This research was supported by the Center of Innovation Program from the Japan Science and Technology Agency, JST.

All aspects of the experiment were approved by the Ethical Committee of Tokyo Institute of Technology.
1. Experimental Setup
<ol>
	<li>Material and Setup for Measuring Proprioceptive Drift.
	<ol>
		<li>Obtain a stand that can hold a 100 x 100 cm plate vertically (Figure 1).</li>
		<li>Obtain a chair on which the participant can sit comfortably during the experiment.</li>
		<li>Obtain a 100 x 100 cm acrylic mirror and matte blackboard.</li>
		<li>Obtain the position tracker (for example, SLC-C02, Cyverse) to track the participant&#39;s right hand position. Spatial resolution should be about 1.5 mm to allow sufficient number of samplings to be used for the machine learning.</li>
		<li>Obtain an infrared LED and retroreflective markers that will be used to indicate the position of the stand and the participant&#39;s right hand, respectively (see steps 1.1.11 and 3.2.6).</li>
		<li>Obtain the foot pedal for the participant&#39;s response.</li>
		<li>Create the custom-made program, which can record and simultaneously display the participant&#39;s response and right hand position and play a beep as feedback of the participant&#39;s response when the foot pedal is pressed. In these experiments, the participant&#39;s right hand position was collected using the motor capture device and its custom-made program according to manufacturer instructions. 
		NOTE: According to a previous paper16, the program was developed with a software development toolkit. The custom-made program developed by the software development toolkit can be adapted for other brands of motion capture devices.</li>
		<li>Use a metronome to provide timing signals for the training of the hand movement, that is, tapping the surface of the mirror or blackboard. See step 3.1.1 for precise training instructions.</li>
		<li>Use noise-cancelling headphones to reduce the possibility that the participant can hear sound cues for the hand position.</li>
		<li>For the visual feedback condition, attach the mirror to the stand. For the condition without visual feedback, attach the blackboard to the stand.</li>
		<li>Place the infrared LED at the top-left of the mirror or blackboard.</li>
	</ol>
	</li>
	<li>Material and Setup for Measuring Sense of Ownership and Agency.
	<ol>
		<li>Repeat the procedure from step 1.1.1 to step 1.1.11.</li>
		<li>Create or obtain the questionnaire assessing sense of ownership and agency (e.g.,10,13,16). Table 1 shows examples from this questionnaire used in the previous study15.</li>
		<li>Use a monitor or tablet PC to display the questionnaire to the participant.</li>
	</ol>
	</li>
</ol>
2. Participants
<ol>
	<li>Recruit about 10 right-handed participants with normal or corrected-to-normal vision. 
	Note: The number of participants can be adjusted according to the experimental goals and the number of repeated trials per participant.</li>
	<li>Obtain written informed consent for participation prior to the start of the experiment.</li>
</ol>
3. Experiment Procedure
<ol>
	<li>Training Phase for the Hand Movement.
	<ol>
		<li>Train the participants to synchronously tap with both hands on the mirror or blackboard at a certain tempo using the metronome. Instruct the participants to perform the tapping movement by keeping the heel of the hand in contact with the mirror or board. At the beginning of training, start the metronome at a tempo of 60 beats per min and then instruct the participant to move both hands synchronously according to the sound of the metronome.</li>
		<li>Ensure that the timing of the participant&#39;s hand movement is close to one cycle per sec (approximately 1 Hz) by comparing it to the sound of the metronome several minutes after the beginning of tapping.</li>
	</ol>
	</li>
	<li>Estimation of Proprioceptive Drift in the Participant&#39;s Midsagittal Plane.
	<ol>
		<li>Use a counter-balanced order of conditions across participants.</li>
		<li>Mount the mirror or blackboard on the stand based on the conditions: with visual feedback, mount the mirror; without visual feedback, mount the blackboard.</li>
