The experiments involving walking individuals are performed in cooperation with Movement &#38; posture Analysis Laboratory Leuven (MALL)25. Their cooperation and support is gratefully acknowledged.

All procedures were approved by the ethical committee of the university hospital of the KU Leuven and each subject gave a written informed consent prior to participation.
1. 3D Motion Tracking: Configuration and Data Acquisition
<ol>
	<li>Ensure that the individual sensors are fully charged (Figure 1A). This step takes about 1 hr but can be performed on the days prior to the actual measurements. Follow the manufacturer&#39;s charging protocol.</li>
	<li>MT Manager &#8212; Data acquisition2:
	<ol>
		<li>Enable the wireless connection with the sensors and specify the desired sample rate (Wireless Configuration &#62; Enable all wireless masters). 
		Note: To allow for an accurate characterization of the pedestrian behavior, a sampling rate of at least 60 Hz is advised. The individual sensors record 3D linear acceleration, angular velocity (earth) magnetic field and atmospheric pressure data.</li>
		<li>Activate the operational mode and initiate the measurement mode: make slow movements with the sensors for about 1 min (Wireless Configuration &#62; Start measurement on all wireless masters).</li>
		<li>Display inertial and magnetic data of all active sensors (View &#62; Display &#62; Inertial Data). Make sure that, while stationary, the orientation of the sensor does not chance. 
		Note: A changing orientation of the stationary sensor would indicate a magnetically disturbed environment and, thereby, inaccurate orientation information.</li>
	</ol>
	</li>
	<li>Orientation reset: Apply an object/heading reset (Object/heading reset &#62; Reset orientation) to define the global reference frame of the experiments (Figure 1B) 2.</li>
	<li>Place the sensor as close as possible to the body center of mass (CoM) located at the level of the fifth lumbar vertebrae (Figure 1C). Fasten a single sensor tightly and robustly onto each participant with specially designed click-in full body straps (Figure 1C).</li>
	<li>Record data as required.</li>
	<li>Load the records of interest (open file), specify the export settings (Tools &#62; Preferences &#62; Exporters) and export the acceleration (and orientation matrix) data for subsequent analysis2 (File &#62; Export).</li>
</ol>
2. Force Plate: Setup and Configuration
Note: The present step discusses the application of a force plate to register the GRFs. In the case that a walking/running person is involved, a series of force plates or an instrumented treadmill is to be used to register the loading induced by subsequent steps3, the protocol itself is analogous.
<ol>
	<li>Ensure that the force plate is securely fixed to the laboratory floor (Figure 2).</li>
	<li>Configure the device and acquisition settings4 (NDI Open Capture &#62; Data &#62; Device Settings &#62; Settings). Select the proper &#34;gain&#34; and &#34;sample rate&#34;. Configure and check the external trigger settings, if required4.
	<ol>
		<li>Choose the gain and sample rate in accordance with the desired accuracy and the involved loading type. For the present application, use a gain of 128 (Maximum Force 4,879 N) and a sample rate 200 Hz.</li>
	</ol>
	</li>
	<li>Start and end each trial with an empty force plate: Tare the force plate when empty (NDI Open Capture &#62; Data &#62; Device Settings &#62; Settings &#62; Tare).</li>
	<li>For verification purposes: Place a known weight on top of the force plate before and after each trial. 
	Note: In the present application a mass of 5 kg is used, however, the use of another well-known rigid mass (&#62; 2 kg) can equally serve this verification test.</li>
	<li>Record and save the GRF data as required. Export the GRFs for subsequent analysis4.</li>
</ol>
3. Measurement of the Structural Accelerations
Note: The present steps aim to collect the structural vibrations at one or more relevant locations on the structure. The present application employs GeoSIG GMS recorders (Figure 3) to register the structural accelerations. Other sensor types with proper characteristics for the involved application, can be equally applied.
