This work was supported by National Natural Science Foundation of China (31371129).

This protocol was approved by the Institutional Review Board of Psychology Department of Sun Yat-Sen University.
NOTE: A custom program &#34;BouncingBall&#34;9 is provided for accomplishing this protocol. Extracting the file &#34;BouncingBall.zip&#34; generates a directory &#34;BouncingBall&#34;, which contains auditory and visual stimuli files (see Section 1 below), a script &#34;BouncingBall_run.m&#34; for programming of stimulus presentation and response recording (see Section 2 below) and presenting stimuli and recording responses (see Section 3 below), and a script &#34;BouncingBall_analyze.m&#34; for programming of data analyses (see Section 4 below) and performing data analyses (see Section 5 below).
1. Preparation of Auditory and Visual Stimuli
NOTE: The &#34;BouncingBall&#34; directory contains generated stimuli. Readers can use or modify the stimuli as required.
<ol>
	<li>Use software in which auditory tones can be generated, to create a pure tone of 600 Hz and 50 ms duration.</li>
	<li>Use software in which visual images can be generated, to create an image of an orange ball with a transparent background.</li>
	<li>Use a smartphone with a camera to take a picture of an orange basketball, and use software in which visual images can be generated, to create an image of the basketball with a black background.</li>
</ol>
2. Programming of Stimulus Presentation and Response Recording
NOTE: The programming of stimulus presentation and response recording is already implemented in the script &#34;BouncingBall_run.m&#34; in the directory &#34;BouncingBall&#34;, which can be opened and edited using a text edit software. Readers can set up or modify the settings as required, following the comments in the script.
<ol>
	<li>Set up the background color as black.</li>
	<li>Set up each sequence to have 55 events (54 IOIs). For practice (see below), set up the event number as 20.</li>
	<li>Set up each sequence to be repeated 6 times. For practice, set up the repetition number as 2.</li>
	<li>Setup the IOI to be 600 ms or 900 ms.</li>
	<li>Construct the AT sequence.
	<ol>
		<li>Present the pure tone every IOI.</li>
		<li>Permanently display an orange ball of 1.74 cm in diameter (subtended a visual angle of 1.66&#176;) and a white bar of 3.54 cm x 4.06 cm. Present the ball at the center of the computer screen and display the bar 0.92 cm below the bottom edge of the ball. 
		NOTE: The orange ball and the bar are presented to keep visual settings as consistent as possible between the AT sequence and the visual sequences, and to keep the subject fixating on the ball and maintaining attention on the auditory task.</li>
	</ol>
	</li>
	<li>Construct the VF sequence.
	<ol>
		<li>Have the orange ball flash every IOI, i.e., it lasts for 50 ms and disappears for the remaining IOI time.</li>
	</ol>
	</li>
	<li>Construct the VB sequence.
	<ol>
		<li>Replace the orange ball with the realistic orange basketball.</li>
		<li>Let the ball continually move 0.92 cm (movement distance) down and touch the white bar, and then move up to the initial position. 
		NOTE: The velocity of the ball is uniformly varied with a constant acceleration. The acceleration is 0.20 m/s2 for the 600-ms IOI or 0.09 m/s2 for the 900-ms IOI. The ball reaches its peak velocity when touching the bar. Use different accelerations for different IOIs.</li>
	</ol>
	</li>
	<li>Record and save the time when the subject presses the responding key on a computer keyboard.</li>
</ol>
3. Experimental Procedure
NOTE: Presenting stimuli and recording responses are accomplished by typing &#34;BouncingBall_run(SequenceType, IOI, ScreenX, ScreenY, isFormal)&#34; in the command window of the software. &#34;ScreenX&#34; and &#34;ScreenY&#34; refer to the width and height of the computer screen in cm, respectively. The tapping data are automatically recorded in the file &#34;SequenceType_IOI_ScreenX_ScreenY.mat&#34; by the program. IsFormal refers to whether the experiment is the formal experiment or the practice.