		<li>Ensure that participant is sitting very close to the mirror or blackboard, which is positioned along the midsagittal plane of the participant (Figure 1).</li>
		<li>Make sure that participant can see the mirror image of the left hand but cannot see the real right hand.</li>
		<li>Instruct participant to pay attention to the image of the left hand in the mirror during the experiment.</li>
		<li>Put the retroreflective markers on the participant&#39;s right index fingertip and wrist.
		<ol>
			<li>Since the markers are only put on the participant&#39;s right hand, make sure that the haptic sensation of the participant&#39;s right hand due to the attached markers is not altered substantially compared to the left hand by querying the participant orally.</li>
		</ol>
		</li>
		<li>Put the noise-cancelling headphones over the participant&#39;s ears.</li>
		<li>Instruct the participant to move the left hand approximately 30 cm vertically and 30 cm horizontally from the lower right corner of the mirror and to maintain this left hand position during the experiment. This position is set as the origin of the surface of the 2D plane.</li>
		<li>Instruct the participant to place the right hand at will on the other side of the mirror or blackboard and to maintain its position until the end of the trial.</li>
		<li>Instruct the participant about the task as follows:
		<ol>
			<li>At the beginning of each trial, instruct the participant to push the middle button of the foot pedal. At this time, the system will sound a beep through the headphone as feedback of the pedal press.</li>
			<li>After hearing the beep, instruct the participant to start tapping with both hands synchronously at 1 Hz on the board, which is the mirror in the condition with visual feedback or the blackboard in the condition without visual feedback.</li>
			<li>After more than six hand movements, instruct the participant to stop the movement at the preferred time and answer the question about the right hand position by pressing the right or left button on the foot pedal. The right button is a yes and the left is a no. The question is, &#34;Do you feel that the right and left hand are in the same position?&#34; At this moment, the participant will hear a beep as feedback for the pedal press again. 
			NOTE: If the participants ask about the meaning of the &#34;the same position,&#34; tell them that &#34;same position&#34; means that the subjective height and depth of the right hand are equivalent to that of the left hand.</li>
			<li>Instruct the participant to move their right hand to another position of their choice. Then, start the trial again. This cycle will continue for up to 200 trials per condition.</li>
		</ol>
		</li>
		<li>Ensure that the participant can understand the task and ask the participant to restart the task.</li>
		<li>During the task, check that the timing of the participant&#39;s tapping stays at approximately 1 Hz by viewing the movement compared to the metronome. 
		Note: The sound of the metronome can be heard only by the experimenter.</li>
		<li>After finishing about 100 trials, let the participant take a break.</li>
		<li>Perform the experiment for the other conditions (with or without visual feedback) on separate days.</li>
	</ol>
	</li>
	<li>Estimation of Sense of Ownership and Agency in the Mirror Condition.
	<ol>
		<li>Define the right hand&#39;s positions for collecting the participant&#39;s responses on the questionnaire about sense of ownership and agency. For example, in a previous publication16, there were 13 prefixed right hand positions. These points were arranged every 7 cm up to &#177; 21 cm from the origin.</li>
		<li>Carry out the same procedure for the above estimation as listed from step 3.2.2 to step 3.2.7.</li>
		<li>Instruct the participant to place the right hand following the experimenter&#39;s guide and maintain its position until finishing one trial.</li>
		<li>Instruct the participant about the task as follows:
		<ol>