<ol>
	<li>Ensure that the individual sensors are fully charged. This step can take several hours but can be performed on the days prior to the actual measurements. Follow the manufacturer&#39;s charging protocol.</li>
	<li>Install the sensors on the desired locations of the primary structure: level the sensors and, if necessary, provide proper fixation to the primary structure (e.g., by using magnets). 
	Note: given the high mass of the individual GMS recorders (&#62; 6 kg) and the involved low-frequency oscillations (&#60; 6 Hz), no additional fixation was necessary in this case.</li>
	<li>For GeoDAS Data acquisition5: Configure and enable the wireless GMS network and connection with the sensors5. Check time settings and synchronization settings (if necessary) (right click on the sensor &#62; More information).</li>
	<li>Position the sensors on the desired location and level them in agreement with the global reference frame.</li>
	<li>For GeoDAS Data acquisition5: Export the recorded data for subsequent analysis (right click on the sensor &#62; Instrument Control &#62; Send a request &#62; User request &#62; GETEVT5).</li>
</ol>
4. Experiments in a Controlled Laboratory Environment
<ol>
	<li>Configure / Setup 3D motion tracking (as discussed in section 1).</li>
	<li>Configure / Setup force plate (as discussed in section 2).</li>
	<li>During operation: visually check the real-time measurements of both the wireless inertial sensors and the force plate to verify their operational mode.</li>
	<li>Ask the participant to step onto the force plate and stand still for at least 30 sec: this allows to identify the weight of each individual.</li>
	<li>Configure the metronome signal: select the desired rhythm, i.e., fundamental forcing frequency. 
	Note: The metronome signal can be easily configured using free online or smartphone applications.</li>
	<li>Start recording the data of both the force plate and the wireless inertial sensors.</li>
	<li>Ask the participant to initiate the desired activity: walking, jumping or bobbing at the (targeted pacing) rate as indicated by the metronome signal (see Figure 4).</li>
	<li>Record the chosen number of loading cycles, e.g., steps, jumps or bobbing cycles. Ask the participant to get off the force plate. 
	Note: For validation purposes it is advised to consider some additional recording time in these unloaded conditions. In literature, there is no clear consensus about the minimum number loading cycles required to characterize the cycle-to-cycle variabilities6. Based on experience and the work presented in [6], the study presented here considers 60 consecutive cycles whereby the first and last five loading cycles are excluded from the further analysis to exclude irregularities in the loading pattern at the start and end of the trial.</li>
</ol>
5. Experiments In Situ
<ol>
	<li>Configure / Setup the network of 3D inertial sensors that track the motion of the participants (see section 2 and Figure 5).</li>
	<li>Configure / Setup the GMS network of wireless accelerometers that register the structural accelerations (see section 4).</li>
	<li>During operation: (visually) check the real-time measurements of the wireless inertial sensors to verify their operational mode.</li>
	<li>Define a clear protocol that allows to synchronize the involved measurement systems, if required. 
	Note: This step is necessary when the involved data acquisition systems do not allow for direct synchronization due to the lack of a trigger or common channel. The latter is the case for the wireless measurement systems applied in the in situ experiments (5.1 and 5.2). Therefore, a clear protocol has been adopted on site that allows to synchronize the datasets offline. In the present application, the involved measurement systems are synchronized through registration of an identical event, i.e., impact, at the beginning and end of each trial, registered by at least one sensor of each of the involved measurement systems. Properly aligned time vectors are subsequently obtained through offline alignment of these events.</li>