<ol>
	<li>Ask the subject to sit in front of a computer screen with a 60-cm viewing distance and wear a headset.</li>
	<li>Give the subject the written informed consent form to sign.</li>
	<li>Give the subject the written experiment instructions.
	<ol>
		<li>In the instructions, explain the metronome synchronization task to the subject: tap (on a keyboard key using their index finger of their preferred hand) in synchrony with the tones in the AT sequence, the flashing balls in the VF sequence, and the moments when the bouncing ball moves to the lowest position (i.e., touches the bar) in the VB sequence.</li>
	</ol>
	</li>
	<li>Open the software. 
	NOTE: The software continuously runs and its command window stays open. Typing the Bouncingball_run command in the command window will run the stimulus presentation and return to the command window when the stimulus presentation is finished.</li>
	<li>Ask the subject to practice with the 600 ms IOI AT, VF, and VB sequences twice to become familiar with the stimuli and task. 
	NOTE: The practice was the same as the below formal experiment except: (1) The order of the three sequence types is not changed across the subjects; (2) Each sequence is repeated 2 times; and (3) Each sequence has 20 events.
	<ol>
		<li>Type &#34;Bouncingball_run(&#39;AT&#39;, 600, 53.1, 29.8, 0)&#34; in the command window and then press the &#39;Enter&#39; key, to perform the practice of the AT sequence. 
		NOTE: The screen size was 53.1 cm x 29.8 cm in Gan et al.4, but the readers should assign the size according to the screen that they use. When the experiment program is running, the subject is performing the task described in the experiment instructions. In addition, the sequence presentation is self-paced, i.e., the subject presses the space bar to start a sequence.</li>
		<li>Type &#34;Bouncingball_run(&#39;VF&#39;, 600, 53.1, 29.8, 0)&#34; in the command window and then press the &#39;Enter&#39; key, to perform the practice of the VF sequence.</li>
		<li>Type &#34;Bouncingball_run(&#39;VB&#39;, 600, 53.1, 29.8, 0)&#34; in the command window and then press the &#39;Enter&#39; key, to perform the practice of the VB sequence.</li>
	</ol>
	</li>
	<li>Ask the subject to conduct the experiment. Counterbalance the orders of IOI types and sequence types among subjects. 
	NOTE: Below is the procedure for one subject.
	<ol>
		<li>Type &#34;Bouncingball_run(&#39;AT&#39;, 600, 53.1, 29.8, 1)&#34; in the command window and then press the &#39;Enter&#39; key, to perform the 600-ms IOI AT sequence.
		<ol>
			<li>Note that the subject will tap on a keyboard key using the index finger in synchrony with the tones in the AT sequence.</li>
		</ol>
		</li>
		<li>Type &#34;Bouncingball_run(&#39;VF&#39;, 600, 53.1, 29.8, 1)&#34; in the command window and then press the &#39;Enter&#39; key, to perform the 600-ms IOI VF sequence.
		<ol>
			<li>Note that the subject will tap on a keyboard key using the index finger in synchrony with the flashing balls.</li>
		</ol>
		</li>
		<li>Type &#34;Bouncingball_run(&#39;VB&#39;, 600, 53.1, 29.8, 1)&#34; in the command window and then press the &#39;Enter&#39; key, to perform the 600-ms IOI VB sequence.
		<ol>
			<li>Note that the subject will tap on a keyboard key using the index finger in synchrony with the moments when the bouncing ball moves to the lowest position (i.e., touches the bar).</li>
		</ol>
		</li>
		<li>Type &#34;Bouncingball_run(&#39;AT&#39;, 900, 53.1, 29.8, 1)&#34; in the command window and then press the &#39;Enter&#39; key, to perform the 900-ms IOI AT sequence.
		<ol>
			<li>Note that the subject will tap on a keyboard key using the index finger in synchrony with the tones in the AT sequence.</li>
		</ol>
		</li>
		<li>Type &#34;Bouncingball_run(&#39;VF&#39;, 900, 53.1, 29.8, 1)&#34; in the command window and then press the &#39;Enter&#39; key, to perform the 900-ms IOI VF sequence.
		<ol>