			<li>At the beginning of the trial, push the middle button of the foot pedal. At this time, the participant will hear the beep as feedback of the pedal press.</li>
			<li>Then, start to tap the right and left hands synchronously at 1 Hz.</li>
			<li>After more than six times of tapping, stop tapping when the experimenter indicates. Then, answer the questions about sense of ownership and agency displayed on the monitor using a 7-point Likert scale with ratings ranging from -3 (&#34;totally disagree&#34;) to +3 (&#34;totally agree&#34;) with 0 indicating neither agreement nor disagreement (&#34;uncertain&#34;).</li>
			<li>Move the right hand to the position that the experimenter indicates. Then, start the trial again. This cycle will continue up to the number of right-hand positions that the experimenter defines.</li>
		</ol>
		</li>
		<li>Ensure that the participant can understand the task and ask the participant to start the task.</li>
	</ol>
	</li>
</ol>
4. Data Analysis
<ol>
	<li>The Analysis of Proprioceptive Drift in the Participant&#39;s Midsagittal Plane.
	<ol>
		<li>Obtain the statistical tool that contains the machine learning application, especially support vector machine (e.g., R, MATLAB). Use support vector machine (SVM) as the classifier to extract the borders of the participant&#39;s responses. A previous publication provides an explanation for the algorithms of the classifier (see Chapter 7)20. In this article, we explain the method using R (version 3.1.2).</li>
		<li>Install the package named &#34;kernlab&#34;21, which contains the analysis using SVM in the R application.</li>
		<li>Mark the area that shows proprioceptive drift of the hand as follows (Figure 2 describes the schematic representation of the data analysis flow). See Supplemental software code and data sample for further explanation of this data analysis.
		<ol>
			<li>Calculate the relative right hand positions from the origin. Discard data with errors (e.g., missing position data or participant&#39;s responses) from the analysis.</li>
			<li>Make a probabilistic model of the participant&#39;s &#34;yes&#34; responses in 2D space using the SVM. Use the data from the responses as a symbolic description of the model. Use the data of the right hand position as the parameters of the model. Use the commonly used radial basis function kernel as the kernel for the SVM. In order to avoid the arbitrary analysis, calculate sigma (i.e., parameter used for changing the weight of each data point) by automatic sigma estimation.</li>
			<li>Ensure that the model is correctly fitted by checking that the training errors of the model are under 0.2. Using the probabilistic model, define the area in which the p-value of the participant&#39;s &#34;yes&#34; responses was estimated to be over 0.5.</li>
		</ol>
		</li>
		<li>Average each participant&#39;s data to make an area that shows proprioceptive drift. 
		NOTE: As it is difficult to average the border of the &#34;yes&#34; and &#34;no&#34; response area estimated by the p-values of the responses in 2D space, two types of average are recommended. One method is to average the p-values for the participant&#39;s responses in 2D space, which is the method used prior to estimating the border. The other method is to average the area size, which is used after estimating the border.</li>
	</ol>
	</li>
	<li>Analysis of the Questionnaire Data and Area Size.
	<ol>
		<li>Obtain the statistical tool to assess the significance of the position and the categories of the questionnaire (e.g., SPSS or R).</li>
		<li>Assess normal distribution of all data using the Shapiro-Wilk test, and apply the appropriate non-parametric test when one or more of the corresponding data sets failed to meet the criteria for normal distribution (e.g., Wilcoxon signed-rank test, Friedman test). 
		Note: If a non-parametric method that suits the experiment is lacking, use a parametric method and explain the reasoning. In a previous study16, a two-way repeated-measures ANOVA analyzed the questionnaire data, as there was no non-parametric substitute for this analysis.</li>
	</ol>
	</li>
</ol>

