on New Session to set up a participant information database.</li>
	<li>Click on Go Live in the left toolbar, then use the Split horizontal button in the View interface. Select graphics to view trajectory counts.</li>
	<li>Click on the Subject Preparation in the toolbar. Instruct the participant to stand with feet shoulder-width apart, one foot on the force platform, arms parallel to the shoulders, and looking straight ahead. Click on Start to begin data collection, and hold the position for 10 s. Click on Stop to complete the static capture. 
	NOTE: Switch to &#34;3D Perspective&#34; mode to ensure the camera captures at least one frame showing the 38 reflective markers on the participant&#39;s body.</li>
</ol>
4. Dynamic trials
<ol>
	<li>Inform participants to prepare for the experiment and enter the experimental state.</li>
	<li>Software operations
	<ol>
		<li>Click on Go Live in the left Resources panel and Capture in the right Tools panel. Once participants are ready, start the trial by clicking on Start and end it by clicking on Stop after completion. 
		NOTE: Label different trials with varying step widths in the &#34;Trial Name&#34; section.</li>
	</ol>
	</li>
	<li>Conduct the preferred step width experiment, capturing motion at three different speeds.
	<ol>
		<li>Instruct participants to walk naturally along a straight pathway with two force platforms, placing the left foot on platform A (Kistler) and the right foot on platform B (AMTI).</li>
		<li>Consider the trial successful if the time to complete one run (passing over the force platforms) falls between 0.95-1.05 s at 3.0 m/s or between 0.76-0.86 s at 3.7 m/s. Repeat the trial if the timing does not meet these criteria to minimize the influence of speed fluctuations on running biomechanics. The primary objective is to investigate the influence of speed. 
		NOTE: These time ranges were calculated based on predefined running speeds (3.0 m/s and 3.7 m/s) and the known distance between the electronic timing gates. This ensures participants maintain a consistent running speed throughout the trial and accurately reflect the experimental conditions.</li>
		<li>Instruct participants to run at a speed of 3.7 m/s on a straight pathway with two force platforms. 
		NOTE: If the time passing over the force platform is between 0.76-0.86 s, consider the trial successful; otherwise, retry. Ensure participants consistently place their left foot on platform A and right foot on platform B. Prior studies have shown significant biomechanical differences at various speeds9,10,11,12. Instruct participants to practice multiple times before formal trials to ensure a natural gait. Monitor the trials closely, ensuring correct execution and that markers stay in place. Each condition requires at least five successful trials.</li>
	</ol>
	</li>
	<li>Measuring the preferred step width and setting different step widths
	<ol>
		<li>Based on the definition of step width and previous research, measure the participants&#39; preferred step width in three movement modes8,18,19,20. Using the motion capture system, ensure the Y-axis in the lab coordinate system represents the mediolateral (side-to-side) direction.
		<ol>
			<li>Record the Y-axis coordinates of the left and right heel markers during initial ground contact. Calculate the preferred step width as the difference in the Y-axis coordinates of the two heels, providing a direct measure of the lateral distance between the feet.</li>
		</ol>
		</li>
		<li>Mark the force platforms with different colored tapes corresponding to five step width conditions (step width reduced or increased by 13%, 6.5%, or 25% of leg length). The yellow tape indicates the preferred step width. 
		NOTE: Previous studies have demonstrated significant biomechanical differences when step width changes by 13% relative to leg length5,8,18,19. Based on this finding, introduce additional step width conditions.</li>
		<li>Instruct the participants to walk straight while focusing ahead. Ensure the left foot steps on the yellow tape on platform A and the right foot steps on a different colored tape on platform B. Randomly assign specific tape colors to alter step width. 
		NOTE: Collect at least five successful trials for each step width condition.</li>
		<li>Instruct participants to run at speeds of 3.0 m/s and 3.7 m/s with the five different step widths. 
		NOTE: Ensure participants follow instructions correctly and monitor for marker detachment. Collect at least five successful trials for each condition, and ensure adequate rest time after each test.</li>
	</ol>
	</li>
</ol>
5. Data processing
<ol>
	<li>After the experiment, manually process all collected data using compatible software. Sequentially connect the data according to the marker points set in Table 1.</li>
	<li>Check each static or dynamic trajectory for any missing or interrupted markers on the participants. Use the point connection/fill tool in the software to manually reconnect the missing markers, referencing nearby markers for accuracy.</li>
	<li>Analyze ground reaction forces (GRFs) during dynamic activities by setting a vertical threshold of 20 N to detect foot contact and toe-off, defining the stance phase of each gait cycle. 
	NOTE: GRF data from heel contact to toe-off defines one gait cycle and is saved as raw C3D files.</li>
	<li>Export the processed data in C3D, MOT, TRC, and CSV formats. Ensure each participant completes approximately 55 trials across static, walking, and running conditions with six different step widths. Use the data to compare step widths and limb characteristics. 
	NOTE: Despite strict protocol adherence, some participants may have incomplete data due to marker trajectory disappearance in the equipment system.</li>
</ol>
6. Statistical analysis
<ol>
	<li>Analyze the data using statistical software (e.g., SPSS, Python), focusing on the effects of step width and running speed on lower limb biomechanics.</li>
	<li>Use repeated measures ANOVA to analyze biomechanical parameters, applying Bonferroni correction for post-hoc tests if necessary.</li>
	<li>6.3. Use non-parametric methods like the Friedman test when normality and sphericity assumptions are not met. Set all significance levels at p&#160;&#60; 0.05.</li>
</ol>