	<li>Configure the metronome signal: in situ, the use of a megaphone to amplify the targeted beat is required.</li>
	<li>Collect a sufficient number of trials to check the repeatability of the experiment. Based on experience, the authors recommend to record at least 3, or preferably 4, trials.</li>
</ol>
6. Data Analysis
<ol>
	<li>Pre-process the raw data of the involved equipment as required: Apply the proper filters to remove undesired influences such as irrelevant high-frequency contributions and measurement noise, and retain the relevant time window according to manufacturer&#39;s protocol. 
	Note: The filtering characteristics should be chosen in accordance with the application. In the present study, the MATLAB Signal Processing Toolbox7 is applied to perform a low-pass filtering with a cut-off frequency at 20 Hz for all involved signals.</li>
	<li>For each participant: Compute the discrete Fourier transform of the registered accelerations of the CoM using MATLAB Signal Processing Toolbox7 and identify the average loading frequency as the frequency of the dominant peak of the fundamental harmonic in the obtained spectrum.</li>
	<li>Identify the time in between any two nominally identical events of the load cycles using the method detailed in [3] or the lc_timing tool of the PediVib MATLAB toolbox8
	<ol>
		<li>Load the data vector (lc_timing &#62; Load).</li>
		<li>Specify the sampling rate and estimate the average loading frequency. Specify the relevant time window, if required. Save the identified timing of the nominally identical events, i.e., load cycles (lc_timing &#62; Save).</li>
	</ol>
	</li>
	<li>Compute the average loading frequency as the inverse of the average time in between the subsequent load cycles (as identified in 6.3).</li>
	<li>For the experiments in the laboratory: Apply the procedure detailed in 6.3 for both the resulting ground reaction forces and the accelerations registered at the CoM of each individual. 
	Note: This step serves as validation for the procedure as applied for the in situ experiments where the GRFs cannot be measured directly. The method detailed in [3] shows how the time variant pacing rate of the pedestrian can be identified by characterizing the relation between the accelerations registered near the CoM of the individual and the consequent GRFs.</li>
	<li>For the experiments in situ: Apply the procedure detailed in 6.3 for the accelerations registered at the CoM of each individual.</li>
</ol>
7. Simulation and Analysis of the Structural Response
Note: The subsequent steps are performed using MATLAB7. The structural response is computed using the PediVib toolbox, a MATLAB toolbox developed by the authors8 (Figure 6): the human-induced forces are determined through application of the generalized load models of defined by Li et al.9 (walking) and Bachmann et al.1 (jumping, running and vandal loading), and the structural model is formulated in modal coordinates10. The accompanying manual includes tutorials that clearly illustrate the following steps.
<ol>
	<li>Simulation of the structural response
	<ol>
		<li>Define the modal parameters of the test structure: Natural frequencies, modal damping ratios, mass-normalized modal displacements, coordinates of the corresponding nodes (PediVib &#62; Structural parameters &#62; New). Visually check the modal input information (PediVib &#62; Structural parameters &#62; View).</li>
		<li>Define the characteristics of the pedestrian and the corresponding induced loads: load type, weight, walking path/location, average pacing rate, onset of each load cycle (PediVib &#62; Single pedestrian &#62; New). Run and save the simulated structural response for the involved participants. Visually check the results (PediVib &#62; Single pedestrian &#62; View).</li>
	</ol>
	</li>
	<li>Compute the total structural response through superposition of the individual responses, i.e., summation of the corresponding vectors, and compare the result with the measured structural response, e.g., by creating a figure which displays the measured and simulated structural response.</li>
</ol>