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The authors declare that they have no competing financial interest.

We demonstrate a method to estimate proprioceptive drift in a 2D plane during the mirror illusion using SVM and to compare the result with questionnaire responses for sense of ownership and agency. This novel method revealed that the required offset between visual and proprioceptive feedback to maintain proprioceptive drift is approximately 10 cm and that this offset closely overlaps with the offset required to maintain the feeling of ownership and agency.
Note that the most critical step of t...

In recent years, research about the sense or experience of the self-body, that is, one&#39;s own body, has increased in the context of embodiment. Embodiment refers to the idea or concept of having a physical or virtual body that can interact with the environment, such as reaching, grasping, and touching. For instance, humans can touch an object or another human positioned in the environment by moving their own body, in this case their own arm and hand. Nowadays, this interaction or communication is not limited to using one&#39;s own natural body. Due to inventions and development of human-like robots or avatars in the virtual world, the natural human body can be substituted by an artificial body, such as a humanoid, remote control robot, electric prosthesis, or computer-graphics avatar in virtual reality. For example, researchers developed a robot whose operator can &#34;grasp&#34; an object placed in front of the robot via its mechanical body, even if the robot is placed far away from the operator&#39;s body position1,2. Similar to this example, if a human could perform an action via an artificial body, which body would hold the attribution of the operator&#39;s self-body?
We can easily find topics related to this discussion on the attribution or projection of &#34;self&#34; from our own natural body to an artificial, non-flesh-and-bone body. One example can be found in the medical field; for example, in the field of medical rehabilitation, treatments that &#34;trick&#34; the patient&#39;s self-body sensation using mirrors are being explored for reducing pain and improving motor function of a missing or paralyzed limb, called mirror therapy3-6. In this therapy, the mirrored image of the unaffected body part or limb can mislead the patient&#39;s brain into believing that the missing or paralyzed limb corresponds to the one displayed in the mirror and lead to the feeling that it is still in its former condition (i.e., before the accident). It is still under discussion how this illusion affects the brain&#39;s resilience related to body representation. In addition to this type of discussion on our natural body, we can find similar discussions on embodiment, especially of human-system interaction design issues in the field of engineering. The sense of self for an artificial or virtual body has been well investigated in the context of telepresence, brain-machine interface, and brain-computer interface1,2,7-9. Some researchers reported that a humanlike robot, which can transfer the tactile sensation from its robot hand to the operator&#39;s hand, can capture the operator&#39;s sense of self-body to the robot as well as the sense of being at a place where the robot is positioned rather than where the operator actually exists, called tele-existence1. Other researchers reported that a virtual avatar reflecting the operator&#39;s body movements strongly transfers the operator&#39;s sense of self-body from the operator&#39;s own body to the virtual body9. These findings indicate how users can project their sense of self-body into an artificial body, such as a humanoid, remote control robot, electric prosthesis, or computer-graphics avatar in virtual reality, even if the artificial body is not directly connected to their brain and body.
Basic scientific research on this type of self-body sensation for non-flesh-and-blood, artificial body-like objects examined the underlying brain mechanisms for the experience of self-body using the rubber hand illusion (RHI)10-13 and mirror illusion (MI)14-16 in the medical and engineering fields as well as in psychophysics and neuropsychology. The RHI is the sensation that a rubber hand belongs to one&#39;s own body and is evoked by simultaneously stroking a visible rubber hand and the participant&#39;s obscured hand. In the MI, a hand image in a mirror positioned along the midsagittal axis visually captures the participant&#39;s perceived position of the unseen opposite hand. Moreover, synchronous movements of the reflected and unseen hand evoke the strong sensation as if the reflected hand image were the unseen opposite hand. According to the research on these illusions, the consistency between multimodal information and the prediction and sensory feedback about body movements seems to play an important role for the judgment of self-body attribution. Thus, these two illusions can be simple but powerful evidence and tools for scientists to investigate the brain mechanisms underlying our sensation of being tricked or believing that some artificial object or image can subjectively be our own body part, and that our self-body sensation does not have to be tied to our natural physical body.