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The impact of step width on human running is a multifaceted and significant issue. Step width refers to the lateral distance between the center of the heel and the ground upon initial contact of each foot3. Changing step width may affect stability, balance, biomechanics, and energy expenditure during running. Earlier research suggests that an increase in step width may reduce the hip adduction angle, knee abduction moment, and peak impulse. Conversely, narrowing the step width could potentially increase the knee internal rotation angle and hip adduction angle1,6,7,21. Additionally, variations in step width may influence muscle coordination and movement efficiency20,22,23. Therefore, selecting the appropriate step width can significantly impact running performance and injury risk.
This study specifically found that increasing step width led to a significant reduction in hip adduction angle and knee abduction moment. This aligns with previous studies, which suggest that wider step widths reduce joint loading and potentially lower injury risk. However, the current findings also indicated an increase in knee internal rotation with narrower step widths, a change more pronounced than previously reported. This difference highlights the complexity of biomechanical adaptations to step width changes and suggests the need for individualized assessments in training regimens.
To ensure participants could adapt to the prescribed step widths, they were given clear visual and verbal instructions. Although a 25% increase or decrease in step width represents a significant adjustment, most participants were able to maintain the required step width after several practice trials. Minimal deviations, which were within an acceptable range, were observed. This indicates that the protocol is both feasible and practical for participants. The successful implementation of these step-width conditions supports the validity of this methodology for studying the biomechanical effects of step-width manipulation.
Before each experiment, the motion capture system and laboratory setup were calibrated to reduce noise and ensure optimal marker visibility. Gap filling between markers was performed manually using &#34;pattern fill&#34; to complete the gap based on the shape of another gapless trajectory. The force plates were inspected and zeroed before each experimental session to ensure accurate measurement of ground reaction forces. Similar procedures were implemented as outlined in recently published data descriptor articles13,24,25,26. The original C3D files contain all running marker trajectories and ground reaction forces, which were visually examined and validated using an open-source motion analyzer software. This software was used for post-data analysis to investigate the biomechanical effects of manipulating step width on running performance, loading profiles, and potential injury risk.
The present dataset, which included healthy male participants of Chinese ethnicity, revealed these biomechanical responses. While this focus provides specific insights, it also limits the generalizability of the results. The exclusive use of male recreational runners may not capture the biomechanical nuances present in different genders or levels of athletic experience. Future research should aim to include a more diverse participant pool, incorporating various genders, age groups, and ethnicities to better understand both the universal and specific impacts of step-width changes.
The methodological approach used in this study, including the use of a single-beam electronic timing gate instead of a treadmill, is another potential limitation. This method may introduce velocity measurement errors, as it relies on participants maintaining a consistent speed across the force plates. While this setup reflects more natural running conditions, future studies could use treadmills to control speed more precisely, reducing variability in the data.
The statistical analysis was conducted using repeated measures ANOVA, with significant findings identified at a p &#60; 0.05 level. The use of Bonferroni correction helped manage the risk of Type I errors across multiple comparisons. These methods provided a robust framework for interpreting the data; however, the small sample size means the findings should be considered preliminary.
Overall, this study has contributed new samples to relevant datasets and explored the impact of changes in step width on lower limb kinematics at different speeds, setting the stage for deeper exploration into the biomechanical effects of varying step widths in human movement. The insights highlighted the importance of personalized biomechanical assessments in optimizing performance and reducing injury risk.