<ol>
	<li>Bachmann, H., Ammann, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Bachmann vibrations in structures : induced by man and machines. , (1987).</li><li>. MTw User Manual Available from: <a target="_blank" href="https://www.xsens.com/download/usermanual/MTw_usermanual.pdf">https://www.xsens.com/download/usermanual/MTw_usermanual.pdf</a> (2013)</li><li>Van Nimmen, K., Lombaert, G., Jonkers, I., De Roeck, G., Vanden Broeck, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Characterisation+of+walking+loads+by+3D+inertial+motion+tracking.">Characterisation of walking loads by 3D inertial motion tracking.</a> J. Sound Vib. 333 (20), 1-15 (2013).</li><li>Northern Digital Inc. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> TrueImpulse Kinetic Measurement System User Guide. , (2013).</li><li>Racic, V., Pavic, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mathematical+model+to+generate+near-periodic+human+jumping+force+signals.">Mathematical model to generate near-periodic human jumping force signals.</a> Mech. Syst. Signal Process. 24 (1), 138-152 (2010).</li><li>The MathWorks Inc. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> MATLAB and Signal Processing Toolbox Release. , (2014).</li><li>Van Nimmen, K., Van den Broeck, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> PediVib 1.0 - A MATLAB toolbox for the simulation of human-induced vibrations. , (2015).</li><li>Li, Q., Fan, J., Nie, J., Li, Q., Chen, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Crowd-induced+random+vibration+of+footbridge+and+vibration+control+using+multiple+tuned+mass+dampers.">Crowd-induced random vibration of footbridge and vibration control using multiple tuned mass dampers.</a> J. Sound Vib. 329 (19), 4068-4092 (2010).</li><li>Van Nimmen, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Numerical and experimental study of human-induced vibrations of footbridges [dissertation]. , (2015).</li><li>Middleton, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Dynamic performance of high frequency floors [dissertation]. , (2009).</li><li>Ing&#242;lfsson, E. T., Georgakis, C. T., Ricciardelli, F., J&#246;nsson, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Experimental+identification+of+pedestrian-induced+lateral+forces+on+footbridges.">Experimental identification of pedestrian-induced lateral forces on footbridges.</a> J. Sound Vib. 330 (6), 1265-1284 (2011).</li><li>Racic, V., Brownjohn, J. M. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mathematical+modelling+of+random+narrow+band+lateral+excitation+of+footbridges+due+to+pedestrians+walking.">Mathematical modelling of random narrow band lateral excitation of footbridges due to pedestrians walking.</a> Comput. Struct. 90-91 (1), 116-130 (2012).</li><li>Reynders, E., Roeck, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=De+Reference-based+combined+deterministic-stochastic+subspace+identification+for+experimental+and+operational+modal+analysis.">De Reference-based combined deterministic-stochastic subspace identification for experimental and operational modal analysis.</a> Mech. Syst. Signal Process. 22 (3), 617-637 (2008).</li><li>Bocian, M., Macdonald, J. H. G., Burn, J. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Biomechanically+inspired+modeling+of+pedestrian-induced+vertical+self-excited+forces.">Biomechanically inspired modeling of pedestrian-induced vertical self-excited forces.</a> J. Bridg. Eng. 18 (12), 1336-1346 (2013).</li><li>&#381;ivanovi&#263;, S., Pavi&#263;, A., Ing&#242;lfsson, E. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Modeling+spatially+unrestricted+pedestrian+traffic+on+footbridges.">Modeling spatially unrestricted pedestrian traffic on footbridges.</a> Journal of Structural Engineering. 136 (10), 1296-1308 (2010).</li><li>Agu, E., Kasperski, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Influence+of+the+random+dynamic+parameters+of+the+human+body+on+the+dynamic+characteristics+of+the+coupled+system+of+structurecrowd.">Influence of the random dynamic parameters of the human body on the dynamic characteristics of the coupled system of structurecrowd.</a> J. Sound Vib. 330 (3), 431-444 (2011).</li><li>. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Vicon Motion Systems Product Manuals. , (2012).</li><li>. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> CODAmotion Technical data sheet. , (2012).</li><li>Meichtry, A., Romkes, J., Gobelet, C., Brunner, R., M&#252;ller, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Criterion+validity+of+3D+trunk+accelerations+to+assess+external+work+and+power+in+able-bodied+gait.">Criterion validity of 3D trunk accelerations to assess external work and power in able-bodied gait.</a> Gait Posture. 25 (1), 25-32 (2007).</li><li>Jung, Y., Jung, M., Lee, K., Koo, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ground+reaction+force+estimation+using+an+insole-type+pressure+mat+and+joint+kinematics+during+walking.">Ground reaction force estimation using an insole-type pressure mat and joint kinematics during walking.</a> J. Biomech. 47 (11), 2693-2699 (2014).</li><li>Liedtke, C., Fokkenrood, S. A., Menger, J. T., van der Kooij, H., Veltink, P. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluation+of+instrumented+shoes+for+ambulatory+assessment+of+ground+reaction+forces.">Evaluation of instrumented shoes for ambulatory assessment of ground reaction forces.</a> Gait Posture. 26 (1), 39-47 (2007).</li><li>Boutaayamou, M., Schwartz, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Validated+extraction+of+gait+events+from+3D+accelerometer+recordings.">Validated extraction of gait events from 3D accelerometer recordings.</a> , 6-9 (2012).</li><li>Kavanagh, J. J., Menz, H. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Accelerometry:+A+technique+for+quantifying+movement+patterns+during+walking.">Accelerometry: A technique for quantifying movement patterns during walking.</a> Gait Posture. 28 (1), 1-15 (2008).</li><li>. MALL: Movement and posture Analysis Laboratory Leuven (Interdepartemental research laboratory at the Faculty of Kinisiology and Rehabilitation Sciences) Available from: <a target="_blank" href="https://faber.kuleuven.be/MALL/mall.php">https://faber.kuleuven.be/MALL/mall.php</a> (2015)</li></ol>