In all these studies listed above, the discussion has been based on the concept of &#34;self&#34; consisting of two types of sensation proposed by the philosopher Gallagher17: the sense of ownership and the sense of agency. The sense of ownership refers to the sensation that an observed body part is one&#39;s own. The sense of agency corresponds to the sensation that body movement is self-caused. These two sensations are defined as the minimal self, that is, an immediate sense of the self16. According to this concept, the attribution of the &#34;self&#34; for the natural, damaged, virtual, and mechanical bodies can be evaluated by the same indexes: the sense of ownership and agency. In order to use this sensation for scientific evaluation, the question arises of how to measure the sense of ownership and agency robustly. Currently, the estimation of the sense of ownership and agency mainly relies on questionnaires, originally proposed by Botvinick9. In addition to questionnaires, we can attempt to measure them in quantitative ways. For instance, the skin conductance response (SCR) has been used as a physiological index of ownership in cases where the rubber hand is suddenly cut by a knife18. The SCR is calculated by measuring the electrical characteristics of the skin and is a sensitive and valid indicator for arousal19. Since this method is typically applied for single trials per participant, measuring SCR is not suitable as a physical index during psychophysics experiments that require repetitive measurements within participants. One of the most successful behavioral indexes for the sense of ownership is proprioceptive drift. Proprioceptive drift is the change in the perceived position of the unseen real hand toward the position of an object that looks like a hand, such as the rubber-made prosthesis or computer graphics10-13. Since this change can be estimated repetitively and robustly by measuring the distance between the unseen real hand and the visual image of the hand, proprioceptive drift is a suitable physical index for psychophysical measurements. However, this usage needs to be evaluated carefully, because recent discussions have questioned whether proprioceptive drift can always be used as a behavioral index of ownership12.
Typically, proprioceptive drift is measured in only one of the three directions, such as height, width, or depth. Proprioceptive drift has rarely been measured in multiple directions due to the difficulty of estimating and visualizing multi-dimensional data. This metrological limitation is not critical for basic research exploring the mechanisms that process multisensory information, because experimental conditions can be easily designed and controlled to limit the measured dimensions. However, in daily life, our hands move freely in 3D to follow our intentions. In this situation, it is difficult and inadequate to measure a participant&#39;s behavior with questionnaires, which severely limits movement and positions of the hands. Thus, considering the potential applications for sense of ownership and agency in engineering and rehabilitation, a measurement that includes multiple directions and allows free hand movement is needed to evaluate the spatial relationship between visual and proprioceptive feedback in daily life situations. If such measurement were possible, the measured distance between real and observed hands could be utilized as a guideline for the sense of self-body. This could not only become an indicator for the progress of rehabilitation but also a criterion for the spatial offset between the manipulated target in the display and the operating hand. The question remains as to how this measurement can be implemented reliably and effectively.
To address this question, we introduce a novel method to estimate proprioceptive drift, which corresponds to the shift from the position of the participant&#39;s unseen real hand to that of a visible hand-like object, on a 2D plane using the mirror illusion by combining a psychophysical procedure and an analysis using machine learning. Compared to a rubber hand, the hand image in a mirror strongly captures the participant&#39;s perceived position of the unseen real hand. Moreover, a mirrored image immediately reflects voluntary hand movements for hand placement. Thus, a mirror image was selected as the visual feedback of the participant&#39;s hand. In addition, to measure proprioceptive drift similar to daily life situations, the participants positioned their hidden hand trial-by-trial at their will, and the number of trials was increased. Although any combination of directions could have been used, the combination of height and depth was chosen due to the ease of placing the mirror vertically. To check consistency between our method and previous research13, two visual conditions were implemented: with and without visual feedback. In the condition with visual feedback, the mirror was positioned along the midsagittal plane to create a reflected image of the left hand, as if it were seen as the right hand. In the condition without visual feedback, a matte blackboard was used in order to hide the participant&#39;s real right hand. We assessed the effectiveness of this novel method by comparing the results to those obtained with a questionnaire on the sense of ownership and agency.