Environmental factors, including spatial and temporal elements, directly influence human daily movement. Individuals may adopt different postures and movement patterns while running and walking in various environmental conditions. It is well-established that altering running techniques can impact body biomechanics, with step width being closely associated with stability and balance during human running1,2. Step width is defined as the mediolateral distance between the midfoot and the initial ground contact of each foot, representing a variable in the frontal plane3. During walking and running, short-term variations in step width can influence lower extremity biomechanics across three planes3,4,5.
Numerous studies have demonstrated that step width significantly affects the biomechanics of lower limb joints, kinematics, and kinetics during running. A wider step width reduces hip adduction angles, knee abduction moments, and rearfoot eversion angles, contributing to enhanced stability and potentially lowering injury risk6,7. Conversely, a narrower step width increases knee internal rotation and hip adduction angles, potentially elevating joint loads. Specifically, a narrow step width has been associated with increased variations in knee internal rotation and peak knee abduction torque compared to a normal step width6,7. Additionally, wider step widths have been shown to reduce tibial loading, thereby decreasing stress on the tibia during running8. These findings underscore the critical role of step width in influencing running biomechanics, highlighting its importance in optimizing performance and minimizing injury risk.
Studies have further demonstrated that walking and running speeds influence the biomechanical parameters of the lower limbs9,10,11,12. However, the effects of step width change on biomechanics at varying exercise speeds remain unclear, and limited scientific data regarding human movement under different speed and step width conditions are available. Therefore, this study aims to investigate the impact of step width changes on lower limb biomechanics at different speeds, focusing on key parameters such as hip adduction angle and knee abduction moment.
To address this, a dataset was established comprising 13 healthy male participants aged 20-24 years, including C3D files and ready-to-use kinematic data. Participants were instructed to run at speeds of 3.0 m/s and 3.7 m/s using six distinct step widths. The selection of these step widths and speeds was informed by existing research findings and the current state of open-source datasets on gait biomechanics in the literature13,14,15,16,17. This study aims to examine the acute effects of step width changes on the lower limb kinetic chain while expanding the dataset to provide valuable insights into the relationship between step width and lower limb biomechanics.

<table><tbody><tr><td>AMTI Force Plate</td><td>AMTI</td><td>OR6-7</td><td>Watertown, MA, USA</td></tr><tr><td>Colored Tape for Floor Marking</td><td>Generic</td><td>-</td><td>Used to mark different step widths on force plates, purchased online</td></tr><tr><td>Kistler Force Plate</td><td>Kistler</td><td>9260AA</td><td>Winterthur, Switzerland</td></tr><tr><td>MATLAB</td><td>MathWorks</td><td>Version R2021b</td><td>Data processing and modeling</td></tr><tr><td>Reflective Markers</td><td>Vicon Metrics Ltd.</td><td>-</td><td>Used for marking participant body points</td></tr><tr><td>Single-Beam Electronic Timing Gate</td><td>Brower Timing System</td><td>-</td><td>Draper, UT, USA, Used to record running speed</td></tr><tr><td>Standard Experimental Shoes</td><td>Designated Brand</td><td>-</td><td>Standardized shoes to minimize gait interference</td></tr><tr><td>Vicon Motion Capture System</td><td>Vicon Metrics Ltd.</td><td>Vicon T-Series</td><td>Oxford, UK, Used for motion capture</td></tr><tr><td>Vicon Nexus Software</td><td>Vicon Metrics Ltd.</td><td>Version 1.8.5A</td><td>Data collection and analysis</td></tr></tbody></table>