The authors have nothing to disclose.

The human motion and resulting GRFs are usually identified by the application of force plates, instrumented treadmills as well as optical motion capture technology such as Vicon18 and CODA19. The application of these techniques is, however, restricted to the laboratory environment. In answer to this drawback, the potential of innovative techniques that permit the measurement of &#39;natural&#39; person behavior over many repeated and uninterrupted cycles is currently investigated20. Alter...

Driven by the economic demand of efficiency and the increasing strength of (new) materials, architects and engineers are pushing the limits to build ever longer, taller and lighter structures. Typically, light and slender structures have one or more natural frequencies that lie within the dominant spectrum of common human activities such as walking, running or jumping. Likely to be subject to (near-)resonant excitation, they are often unduly responsive to human motion, resulting in disturbing or even harmful vibrations1. For these slender and lightweight structures, the vibration serviceability is a matter of growing concern, often constituting the critical design requirement.
The human motion and the resulting ground reaction forces (GRFs) are usually experimentally identified in laboratory conditions. Currently, designers are forced to rely on &#8212; what are assumed to be &#39;conservative&#39;&#160;&#8212; equivalent load models, upscaled from single-person force measurements. With designs governed by the dynamic performance under high crowd densities, a strong demand exists for the verification and refinement of the currently available load models.
The present protocol employs a 3D inertial motion tracking technique for the characterization of the natural motion of pedestrians. It is shown how this information can be used to define the correlation among the pedestrians as well as the corresponding induced loads. In a subsequent step, the characterized pedestrian behavior is used to numerically simulate the induced structural response. Comparison with the registered structural response allows to quantify the effect of unaccounted human-structure interaction phenomena, e.g., the added damping due to the presence of the pedestrians. The methodology is illustrated for full-scale experiments on a real footbridge where the structural response and the motion of the participants are registered simultaneously.

<table><tbody><tr><td>MTw Development Kit + MT Manager Software</td><td>Xsens</td><td>MTW-38A70G20-1</td><td>Development kit with wireless, highly accurate, small and lightweight 3D human motion trackers and accompanying click-in full body straps.</td></tr><tr><td>True Impulse Kinetic Measurement System + NDI Open Capture Data Acquisition and Visualization System</td><td>NDI Northern Digital Inc.</td><td>791028</td><td>TrueImpulse measures reaction forces exerted by humans during a wide variety of activities.</td></tr><tr><td>GMS-24</td><td>GeoSIG Ltd</td><td>Rev. 03.08.2010</td><td>(Wireless) accelerometers to register the structural vibrations.</td></tr><tr><td>GeoDAS GeoSIG Data Acquisition System</td><td>GeoSIG Ltd</td><td>Rev. 03.08.2010</td><td>Graphical MS Windows application running under Windows 9x/NT/2000, providing a software interface between users and GeoSIG recorders GSR/GCR/GBV/GT.</td></tr><tr><td>PediVib toolbox</td><td>KU Leuven</td><td></td><td>Software interface/toolbox to simulate the structural vibrations induced by pedestrians.</td></tr><tr><td>Metronome</td><td></td><td></td><td>A device to indicate the targetted pacing rate of the activity (free applications are available online for pc/laptop/smartphone).</td></tr></tbody></table>

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.