<table><tbody><tr><td>Acric mirror</td><td></td><td></td><td></td></tr><tr><td>Matte blackboard</td><td></td><td></td><td></td></tr><tr><td>custom-made stand</td><td></td><td></td><td>&lt;i&gt;e.g.&lt;/i&gt; wood pole or PVC(poly vinyl chloride) pipe&amp;nbsp;</td></tr><tr><td>Chair</td><td></td><td></td><td></td></tr><tr><td>Foot pedal</td><td>P.I. Engineering</td><td>Classic X-keys USB, and PS/2 Foot Pedals</td><td>Other response device can be avaliable.</td></tr><tr><td>Position sensor</td><td>CyVerse</td><td>SLC-C02</td><td>Other position sensor can be avaliable.</td></tr><tr><td>Custom-made retroreflectivemarker</td><td></td><td>The marker provided by the motion capture vendor can be available.</td><td></td></tr><tr><td>Noise canselling head phone</td><td>bose</td><td>Quiet Comfort 3</td><td>Other head phone can be avaliable.</td></tr><tr><td>PC</td><td>Mouse computer</td><td>NG-N-i300GA</td><td>Other PC can be available.</td></tr></tbody></table>

visualization method for proprioceptive drift on a 2d plane using support vector machine

Proprioceptive drift, which is a perceptual shift in body-part position from the unseen real body to a visible body-like image, has been measured as the behavioral correlate for the sense of ownership. Previously, the estimation of proprioceptive drift was limited to one spatial dimension, such as height, width, or depth. As the hand can move freely in 3D, measuring proprioceptive drift in only one dimension is not sufficient for the estimation of the drift in real life situations. In this article, we provide a novel method to estimate proprioceptive drift on a 2D plane using the mirror illusion by combining an objective behavioral measurement (hand position tracking) and subjective, phenomenological assessment (subjective assessment of hand position and questionnaire) with a sophisticated machine learning approach. This technique permits not only an investigation of the underlying mechanisms of the sense of ownership and agency but also assists in the rehabilitation of a missing or paralyzed limb and in the design rules of real-time control systems with a self-body-like usability, in which the operator controls the system as if it were part of his/her own body.

Representative results from a previous study are presented to illustrate the method16. Figure 3A shows that the area shapes where the participant could not detect the spatial offset between left and right hand position differed between the conditions with (mirror) and without (blackboard) visual feedback. Figure 3B shows that area sizes in the condition with visual feedback are significantly larger than in the condition without visual feedback ...

Watch this Scientific Journal Video about Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine at JoVE.com

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

This article describes a novel method to estimate proprioceptive drift on a 2D plane using the mirror illusion and combining a psychophysical procedure with an analysis using machine learning.

Proprioceptive drift, which is a perceptual shift in body-part position from the unseen real body to a visible body-like image, has been measured as the ...

visualization-method-for-proprioceptive-drift-on-2d-plane-using

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Planar Kinematics of a Rigid Body

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9.2K Views.  Tokyo Institute of Technology.  Dit artikel beschrijft een nieuwe werkwijze proprioceptieve drift schatten op een 2D-vlak met de spiegel illusie en combineren van een psychofysische methode waarvan analyse met machine learning. 

Video: Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior

Kinesthetic awareness is important to successfully navigate the environment. When we interact with our daily surroundings, some aspects of movement are deliberately planned, while others spontaneously occur below conscious awareness. The deliberate component of this dichotomy has been studied extensively in several contexts, while the spontaneous component remains largely under-explored. Moreover, how perceptual processes modulate these movement classes is still unclear. In particular, a currently debated issue is whether the visuomotor system is governed by the spatial percept produced by a visual illusion or whether it is not affected by the illusion and is governed instead by the veridical percept. Bistable percepts such as 3D depth inversion illusions (DIIs) provide an excellent context to study such interactions and balance, particularly when used in combination with reach-to-grasp movements. In this study, a methodology is developed that uses a DII to clarify the role of top-down processes on motor action, particularly exploring how reaches toward a target on a DII are affected in both deliberate and spontaneous movement domains.

It is unclear how top-down signals from the ventral visual stream affect movement. We developed a paradigm to test motor behavior towards a target on a 3D depth inversion illusion. Significant differences are reported in both deliberate, goal-directed movements and automatic actions under illusory and veridical viewing conditions.

Kinesthetic awareness is important to successfully navigate the environment. When we interact with our daily surroundings, some aspects of movement are ...