			<li>Note that the subject will tap on a keyboard key using the index finger in synchrony with the flashing balls.</li>
		</ol>
		</li>
		<li>Type &#34;Bouncingball_run(&#39;VB&#39;, 900, 53.1, 29.8, 1)&#34; in the command window and then press the &#39;Enter&#39; key, to perform the 900-ms IOI VB sequence.
		<ol>
			<li>Note that the subject will tap on a keyboard key using the index finger in synchrony with the moments when the bouncing ball moves to the lowest position (i.e., touches the bar).</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
4. Programming of Data Analyses
NOTE: The programming of data analyses is already implemented in the script &#34;BouncingBall_analyze.m&#34; in the directory &#34;BouncingBall&#34;, which can be opened and edited using a text edit software. Readers can set up or modify the settings as required, following the comments in the script. The raw synchronization data are a sequence of tap times. Use a circular method to analyze the variable periodic synchronization data, which has been detailed in4,5,10. Here, the present protocol describes a procedure to analyze the stability (R) of synchronization, using the circular statistics toolbox (e.g., CircStat toolbox11 ), as described in Gan et al.4
<ol>
	<li>Omit the taps to the first five events in the tap sequence because synchronization typically requires a few taps to stabilize.</li>
	<li>Compute asynchrony.
	<ol>
		<li>For each tap sequence, calculate asynchrony as the difference between the time of a tap and the time of the corresponding event onset, resulting in a sequence of asynchronies. 
		NOTE: It is better to exclude invalid taps for each sequence. Invalid taps include missing tap (i.e., there is no tap during the -1/2 to +1/2 IOI interval around an event) and multiple taps (i.e., when there is more than one tap during the -1/2 to +1/2 IOI interval).</li>
		<li>Convert the times (ms) of asynchronies into angles (degree) by asynchrony*(360/IOI). Then use function circ_ang2rad11 to convert degrees into radians, which results in the relative phase (RP) on a unit circle.</li>
	</ol>
	</li>
	<li>Compute synchronization stability (R).
	<ol>
		<li>For each asynchrony sequence, use function circ_var11 to calculate the variance (S) of the RPs.</li>
		<li>Compute R using R = 1 - S. 
		NOTE: R is the length of the resultant (i.e., average of vectors) of the RPs, ranging from 0 (unstable tapping with uniformly distributed RPs) to 1 (perfectly stable tapping with a unimodal distribution of RPs).</li>
		<li>For each sequence type, calculate the mean value of the RPs of the 6 trials, as the R of that sequence type of that subject.</li>
	</ol>
	</li>
</ol>
5. Performing Data Analyses
NOTE: Below is the analysis of data from one subject collected as introduced above. Performing data analyses is accomplished by typing &#34;Bouncingball_analyze(RecordedFile)&#34; in the command window of the software, which returns the mean and standard deviation (SD) of the stability.
<ol>
	<li>For the 600-ms IOI AT sequence, type &#34;Bouncingball_analyze(&#39;AT_600_53.1, 29.8.mat&#39;)&#34; in the command window and then press the &#39;Enter&#39; key.</li>
	<li>For the 600-ms IOI VF sequence, type &#34;Bouncingball_analyze(&#39;VF_600_53.1, 29.8.mat&#39;)&#34; in the command window and then press the &#39;Enter&#39; key.</li>
	<li>For the 600-ms IOI VB sequence, type &#34;Bouncingball_analyze(&#39;VB_600_53.1, 29.8.mat&#39;)&#34; in the command window and then press the &#39;Enter&#39; key.</li>
	<li>For the 900-ms IOI AT sequence, type &#34;Bouncingball_analyze(&#39;AT_900_53.1, 29.8.mat&#39;)&#34; in the command window and then press the &#39;Enter&#39; key.</li>
	<li>For the 900-ms IOI VF sequence, type &#34;Bouncingball_analyze(&#39;VF_900_53.1, 29.8.mat&#39;)&#34; in the command window and then press the &#39;Enter&#39; key.</li>
	<li>For the 900-ms IOI VB sequence, type &#34;Bouncingball_analyze(&#39;VB_900_53.1, 29.8.mat&#39;)&#34; in the command window and then press the &#39;Enter&#39; key.</li>
</ol>