First, it is shown how the accelerations registered near the CoM of the individuals can be used to characterize the consequent GRFs. The results are discussed here for a walking individual3. Fully comparable observations are made when rhythmical human activities, i.e., jumping and bobbing, are considered. Figure 7A and 7B show that the amplitude spectrum of the continuous vertical foot forces and the corresponding acceleration levels r...

Watch this Scientific Journal Video about Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior at JoVE.com

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

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 ...

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8.7K Views.  KU Leuven. 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.

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

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

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 ...

Bioengineering

Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior

To cross a road successfully, individuals must coordinate their movements with moving vehicles. This paper describes use of a walking simulator in which people walk on a treadmill to intercept gaps between two moving vehicles in an immersive virtual environment. Virtual reality allows for a safe and ecologically varied investigation of gap crossing behavior. Manipulating the initial starting distance can further the understanding of a participant&#8217;s speed regulation while approaching a gap. The speed profile may be assessed for various gap crossing variables, such as initial distance, vehicle size, and gap size. Each walking simulation results in a position/time series that can inform how velocity is adjusted differently depending on the gap characteristics. This methodology can be used by researchers investigating pedestrian behavior and behavioral dynamics while employing human participants in a safe and realistic setting.

This protocol describes use of a walking simulator that serves as a safe and ecologically valid method to study pedestrian behavior in the presence of moving traffic.

To cross a road successfully, individuals must coordinate their movements with moving vehicles. This paper describes use of a walking simulator in which ...

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Collision warning system plays a key role in the prevention of driving distractions and drowsy driving. Previous studies have proven the advantages of tactile warnings in reducing driver&#8217;s brake response time. At the same time, tactile warnings have been proved effective in take-over request (TOR) for partially autonomous vehicles.
How the performance of tactile warnings can be optimized is an ongoing hot research topic in this field. Thus, the presented low-cost driving simulation software and methods are introduced to attract more researchers to take part in the investigation. The presented protocol has been divided into five sections: 1) participants, 2) driving simulation software configuration, 3) driving simulator preparation, 4) vibrating toolkit configuration and preparation, and 5) conducting the experiment.
In the exemplar study, participants wore the tactile vibrating toolkit and performed an established car-following task using the customized driving simulation software. The front vehicle braked intermittently, and vibrating warnings were delivered whenever the front vehicle was braking. Participants were instructed to respond as quickly as possible to the sudden brakes of the front vehicle. Driving dynamics, such as the brake response time and brake response rate, were recorded by the simulation software for data analysis.
The presented protocol offers insight into the exploration of the effectiveness of tactile warnings on different body locations. In addition to the car-following task that is demonstrated in the exemplar experiment, this protocol also provides options&#160;to apply other paradigms to the driving simulation studies by making simple software configuration without any code development. However, it is important to note that due to its affordable price, the driving simulation software and hardware introduced here may not be able to fully compete with other high-fidelity commercial driving simulators. Nevertheless, this protocol can act as an affordable and user-friendly alternative to the general high-fidelity commercial driving simulators.

This protocol describes a driving simulation platform and a tactile vibrating toolkit for the investigation of driving-related research. An exemplar experiment exploring the effectiveness of tactile warnings is also presented.&#160;

Collision warning system plays a key role in the prevention of driving distractions and drowsy driving. Previous studies have proven the advantages of ...