influence of step-width manipulation on running biomechanics

Step width is a critical factor influencing lower limb biomechanics during running, significantly affecting stability, performance, and injury risk. Understanding these effects is essential for optimizing running performance and minimizing injury risk. This study evaluated the effects of varying step widths on lower limb biomechanics at different running speeds. Thirteen healthy Chinese males (aged 20-24) participated in the study, running at speeds of 3.0 m/s and 3.7 m/s using six distinct step widths: the preferred step width and five variations (reductions of 13% and 6.5%, and increases of 6.5%, 13%, and 25%, based on leg length). Data were collected using a motion capture system and force plates and analyzed through repeated measures ANOVA and correlation tests. The results indicated that wider step widths significantly reduced peak knee abduction moments and hip adduction angles, whereas narrower step widths increased knee joint loading. These findings have important implications for clinicians and runners, suggesting that careful step width selection can help reduce injury risk and enhance running efficiency. This study contributes a new dataset that lays the foundation for future research into the relationship between step width and running biomechanics and serves as a reference for training and rehabilitation practices.

After the experiment and data processing, the processed marker trajectory and simulated ground reaction force (GRF) data were saved in C3D files, as outlined in Table 1. The C3D.zip folder contains the raw dataset obtained from the motion capture experiment. All data records are listed in Table 2. The dataset is organized into folders, each representing one of the 13 participants. Within these subfolders, the experimental files are named NNN_CV_TT.XXX, where NNN denotes the participant number, CV indicates the experiment type (static or dynamic), TT represents the running trials, and XXX refers to the file format extension. Static files are named NNN_Cal_TT.XXX to distinguish them from trial files.
The processed (ready-for-use) kinematic results were compared with recent running studies and confirmed to exhibit shape trends and magnitudes similar to those previously reported9,10,11,12. Specifically, Figure 2 illustrates the hip adduction and abduction angles during running at different step widths and speeds, showing patterns (trends and magnitudes) consistent with findings in existing running studies12,13.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/67152/67152fig01.jpg" /> 
Figure 1: Laboratory setup and step width markers on the force platform. This diagram illustrates the overall laboratory layout and highlights the placement of color-coded strips on the force platform, representing different step-width conditions. These color-coded markers distinguish the six-step width conditions in the experiment, including the participants&#39; preferred step width and variations based on leg length adjustments. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/67152/67152fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/67152/67152fig02.jpg" /> 
Figure 2: Hip adduction-abduction angles during running with different step widths. This figure shows the dynamic movement of the hip joint in abduction-adduction angles under six different step width variations, including the preferred step width and its incremental and decremental changes. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/67152/67152fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Table 1: Description of markers and metadata information in the C3D files. This table lists the anatomical locations where reflective markers were applied to the participants&#39; bodies and provides the corresponding names for each marker. The markers were strategically placed to ensure accurate tracking of joint movements during the experiment, aiding in the analysis of biomechanical parameters. Each marker&#39;s position is defined according to standard anatomical landmarks for precise data collection. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/67152/67152_Table 1.xlsx" target="_blank">Please click here to download this Table.</a>
Table 2: Data records. This table lists experimental data under various step width conditions, including specific lengths, names, and corresponding running speeds. The step width variations range from the preferred width to different percentages of leg length reductions and increases, applicable at running speeds of 3.0 m/s and 3.7 m/s. This data aims to analyze how these variations influence lower limb biomechanics. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/67152/67152_Table 2.xlsx" target="_blank">Please click here to download this Table.</a>

The present protocol outlines an experimental setup designed to investigate the influence of step width manipulation on running biomechanics using a motion capture system. The objective is to expand relevant datasets and examine the effects of varying step widths on the kinematic chain of the human lower limb.