<ol>
	<li>Repp, B., Su, Y. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sensorimotor+synchronization:+A+review+of+recent+research+(2006-2012).">Sensorimotor synchronization: A review of recent research (2006-2012).</a> Psychon. Bull. Rev. 20, 403-452 (2013).</li><li>Patel, A. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Evolutionary+Biology+of+Musical+Rhythm:+Was+Darwin+Wrong?.">The Evolutionary Biology of Musical Rhythm: Was Darwin Wrong?.</a> PLoS Biol. 12, e1001821 (2014).</li><li>Hove, M. J., Spivey, M. J., Krumhansl, C. 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The authors have nothing to disclose.

This protocol illustrates how to examine a bouncing ball with a uniformly varying velocity in a metronome synchronization task. Given its comparable synchronization performance to that of auditory tones, the bouncing ball is of particular importance for addressing the current research topic of whether SMS is modality specific. 
The critical step in the current protocol is to introduce the uniformly varying velocity of the bouncing ball, and step-by-step instructions are provided to carry out t...

Sensorimotor synchronization (SMS) refers to the coordination of movements (e.g., finger taps) with an external rhythm, and is canonically studied using a simple metronome synchronization task in which the subject is required to tap a finger along with an isochronous sequence1,2. The superiority of the auditory over visual modality in metronome synchronization has been established for over a century: synchronization is much more stable to an auditory sequence consisting of auditory tones (Figure 1A) than to a visual sequence consisting of visual flashes (Figure 1B)1. This auditory advantage of metronome synchronization, however, has recently been challenged by studies employing periodically moving visual stimuli3,4,5,6 (note that periodically moving visual stimuli refer to continuous movements). Hove et al. used a visual sequence composed of an up-down bar moving with a constant velocity, and found that synchronization of the up-down bar was more stable than synchronization of visual flashes, but was still less stable than synchronization of auditory tones3,6. Iversen et al. employed a bouncing ball that had a velocity varied according to a rectified sinusoid, and showed that synchronization of the bouncing ball was close to synchronization of auditory tones5. More recently, Gan et al. used a bouncing ball that had a uniformly varying velocity (i.e., simulating the effect of gravity) (Figure 1C), and found that synchronization of the bouncing ball was not less stable than synchronization of auditory tones4.
The purpose of the current protocol is to introduce a procedure to apply a bouncing ball with a uniformly varying velocity in a metronome synchronization task, as described in Gan et al.4 The metronome synchronization task includes an auditory sequence composed of auditory tones (AT sequence, Figure 1A) and a visual sequence composed of visual flashes (VF sequence, Figure 1B), which are widely adopted in SMS studies1. The third type of sequence in the task is a visual sequence consisting of the bouncing ball (VB sequence, Figure 1C). Whereas modality-specific mechanisms have long been thought to underlay SMS, such as tighter connections between the auditory and motor cortices than between the visual and motor cortices2, whether SMS is modality specific has recently drawn much research attention1,7. The bouncing ball as introduced in the present protocol is particularly useful for addressing the modality issue because of comparable synchronization performances of the bouncing ball and auditory tones. Moreover, metronome synchronization can be flexibly performed over a limited range of IOI (100-1,800 ms)8. To illustrate the application of the bouncing ball for different IOIs, the current protocol includes a 600-ms IOI (which is approximately the most preferred IOI), and a 900-ms IOI.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/56205/56205fig1.jpg" /> 
Figure 1: Illustration of the stimuli. The subject taps along with an isochronous sequence, which is composed of auditory tones (A: the AT sequence), visual flashing balls (B: the VF sequence), or a visual bouncing ball (C: the VB sequence). In C, the velocity and trajectory of the bouncing ball are plotted as functions of time and are indicated by blue and green lines, respectively. The movement distance of the bouncing ball is 9.2 mm and the peak velocity at the bouncing point (i.e., at the lowest ball position) is 0.061 mm/ms. Eight events in a 600-ms IOI sequence are shown. <a href="//cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/56205/56205fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

<table><tbody><tr><td>Psychtoolbox</td><td>http://psychtoolbox.org</td><td>3.0.12</td><td></td></tr><tr><td>MATLAB</td><td>MathWorks</td><td>7.11.0 (R2010b)</td><td></td></tr><tr><td>Adobe Photoshop</td><td>Adobe&amp;nbsp;Systems</td><td>Adobe Photoshop CS6</td><td></td></tr><tr><td>computer monitor</td><td>AOC</td><td>G2460PQU/BR LCD</td><td></td></tr><tr><td>headphone</td><td>PHILIPS</td><td>SHM6500</td><td></td></tr><tr><td>computer keyboard</td><td>Dell</td><td>kb113t</td><td></td></tr><tr><td>smart phone</td><td>Apple</td><td>iphone6s</td><td></td></tr><tr><td>IBM SPSS Statistics</td><td>IBM</td><td>IBM SPSS Statistics 21</td><td></td></tr></tbody></table>

bouncing ball with a uniformly varying velocity in a metronome synchronization task