A Method for Evaluating Timeliness and Accuracy of Volitional Motor Responses to Vibrotactile Stimuli

Artificial sensory feedback (ASF) systems can be used to compensate for lost proprioception in individuals with lower-limb impairments. Effective design of these ASF systems requires an in-depth understanding of how the parameters of specific feedback mechanism affect user perception and reaction to stimuli. This article presents a method for applying vibrotactile stimuli to human participants and measuring their response. Rotating mass vibratory motors are placed at pre-defined locations on the participant's thigh, and controlled through custom hardware and software. The speed and accuracy of participants' volitional responses to vibrotactile stimuli are measured for researcher-specified combinations of motor placement and vibration frequency. While the protocol described here uses push-buttons to collect a simple binary response to the vibrotactile stimuli, the technique can be extended to other response mechanisms using inertial measurement units or pressure sensors to measure joint angle and weight bearing ratios, respectively. Similarly, the application of vibrotactile stimuli can be explored for body segments other than the thigh.

This article describes a technique for applying vibrotactile stimuli to the thigh of a human participant, and measuring the accuracy and reaction time of the participant&#39;s volitional response for various combinations of stimulation location and frequency.

Artificial sensory feedback (ASF) systems can be used to compensate for lost proprioception in individuals with lower-limb impairments.  Effective design ...

Estimating Vestibular Perceptual Thresholds Using a Six-Degree-Of-Freedom Motion Platform

Vestibular perceptual thresholds refer to the motion intensity required to enable a participant to detect or discriminate a motion based on vestibular input. Using passive motion profiles provided by six degree-of-motion platforms, vestibular perceptual thresholds can be estimated for any kind of motion and thereby target each of the sub-components of the vestibular end-organ. Assessments of vestibular thresholds are clinically relevant as they complement diagnostic tools such as caloric irrigation, the head impulse test (HIT), or vestibular evoked myogenic potentials (VEMPs), which only provide information on sub-components of the vestibular system, but none of them allow for assessing all components. There are several methods with different advantages and disadvantages for estimating vestibular perceptual thresholds. In this article, we present a protocol using an adaptive staircase algorithm and sinusoidal motion profiles for an efficient estimation procedure. Adaptive staircase algorithms consider the response history to determine the peak velocity of the next stimuli and are the most commonly used algorithms in the vestibular domain. We further discuss the impact of motion frequency on vestibular perceptual thresholds.

In this article, we describe the methods, procedures, and technologies required to estimate vestibular perceptual thresholds using a six-degree-of-freedom motion platform.

Vestibular perceptual thresholds refer to the motion intensity required to enable a participant to detect or discriminate a motion based on vestibular ...

Directional Gradient Histogram and SVM for Identifying Concrete Vibration States

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

In this paper, the directional gradient histogram technology is employed to extract the features of concrete image samples captured under different vibration states. The support vector machine (SVM) is utilized to learn the relationship between image features and vibration state. The machine learning results are subsequently used to assess the feasibility of the concrete vibration state. Simultaneously, the influence mechanism of the calculation parameters of the directional gradient histogram on the recognition accuracy is analyzed. The results demonstrate the feasibility of using the directional gradient histogram-SVM technology to identify the vibration state of concrete. The recognition accuracy initially increases and then decreases as the block size of the directional gradient, or the number of statistical intervals increases. The recognition accuracy also decreases linearly with the increase of the binarization threshold. By using sample images with a resolution of 1024 pixels x 1024 pixels and optimizing the feature extraction parameters, a recognition accuracy of 100% can be attained.

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines 

The protocol described in this paper utilizes the directional gradient histogram technique to extract the characteristics of concrete image samples under various vibration states. It employs a support vector machine for machine learning, resulting in an image recognition method with minimal training sample requirements and low computer performance demands.

In this paper, the directional gradient histogram technology is employed to extract the features of concrete image samples captured under different ...