Influence of Step-Width Manipulation on Running Biomechanics

Step width is a critical factor influencing lower limb biomechanics during running, significantly affecting stability, performance, and injury risk. ...

influence-of-step-width-manipulation-on-running-biomechanics

Nanyang Technological University

Research

JoVE Journal

Bioengineering

323 Views.  Ningbo University. The present protocol outlines an experimental setup designed to investigate the influence of step width manipulation on running biomechanics using a motion capture system. The objective is to expand relevant datasets and examine the effects of varying step widths on the kinematic chain of the human lower limb.

Video: Influence of Step-Width Manipulation on Running Biomechanics

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

For slender and lightweight structures, vibration serviceability is a matter of growing concern, often constituting the critical design requirement. With designs governed by the dynamic performance under human-induced loads, a strong demand exists for the verification and refinement of currently available load models. The present contribution uses a 3D inertial motion tracking technique for the characterization of the in-field pedestrian behavior. The technique is first tested in laboratory experiments with simultaneous registration of the corresponding ground reaction forces. The experiments include walking persons as well as rhythmical human activities such as jumping and bobbing. It is shown that the registered motion allows for the identification of the time variant pacing rate of the activity. Together with the weight of the person and the application of generalized force models available in literature, the identified time-variant pacing rate allows to characterize the human-induced loads. In addition, time synchronization among the wireless motion trackers allows identifying the synchronization rate among the participants. Subsequently, the technique is used on a real footbridge where both the motion of the persons and the induced structural vibrations are registered. It is shown how the characterized in-field pedestrian behavior can be applied to simulate the induced structural response. It is demonstrated that the in situ identified pacing rate and synchronization rate constitute an essential input for the simulation and verification of the human-induced loads. The main potential applications of the proposed methodology are the estimation of human-structure interaction phenomena and the development of suitable models for the correlation among pedestrians in real traffic conditions.

A protocol is presented for the characterization of the in-field pedestrian behavior and the simulation of the resulting structural response. Field-tests demonstrate that the in situ identified pacing rate and synchronization rate among the participants constitute an essential input for the simulation and verification of the human-induced loads.

For slender and lightweight structures, vibration serviceability is a matter of growing concern, often constituting the critical design requirement. With ...

Engineering

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb

Individuals with sensorimotor pathology e.g., stroke have difficulty executing the common task of rising from sitting and initiating gait (sit-to-walk: STW). Thus, in clinical rehabilitation separation of sit-to-stand and gait initiation - termed sit-to-stand-and-walk (STSW) - is usual. However, a standardized STSW protocol with a clearly defined analytical approach suitable for pathological assessment has yet to be defined.
Hence, a goal-orientated protocol is defined that is suitable for healthy and compromised individuals by requiring the rising phase to be initiated from 120% knee height with a wide base of support independent of lead limb. Optical capture of three-dimensional (3D) segmental movement trajectories, and force platforms to yield two-dimensional (2D) center-of-pressure (COP) trajectories permit tracking of the horizontal distance between COP and whole-body-center-of-mass (BCOM), the decrease of which increases positional stability but is proposed to represent poor dynamic postural control.
BCOM-COP distance is expressed with and without normalization to subjects&#39; leg length. Whilst COP-BCOM distances vary through STSW, normalized data at the key movement events of seat-off and initial toe-off (TO1) during steps 1 and 2 have low intra and inter subject variability in 5 repeated trials performed by 10 young healthy individuals. Thus, comparing COP-BCOM distance at key events during performance of an STSW paradigm between patients with upper motor neuron injury, or other compromised patient groups, and normative data in young healthy individuals is a novel methodology for evaluation of dynamic postural stability.