Sensorimotor synchronization (SMS), a fundamental human ability to coordinate movements with external rhythms, has long been thought to be modality specific. In the canonical metronome synchronization task that requires tapping a finger along with an isochronous sequence, a well-established finding is that synchronization is much more stable to an auditory sequence consisting of auditory tones than to a visual sequence consisting of visual flashes. However, recent studies have shown that periodically moving visual stimuli can substantially improve synchronization compared with visual flashes. In particular, synchronization of a visual bouncing ball that has a uniformly varying velocity was found to be not less stable than synchronization of auditory tones. Here, the current protocol describes the application of the bouncing ball with a uniformly varying velocity in a metronome synchronization task. The usage of the bouncing ball in sequences with different inter-onset intervals (IOI) is included. The representative results illustrate synchronization performance of the bouncing ball, as compared with the performances of auditory tones and visual flashes. Given its comparable synchronization performance to that of auditory tones, the bouncing ball is of particular importance for addressing the current research topic of whether modality-specific mechanisms underlay SMS.

One of the best-known results in SMS studies is that metronome synchronization is much more stable to an auditory sequence consisting of auditory tones than to a visual sequence consisting of visual flashes1, suggesting modality-specific mechanisms of SMS2. However, recent studies have shown that periodically moving visual stimuli can substantially improve synchronization compared to visual flashes3,<sup cl...

Watch this Scientific Journal Video about Bouncing Ball with a Uniformly Varying Velocity in a Metronome Synchronization Task at JoVE.com

Bouncing Ball with a Uniformly Varying Velocity in a Metronome Synchronization Task

The purpose of this protocol is to introduce the application of a bouncing ball with a uniformly varying velocity in a metronome synchronization task.

Sensorimotor synchronization (SMS), a fundamental human ability to coordinate movements with external rhythms, has long been thought to be modality ...

bouncing-ball-with-uniformly-varying-velocity-metronome

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5.9K Views.  Sun Yat-Sen University. The purpose of this protocol is to introduce the application of a bouncing ball with a uniformly varying velocity in a metronome synchronization task. 

Video: Bouncing Ball with a Uniformly Varying Velocity in a Metronome Synchronization Task

Uncovering Beat Deafness: Detecting Rhythm Disorders with Synchronized Finger Tapping and Perceptual Timing Tasks

A set of behavioral tasks for assessing perceptual and sensorimotor timing abilities in the general population (i.e., non-musicians) is presented here with the goal of uncovering rhythm disorders, such as beat deafness. Beat deafness is characterized by poor performance in perceiving durations in auditory rhythmic patterns or poor synchronization of movement with auditory rhythms (e.g., with musical beats). These tasks include the synchronization of finger tapping to the beat of simple and complex auditory stimuli and the detection of rhythmic irregularities (anisochrony detection task) embedded in the same stimuli. These tests, which are easy to administer, include an assessment of both perceptual and sensorimotor timing abilities under different conditions (e.g., beat rates and types of auditory material) and are based on the same auditory stimuli, ranging from a simple metronome to a complex musical excerpt. The analysis of synchronized tapping data is performed with circular statistics, which provide reliable measures of synchronization accuracy (e.g., the difference between the timing of the taps and the timing of the pacing stimuli) and consistency. Circular statistics on tapping data are particularly well-suited for detecting individual differences in the general population. Synchronized tapping and anisochrony detection are sensitive measures for identifying profiles of rhythm disorders and have been used with success to uncover cases of poor synchronization with spared perceptual timing. This systematic assessment of perceptual and sensorimotor timing can be extended to populations of patients with brain damage, neurodegenerative diseases (e.g., Parkinson&#8217;s disease), and developmental disorders (e.g., Attention Deficit Hyperactivity Disorder).

Behavioral tasks that allow for the assessment of perceptual and sensorimotor timing abilities in the general population (i.e., non-musicians) are presented. Synchronization of finger tapping to the beat of an auditory stimuli and detecting rhythmic irregularities provides a means of uncovering rhythm disorders.

A set of behavioral tasks for assessing perceptual and sensorimotor timing abilities in the general population (i.e., non-musicians) is presented here ...