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

Three Dimensional Vestibular Ocular Reflex Testing Using a Six Degrees of Freedom Motion Platform

The vestibular organ is a sensor that measures angular and linear accelerations with six degrees of freedom (6DF). Complete or partial defects in the vestibular organ results in mild to severe equilibrium problems, such as vertigo, dizziness, oscillopsia, gait unsteadiness nausea and/or vomiting. A good and frequently used measure to quantify gaze stabilization is the gain, which is defined as the magnitude of compensatory eye movements with respect to imposed head movements. To test vestibular function more fully one has to realize that 3D VOR ideally generates compensatory ocular rotations not only with a magnitude (gain) equal and opposite to the head rotation but also about an axis that is co-linear with the head rotation axis (alignment). Abnormal vestibular function thus results in changes in gain and changes in alignment of the 3D VOR response.
Here we describe a method to measure 3D VOR using whole body rotation on a 6DF motion platform. Although the method also allows testing translation VOR responses 1, we limit ourselves to a discussion of the method to measure 3D angular VOR. In addition, we restrict ourselves here to description of data collected in healthy subjects in response to angular sinusoidal and impulse stimulation.
Subjects are sitting upright and receive whole-body small amplitude sinusoidal and constant acceleration impulses. Sinusoidal stimuli (f = 1 Hz, A = 4&deg;) were delivered about the vertical axis and about axes in the horizontal plane varying between roll and pitch at increments of 22.5&deg; in azimuth. Impulses were delivered in yaw, roll and pitch and in the vertical canal planes. Eye movements were measured using the scleral search coil technique 2. Search coil signals were sampled at a frequency of 1 kHz.
The input-output ratio (gain) and misalignment (co-linearity) of the 3D VOR were calculated from the eye coil signals 3.
Gain and co-linearity of 3D VOR depended on the orientation of the stimulus axis. Systematic deviations were found in particular during horizontal axis stimulation. In the light the eye rotation axis was properly aligned with the stimulus axis at orientations 0&deg; and 90&deg; azimuth, but gradually deviated more and more towards 45&deg; azimuth.
The systematic deviations in misalignment for intermediate axes can be explained by a low gain for torsion (X-axis or roll-axis rotation) and a high gain for vertical eye movements (Y-axis or pitch-axis rotation (see Figure 2). Because intermediate axis stimulation leads a compensatory response based on vector summation of the individual eye rotation components, the net response axis will deviate because the gain for X- and Y-axis are different.
In darkness the gain of all eye rotation components had lower values. The result was that the misalignment in darkness and for impulses had different peaks and troughs than in the light: its minimum value was reached for pitch axis stimulation and its maximum for roll axis stimulation.
Case Presentation
Nine subjects participated in the experiment. All subjects gave their informed consent. The experimental procedure was approved by the Medical Ethics Committee of Erasmus University Medical Center and adhered to the Declaration of Helsinki for research involving human subjects.
Six subjects served as controls. Three subjects had a unilateral vestibular impairment due to a vestibular schwannoma. The age of control subjects (six males and three females) ranged from 22 to 55 years. None of the controls had visual or vestibular complaints due to neurological, cardio vascular and ophthalmic disorders.
The age of the patients with schwannoma varied between 44 and 64 years (two males and one female). All schwannoma subjects were under medical surveillance and/or had received treatment by a multidisciplinary team consisting of an othorhinolaryngologist and a neurosurgeon of the Erasmus University Medical Center. Tested patients all had a right side vestibular schwannoma and underwent a wait and watch policy (Table 1; subjects N1-N3) after being diagnosed with vestibular schwannoma. Their tumors had been stabile for over 8-10 years on magnetic resonance imaging.

A method is described to measure three-dimensional vestibulo ocular reflexes (3D VOR) in humans using a six degrees of freedom (6DF) motion simulator. The gain and misalignment of the 3D angular VOR provide a direct measure of the quality of vestibular function. Representative data on healthy subjects are provided

The vestibular organ is a sensor that measures angular and linear accelerations with six degrees of freedom (6DF). Complete or partial defects in the ...