Here, we present a novel protocol to measure positional stability at key events during the sit-to-stand-to-walk using the center-of-pressure to the whole-body-center-of-mass distance. This was derived from the force platform and three-dimensional motion-capture technology. The paradigm is reliable and can be utilized for the assessment of neurologically compromised individuals.

Individuals with sensorimotor pathology e.g., stroke have difficulty executing the common task of rising from sitting and initiating gait (sit-to-walk: ...

Behavior

Using a Split-belt Treadmill to Evaluate Generalization of Human Locomotor Adaptation

Understanding the mechanisms underlying locomotor learning helps researchers and clinicians optimize gait retraining as part of motor rehabilitation. However, studying human locomotor learning can be challenging. During infancy and childhood, the neuromuscular system is quite immature, and it is unlikely that locomotor learning during early stages of development is governed by the same mechanisms as in adulthood. By the time humans reach maturity, they are so proficient at walking that it is difficult to come up with a sufficiently novel task to study de novo locomotor learning. The split-belt treadmill, which has two belts that can drive each leg at a different speed, enables the study of both short- (i.e., immediate) and long-term (i.e., over minutes-days; a form of motor learning) gait modifications in response to a novel change in the walking environment. Individuals can easily be screened for previous exposure to the split-belt treadmill, thus ensuring that all experimental participants have no (or equivalent) prior experience. This paper describes a typical split-belt treadmill adaptation protocol that incorporates testing methods to quantify locomotor learning and generalization of this learning to other walking contexts. A discussion of important considerations for designing split-belt treadmill experiments follows, including factors like treadmill belt speeds, rest breaks, and distractors. Additionally, potential but understudied confounding variables (e.g., arm movements, prior experience) are considered in the discussion.

We describe a protocol for investigating human locomotor adaptation using the split-belt treadmill, which has two belts that can drive each leg at a different speed. We specifically focus on a paradigm designed to test the generalization of adapted locomotor patterns to different walking contexts (e.g., gait speeds, walking environments).

Understanding the mechanisms underlying locomotor learning helps researchers and clinicians optimize gait retraining as part of motor rehabilitation. ...

Using Gold-standard Gait Analysis Methods to Assess Experience Effects on Lower-limb Mechanics During Moderate High-heeled Jogging and Running

A limited number of studies have explored lower-limb biomechanics during high-heeled jogging and running, and most studies have failed to clarify the wearing experience of subjects. This protocol describes the differences in lower-limb kinematics and ground reaction force (GRF) between experienced wearers (EW) and inexperienced wearers (IEW) during moderate high-heeled jogging and running. A three-dimensional (3D) motion analysis system with a configured force platform was used to synchronously capture lower-limb joint movements and GRF. 36 young females volunteered to participate in this study and were asked about high-heeled shoe-wearing experience, including frequency, duration, heel types, and heel heights. Eleven who had the experience of 3 to 6 cm heels for a minimum of three days per week (6 h per day) for at least two years and eleven who wore high heels less than twice per month participated. Subjects performed jogging and running at comfortable low and high speeds, respectively, with the right foot completely stepping onto a force platform when passing by along a 10 m walkway. EW and IEW adopted different biomechanical adaptations while jogging and running. IEW exhibited a generally larger range of joint movement, while EW showed a dramatically larger loading rate of GRF during running. Hence, further studies on the lower-limb biomechanics of high-heeled gait should strictly control the wearing experience of the subjects.

This study investigated lower-limb kinematics and ground reaction force (GRF) during moderate high-heeled jogging and running. Subjects were divided into groups of experienced wearers and inexperienced wearers. A three-dimensional motion analysis system with a configured force platform captured lower-limb joint movements and GRF.

A limited number of studies have explored lower-limb biomechanics during high-heeled jogging and running, and most studies have failed to clarify the ...