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

MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions

Path integration is a process in which self-motion is integrated over time to obtain an estimate of one's current position relative to a starting point 1. Humans can do path integration based exclusively on visual 2-3, auditory 4, or inertial cues 5. However, with multiple cues present, inertial cues - particularly kinaesthetic - seem to dominate 6-7. In the absence of vision, humans tend to overestimate short distances (&lt;5 m) and turning angles (&lt;30&deg;), but underestimate longer ones 5. Movement through physical space therefore does not seem to be accurately represented by the brain.
Extensive work has been done on evaluating path integration in the horizontal plane, but little is known about vertical movement (see 3 for virtual movement from vision alone). One reason for this is that traditional motion simulators have a small range of motion restricted mainly to the horizontal plane. Here we take advantage of a motion simulator 8-9 with a large range of motion to assess whether path integration is similar between horizontal and vertical planes. The relative contributions of inertial and visual cues for path navigation were also assessed.
16 observers sat upright in a seat mounted to the flange of a modified KUKA anthropomorphic robot arm. Sensory information was manipulated by providing visual (optic flow, limited lifetime star field), vestibular-kinaesthetic (passive self motion with eyes closed), or visual and vestibular-kinaesthetic motion cues. Movement trajectories in the horizontal, sagittal and frontal planes consisted of two segment lengths (1st: 0.4 m, 2nd: 1 m; &plusmn;0.24 m/s2 peak acceleration). The angle of the two segments was either 45&deg; or 90&deg;. Observers pointed back to their origin by moving an arrow that was superimposed on an avatar presented on the screen.
Observers were more likely to underestimate angle size for movement in the horizontal plane compared to the vertical planes. In the frontal plane observers were more likely to overestimate angle size while there was no such bias in the sagittal plane. Finally, observers responded slower when answering based on vestibular-kinaesthetic information alone. Human path integration based on vestibular-kinaesthetic information alone thus takes longer than when visual information is present. That pointing is consistent with underestimating and overestimating the angle one has moved through in the horizontal and vertical planes respectively, suggests that the neural representation of self-motion through space is non-symmetrical which may relate to the fact that humans experience movement mostly within the horizontal plane.

An efficient way to gain insight into how humans navigate themselves in three dimensions is described. The method takes advantage of a motion simulator capable of moving observers in ways unattainable by traditional simulators. Results confirm that movement in the horizontal plane is underestimated, while vertical movement is overestimated.

Path integration is a process in which self-motion is integrated over time to obtain an estimate of one's current position relative to a starting point 1. ...

Neuroscience

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

The fields that develop methods for sensory substitution and sensory augmentation have aimed to control external goals using signals from the central nervous systems (CNS). Less frequent however, are protocols that update external signals self-generated by interactive bodies in motion. There is a paucity of methods that combine the body-heart-brain biorhythms of one moving agent to steer those of another moving agent during dyadic exchange. Part of the challenge to accomplish such a feat has been the complexity of the setup using multimodal bio-signals with different physical units, disparate time scales and variable sampling frequencies.
In recent years, the advent of wearable bio-sensors that can non-invasively harness multiple signals in tandem, has opened the possibility to re-parameterize and update the peripheral signals of interacting dyads, in addition to improving brain- and/or body-machine interfaces. Here we present a co-adaptive interface that updates efferent somatic-motor output (including kinematics and heart rate) using biosensors; parameterizes the stochastic bio-signals, sonifies this output, and feeds it back in re-parameterized form as visuo/audio-kinesthetic reafferent input. We illustrate the methods using two types of interactions, one involving two humans and another involving a human and its avatar interacting in near real time. We discuss the new methods in the context of possible new ways to measure the influences of external input on internal somatic-sensory-motor control.

We present protocols and methods of analyses to build co-adaptive interfaces that stream, parameterize, analyze, and modify human body and heart signals in close-loop. This setup interfaces signals derived from the peripheral and central nervous systems of the person with external sensory inputs to help track biophysical change.

The fields that develop methods for sensory substitution and sensory augmentation have aimed to control external goals using signals from the central ...

Investigating Oculomotor Responses in Visual Duration Perception Tasks

Eye Movements in Visual Duration Perception: Disentangling Stimulus from Time in Predecisional Processes

Eye-tracking methods may allow the online monitoring of cognitive processing during visual duration perception tasks, where participants are asked to estimate, discriminate, or compare time intervals defined by visual events like flashing circles. However, and to our knowledge, attempts to validate this possibility have remained inconclusive so far, and results remain focused on offline behavioral decisions made after stimulus appearance. This paper presents an eye-tracking protocol for exploring the cognitive processes preceding behavioral responses in an interval comparison task, where participants viewed two consecutive intervals and had to decide whether it speeded up (first interval longer than second) or slowed down (second interval longer). 
Our main concern was disentangling oculomotor responses to the visual stimulus itself from correlates of duration related to judgments. To achieve this, we defined three consecutive time windows based on critical events: baseline onset, the onset of the first interval, the onset of the second interval, and the end of the stimulus. We then extracted traditional oculomotor measures for each (number of fixations, pupil size) and focused on time-window-related changes to separate the responses to the visual stimulus from those related to interval comparison per se. As we show in the illustrative results, eye-tracking data showed significant differences that were consistent with behavioral results, raising hypotheses on the mechanisms engaged. This protocol is embryonic and will require many improvements, but it represents an important step forward in the current state of art.