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

Bone stress injuries are common in sports and military trainings. Repetitive large ground impact forces during training could be the cause. It is essential to determine the effect of high ground impact forces on lower-body bone deformation to better understand the mechanisms of bone stress injuries. Conventional strain gauge measurement has been used to study in vivo tibia deformation. This method is associated with limitations including invasiveness of the procedure, involvement of few human subjects, and limited strain data from small bone surface areas. The current study intends to introduce a novel approach to study tibia bone strain under high impact loading conditions. A subject-specific musculoskeletal model was created to represent a healthy male (19 years, 80 kg, 1,800 mm). A flexible finite element tibia model was created based on a computed tomography (CT) scan of the subject&#39;s right tibia. Laboratory motion capture was performed to obtain kinematics and ground reaction forces of drop-landings from different heights (26, 39, 52 cm). Multibody dynamic computer simulations combined with a modal analysis of the flexible tibia were performed to quantify tibia strain during drop-landings. Calculated tibia strain data were in good agreement with previous in vivo studies. It is evident that this non-invasive approach can be applied to study tibia bone strain during high impact activities for a large cohort, which will lead to a better understanding of injury mechanism of tibia stress fractures.

During landing, lower-body bones experience large mechanical loads and are deformed. It is essential to measure bone deformation to better understand the mechanisms of bone stress injuries associated with impacts. A novel approach integrating subject-specific musculoskeletal modeling and finite element analysis is used to measure tibial strain during dynamic movements.

Bone stress injuries are common in sports and military trainings. Repetitive large ground impact forces during training could be the cause. It is ...

Stochastic Semi-active Control Method of Structure Based on Magnetorheological Dampers Considering Time Delay

The use of Magnetorheological (MR) dampers in semi-active control systems faces a key challenge: time delay caused by feedback processes, which reduces the reliability of civil engineering structures under stochastic excitations. This paper proposes a Stochastic Optimal Semi-active Control method with time delay compensation (SOSC-PSO), leveraging the Physical Stochastic Optimal control theory (PSO) to address this issue and maintain structural reliability. The proposed method derives the semi-active control force as a function of both current and previous states, compensating for time delays in the control process. To optimize control effectiveness, key parameters are tuned based on a reliability criterion for the system. Validation analyses on single-degree-of-freedom and multi-degree-of-freedom structures under stochastic seismic excitations show that time delays significantly impair the performance of MR dampers. However, the SOSC-PSO method with time delay compensation significantly improves control effectiveness, and with optimized parameters, it enhances the reliability of the structural control system beyond methods without parameter optimization.&#160;&#160;

A Stochastic Optimal Semi-active Control method with time delay compensation (SOSC-PSO) is proposed in this paper, which is designed to maintain the reliability of structures controlled by MR dampers.&#160;

The use of Magnetorheological (MR) dampers in semi-active control systems faces a key challenge: time delay caused by feedback processes, which reduces ...

Experimental Methods to Study Human Postural Control

Many components of the nervous and musculoskeletal systems act in concert to achieve the stable, upright human posture. Controlled experiments accompanied by appropriate mathematical methods are needed to understand the role of the different sub-systems involved in human postural control. This article describes a protocol for performing perturbed standing experiments, acquiring experimental data, and carrying out the subsequent mathematical analysis, with the aim of understanding the role of musculoskeletal system and central control in human upright posture. The results generated by these methods are important, because they provide insight into the healthy balance control, form the basis for understanding the etiology of impaired balance in patients and the elderly, and aid in the design of interventions to improve postural control and stability. These methods can be used to study the role of somatosensory system, intrinsic stiffness of ankle joint, and visual system in postural control, and may also be extended to investigate the role of vestibular system. The methods are to be used in the case of an ankle strategy, where the body moves primarily about the ankle joint and is considered a single-link inverted pendulum.

This article presents an experimental/analytic framework to study human postural control. The protocol provides step-by-step procedures for performing standing experiments, measuring body kinematics and kinetics signals, and analyzing the results to provide insight into the mechanisms underlying human postural control.

Many components of the nervous and musculoskeletal systems act in concert to achieve the stable, upright human posture. Controlled experiments accompanied ...