Oscillation and Reaction Board Techniques for Estimating Inertial Properties of a Below-knee Prosthesis

The purpose of this study was two-fold: 1) demonstrate a technique that can be used to directly estimate the inertial properties of a below-knee prosthesis, and 2) contrast the effects of the proposed technique and that of using intact limb inertial properties on joint kinetic estimates during walking in unilateral, transtibial amputees. An oscillation and reaction board system was validated and shown to be reliable when measuring inertial properties of known geometrical solids. When direct measurements of inertial properties of the prosthesis were used in inverse dynamics modeling of the lower extremity compared with inertial estimates based on an intact shank and foot, joint kinetics at the hip and knee were significantly lower during the swing phase of walking. Differences in joint kinetics during stance, however, were smaller than those observed during swing. Therefore, researchers focusing on the swing phase of walking should consider the impact of prosthesis inertia property estimates on study outcomes. For stance, either one of the two inertial models investigated in our study would likely lead to similar outcomes with an inverse dynamics assessment.

Body segmental inertial properties are required for inverse dynamics modeling. Using an oscillation and reaction board technique, inertial properties of below-knee prostheses were measured. Using direct measures of prosthesis inertia in the inverse dynamics model of the prosthetic leg resulted in lower magnitudes of resultant joint forces and moments.

The purpose of this study was two-fold: 1) demonstrate a technique that can be used to directly estimate the inertial properties of a below-knee ...

Comparative Analysis of Lower Limb Kinematics between the Initial and Terminal  Phase  of 5km Treadmill Running

Running is beneficial for physical health, but it is also accompanied by many injuries. However, the main factors leading to running injury remain unexplained. This study investigated the effects of long running distance on lower-limb kinematic variables and the lower limb kinematic difference of between the initial (IR) and terminal phase (TR) of 5 km running was compared. Ten amateur runners ran on a treadmill at the speed of 10 km/h. Dynamic kinematic data was collected at the phase of IR (0.5 km) and TR (5 km), respectively. The peak angle, peak angular velocities, and range of motion were recorded in this experiment. The main results demonstrated the following: ankle eversion and knee abduction were increased at TR; ROMs of ankle and knee were increased in the frontal plane at TR than IR; a larger peak angular velocity of ankle dorsiflexion and hip interrotation were found in TR compared to IR. These changes during the long distance running may provide some specific details for exploring potential reasons of running injuries.

This study investigated the biomechanical characteristics of the lower extremity kinematic variables between the initial and terminal phase of 5 km treadmill running. The lower-limb kinematic data of 10 runners were collected using a three-dimensional motion capture system on a treadmill at the initial phase (0.5 km) and the terminal phase (5 km), respectively.

Running is beneficial for physical health, but it is also accompanied by many injuries. However, the main factors leading to running injury remain ...

Lower-Limb Biomechanical Characteristics Associated with Unplanned Gait Termination Under Different Walking Speeds

Gait termination caused by unexpected stimulus is a common occurrence in everyday life. This study presents a protocol to investigate the lower-limb biomechanical changes that occur during unplanned gait termination (UGT) under different walking speeds. Fifteen male participants were asked to perform UGT on a walkway at normal walking speed (NWS) and fast walking speed (FWS), respectively. A motion analysis system and plantar pressure platform were applied to collect lower-limb kinematic and plantar pressure data. Paired-sampled T-test was used to examine the differences in lower-limb kinematics and plantar pressure data between two walking speeds. The results showed larger range of motion in the hip, knee, and ankle joints in the sagittal plane as well as plantar pressure in forefoot and heel regions during UGT at FWS when compared with NWS. With the increase in walking speed, subjects exhibited different lower-limb biomechanical characteristics that show FWS associated with greater potential injury risks.

This study compared the biomechanical characteristics of the lower extremity during unplanned gait termination under different walking speeds. The lower-limb kinematic and kinetic data from fifteen subjects with normal and fast walking speeds were collected using a motion analysis system and plantar pressure platform.

Gait termination caused by unexpected stimulus is a common occurrence in everyday life. This study presents a protocol to investigate the lower-limb ...