Author Spotlight: Exploring the Link Between Time Perception of Visual Stimuli and Reading Skills

We present a protocol that employs eye tracking to monitor eye movements during an interval comparison (duration perception) task based on visual events. The aim is to provide a preliminary guide to separate oculomotor responses to duration perception tasks (comparison or discrimination of time intervals) from responses to the stimulus itself.

Eye-tracking methods may allow the online monitoring of cognitive processing during visual duration perception tasks, where participants are asked to ...

Integrating VR and Biosensors for Quantitative Motion-Muscle Activity Assessment

Setup for the Quantitative Assessment of Motion and Muscle Activity During a Virtual Modified Box and Block Test

The ability to move allows us to interact with the world. When this ability is impaired, it can significantly reduce one's quality of life and independence and may lead to complications. The importance of remote patient evaluation and rehabilitation has recently grown due to limited access to in-person services. For example, the COVID-19 pandemic unexpectedly resulted in strict regulations, reducing access to non-emergent healthcare services. Additionally, remote care offers an opportunity to address healthcare disparities in rural, underserved, and low-income areas where access to services remains limited. 
Improving accessibility through remote care options would limit the number of hospital or specialist visits and render routine care more affordable. Finally, the use of readily available commercial consumer electronics for at-home care can enhance patient outcomes due to improved quantitative observation of symptoms, treatment efficacy, and therapy dosage. While remote care is a promising means to address these issues, there is a crucial need to quantitatively characterize motor impairment for such applications. The following protocol seeks to address this knowledge gap to enable clinicians and researchers to obtain high-resolution data on complex movement and underlying muscle activity. The ultimate goal is to develop a protocol for remote administration of functional clinical tests. 
Here, participants were instructed to perform a medically-inspired Box and Block task (BBT), which is frequently used to assess hand function. This task requires subjects to transport standardized cubes between two compartments separated by a barrier. We implemented a modified BBT in virtual reality to demonstrate the potential of developing remote assessment protocols. Muscle activation was captured for each subject using surface electromyography. This protocol allowed for the acquisition of high-quality data to better characterize movement impairment in a detailed and quantitative manner. Ultimately, these data have the potential to be used to develop protocols for virtual rehabilitation and remote patient monitoring.

Author Spotlight: Enhancing Remote Rehabilitation with Virtual Reality and Electromyography

The protocol described here aims to enhance the quantitative evaluation of upper limb deficits, with the goal of developing additional technology for remote assessment both in the clinic and at home. Virtual reality and biosensor technologies are combined with standard clinical techniques to provide insights into the functioning of the neuromuscular system.

The ability to move allows us to interact with the world. When this ability is impaired, it can significantly reduce one's quality of life and ...

Kinematics and Projectile Motion

Source: Ketron Mitchell-Wynne, PhD,&#160;Asantha Cooray, PhD, Department of Physics &#38; Astronomy, School of Physical Sciences, University of California, Irvine, CA
This experiment demonstrates the kinematics of motion in 1 and 2 dimensions. This lab will begin by studying motion in 1 dimension, under constant acceleration, by launching a projectile directly upward and measuring the maximum height reached. This lab will verify that the maximum height reached is consistent with the kinematic equations derived below.
Motion in 2 dimensions will be demonstrated by launching the ball at an angle &#952;. Using the kinematic equations below, one can predict the distance to where the projectile will land based upon the initial speed, total time, and angle of trajectory. This will demonstrate kinematic motion with and with out acceleration in the y- and x-directions, respectively.

Kinematics, Projectile Motion and Motion in Two Dimension

Source: Ketron Mitchell-Wynne, PhD,&#160;Asantha Cooray, PhD, Department of Physics &#38; Astronomy, School of Physical Sciences, University of ...

Education

JoVE Science Education

Physics

Physics I

Average Velocity

To calculate the other physical quantities in kinematics, we must introduce the time variable. The time variable allows us not only to state the position of the object during its motion, but also how fast it is moving. The speed at which an object is moving is given by the rate at which the position changes with time. For each position xi, we assign a particular time ti. If the details of the motion at each instant are not important, the rate is usually expressed as the average velocity. This vector quantity is simply the total displacement between two points, &#916;x, divided by the time taken to travel between them, &#916;t. The average velocity is a vector and can be positive or negative, depending on the initial and final positions. The expression for the average velocity is given by:
<img alt="Equation1" src="/files/ftp_upload/12617/12617_Equation_1.png" style="height: 35px;" />
Average speed, meanwhile, is the total distance traveled divided by the elapsed time, and is not necessarily the same as the magnitude of the average velocity. For example, if a trip starts and ends at the same location, the total displacement is zero, and therefore the average velocity is zero. The average speed, however, is not zero, because the total distance traveled is greater than zero. If we go on a road trip of 300 km and need to be at our destination at a certain time, then we would be interested in our average speed. A car&#8217;s odometer measures the total distance traveled. So the total distance traveled by the car divided by the total time gives the average speed of the car.
For example, the Shanghai Maglev train connects Longyang Road to Pudong International Airport, which is a distance of 30 km. The journey takes 8 minutes on average. What is the Maglev train&#8217;s average velocity?
The known quantities are the distance and time. The unknown quantity, the average velocity, is the total distance divided by the time. Thus, the Maglev train&#8217;s average velocity is 225 km/hr.
This text is adapted from <a href="https://openstax.org/books/university-physics-volume-1/pages/3-1-position-displacement-and-average-velocity">Openstax, University Physics Volume 1, Section 3.1: Position, Displacement, and Average Velocity</a>.

To calculate the other physical quantities in kinematics, we must introduce the time variable. The time variable allows us not only to state the position ...

JoVE Core

Unit 1

Motion Along a Straight Line

KEY TERMS AND CONCEPTS

Velocity and Acceleration of a Wave

A wave propagates through a medium with a constant speed, known as a wave velocity. It is different from the speed of the particles of the medium, which is not constant. In addition, the velocity of the medium is perpendicular to the velocity of the wave. The variable speed of the particles of the medium implies that there must be acceleration associated with it.&#160;
The velocity of the particles can be obtained by taking the partial derivative of the position equation with respect to time. We can then take the partial derivative of the velocity equation with respect to time, to obtain the acceleration of the medium. We can also calculate the slope and curvature of the wave by taking the first and second partial derivative of the position equation with respect to the position, respectively.
These wave parameters can be used to obtain the linear wave equation, which is the ratio of acceleration to the curvature of a wave. The linear wave equation can describe both transverse and longitudinal waves. Interestingly, if two wave functions are individual solutions to the linear wave equation, then the sum of the two linear wave functions is also a solution to it.
This text is adapted from <a href="https://openstax.org/books/university-physics-volume-1/pages/16-2-mathematics-of-waves">Openstax, University Physics Volume 1, Section 16.2: Mathematics of Waves.</a>

A wave propagates through a medium with a constant speed, known as a wave velocity. It is different from the speed of the particles of the medium, which ...

Waves

Relative Motion Analysis - Velocity

A stroke engine has a slider-crank mechanism that converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider.
When an external force is exerted, it sets the crank into a rotational movement. This, in turn, instigates the motion of the connecting rod, leading to what is referred to as a general plane motion. This process involves two key points - point A on the connecting rod and point B on the crank. Both these points experience translational motion. In addition to this, point B also experiences a rotational motion in relation to point A. This dual motion at point B adds complexity to the mechanics of the stroke engine.
To understand the movements of points A and B, different frames of reference can be used. The absolute translational motion of both points can be examined using a stationary or fixed frame, while the rotational motion of point B relative to point A can be studied with a translating x&#39;y&#39; frame attached to point A.
The absolute linear velocity of point B can then be calculated as the vector sum of two components - the absolute linear velocity of point A and the relative velocity of point B with respect to point A. The latter term signifies the relative velocity of point B due to its rotational motion. This motion&#39;s direction is always perpendicular to segment AB, which connects points A and B.

A stroke engine has a slider-crank mechanism that converts rotational motion from the crank into linear motion of the slider or vice versa.
As the crank ...