Comparison of Kinetic Characteristics of Footwork during Stroke in Table Tennis: Cross-Step and Chasse Step

The cross-step and chasse step are the basic steps of table tennis. This study presents a protocol to investigate the ground reaction force characteristics between cross-step and chasse step during stroke in table tennis. Sixteen healthy male national level 1 table tennis players (Age: 20.75 &plusmn; 2.06 years) volunteered to participate in the experiment after understanding the purpose and details of the experiment. All participants were asked to hit the ball into the target zone by cross-step and chasse step, respectively. The ground reaction force in the anterior-posterior, medial-lateral, and vertical directions of the participant was measured by a force platform. The key finding of this study was that: the posterior ground reaction force of cross-step footwork (0.89 &plusmn; 0.21) was significantly large (P = 0.014) than the chasse step footwork (0.82 &plusmn; 0.18). However, the lateral ground reaction force of cross-step footwork (-0.38 &plusmn; 0.21) was significantly lower (P &lt; 0.001) than chasse step footwork (-0.46 &plusmn; 0.29) as well as the vertical ground reaction force of cross-step footwork (1.73 &plusmn; 0.19) was significantly lower (P &lt; 0.001) than chasse step footwork (1.9 &plusmn; 0.33). Based on the mechanism of the kinetic chain, the better lower limb dynamic performance of sliding stroke may be conducive to energy transmission and thus bring gain to the swing speed. Beginners should start from the chasse step to hit the ball technically, and then practice the skill of cross-step.

This study presents a protocol to investigate the ground reaction force characteristics between cross-step and chasse step during stroke in table tennis.

The cross-step and chasse step are the basic steps of table tennis. This study presents a protocol to investigate the ground reaction force ...

Sagittal Plane Kinematic Gait Analysis in C57BL/6 Mice Subjected to MOG35-55 Induced Experimental Autoimmune Encephalomyelitis

Kinematic gait analysis in the sagittal plane has frequently been used to characterize motor deficits in multiple sclerosis (MS). We describe the application of these techniques to identify gait deficits in a mouse model of MS, known as experimental autoimmune encephalomyelitis (EAE). Paralysis and motor deficits in mice subjected to EAE are typically assessed using a clinical scoring scale. However, this scale yields only ordinal data that provides little information about the precise nature of the motor deficits. EAE disease severity has also been assessed by rotarod performance, which provides a measure of general motor coordination. By contrast, kinematic gait analysis of the hind limb in the sagittal plane generates highly precise information about how movement is impaired. To perform this procedure, reflective markers are placed on a hind limb to detect joint movement while a mouse is walking on a treadmill. Motion analysis software is used to measure movement of the markers during walking. Kinematic gait parameters are then derived from the resultant data. We show how these gait parameters can be used to quantify impaired movements of the hip, knee, and ankle joints in EAE. These techniques may be used to better understand disease mechanisms and identify potential treatments for MS and other neurodegenerative disorders that impair mobility.

Kinematic gait analysis in the sagittal plane yields highly precise information about how movement is executed. We describe the application of these techniques to identify gait deficits for mice subjected to autoimmune-mediated demyelination. These methods may also be used to characterize gait deficits for other mouse models featuring impaired locomotion.

Kinematic gait analysis in the sagittal plane has frequently been used to characterize motor deficits in multiple sclerosis (MS). We describe the ...

Neuroscience

Lower Limb Biomechanical Analysis of Healthy Participants

Biomechanical analysis techniques are useful in the study of human movement. The aim of this study was to introduce a technique for the lower limb biomechanical assessment in healthy participants using commercially available systems. Separate protocols were introduced for the gait analysis and muscle strength testing systems. To ensure maximum accuracy for gait assessment, attention should be given to the marker placements and self-paced treadmill acclimatization time. Similarly, participant positioning, a practice trial, and verbal encouragement are three critical stages in muscle strength testing. The current evidence suggests that the methodology outlined in this article may be effective for the assessment of lower limb biomechanics.

This article introduces a comprehensive experimental methodology on two of the latest technologies available to measure the lower limb biomechanics of individuals.