Controlled Rotation of Human Observers in a Virtual Reality Environment

The low cost and availability of Virtual Reality (VR) systems have supported a recent acceleration of research into perception and behavior under more naturalistic, multisensory, and immersive conditions. One area of research that has particularly benefited from the use of VR systems is multisensory integration, for example, the integration of visual and vestibular cues to give rise to a sense of self-motion. For this reason, an accessible method for the controlled physical rotation of an observer in a virtual environment represents a useful innovation. This paper presents a method for automating the rotation of an office swivel chair along with a method for integrating that motion into a VR experience. Using an example experiment, it is demonstrated that the physical motion, thus produced, is integrated with the visual experience of an observer in a way consistent with expectations; high integration when the motion is congruent with the visual stimulus and low integration when the motion is incongruent.

The controlled physical rotation of a human observer is desirable for certain experimental, recreational, and educational applications. This paper outlines a method for converting an office swivel chair into a medium for controlled physical rotation in a virtual reality environment.

The low cost and availability of Virtual Reality (VR) systems have supported a recent acceleration of research into perception and behavior under more ...

Biplanar Videoradiography Dataset for Model-based Pose Estimation Development and New User Training

Measuring the motion of the small foot bones is critical for understanding pathological loss of function. Biplanar videoradiography is well-suited to measure in vivo bone motion, but challenges arise when estimating the rotation and translation (pose) of each bone. The bone's pose is typically estimated with marker- or model-based methods. Marker-based methods are highly accurate but uncommon in vivo due to their invasiveness. Model-based methods are more common but are currently less accurate as they rely on user input and lab-specific algorithms. This work presents a rare in vivo dataset of the calcaneus, talus, and tibia poses, as measured with marker-based methods during running and hopping. A method is included to train users to improve their initial guesses into model-based pose estimation software, using marker-based visual feedback. New operators were able to estimate bone poses within 2&#176; of rotation and 1 mm of translation of the marker-based pose, similar to an expert user of the model-based software, and representing a substantial improvement over previously reported inter-operator variability. Further, this dataset can be used to validate other model-based pose estimation software. Ultimately, sharing this dataset will improve the speed and accuracy with which users can measure bone poses from biplanar videoradiography.

This work presents an in vivo dataset with bone poses estimated with marker-based methods. A method is included here to train operators in improving their initial estimates for model-based pose estimation and reducing inter-operator variability.

Measuring the motion of the small foot bones is critical for understanding pathological loss of function. Biplanar videoradiography is well-suited to ...

Engineering

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 ...

Assessment of Static Graviceptive Perception in the Roll-Plane using the Subjective Visual Vertical Paradigm

Vestibular disorders are among the most common syndromes in medicine. In recent years, new vestibular diagnostic systems have been introduced that allow the examination of all semicircular canals in the clinical setting. Assessment methods of the otolithic system, which is responsible for the perception of linear acceleration and perception of gravity, are far less in clinical use. There are several experimental approaches for measuring the perception of gravity. The most frequently used method is the determination of the subjective visual vertical. This is usually measured with the head in an upright position. We present here an assessment method for testing otolith function in the roll plane. The subjective visual vertical is measured in the head upright position as well as with head inclination of &#177; 15&#176; and &#177; 30&#176; in the roll plane. This extended functional paradigm is an easy-to-perform clinical test of otolith function and ensures increased information content for the detection of impaired graviceptive perception.

The perception of gravity is commonly determined by the subjective visual vertical in the head upright position. The additional assessment at head tilts of &#177; 15&#176; and &#177; 30&#176; in the roll plane ensures increased information content for the detection of impaired graviceptive perception.

Vestibular disorders are among the most common syndromes in medicine. In recent years, new vestibular diagnostic systems have been introduced that allow ...

Medicine

Real-Time Recording and Mapping of Human Interaction with 3D Virtual Objects

Three-Dimensional Mapping of the Rotation of Interactive Virtual Objects with Eye-Tracking Data

We present a method for real-time recording of human interaction with three-dimensional (3D) virtual objects. The approach consists of associating rotation data of the manipulated object with behavioral measures, such as eye tracking, to make better inferences about the underlying cognitive processes.
The task consists of displaying two identical models of the same 3D object (a molecule), presented on a computer screen: a rotating, interactive object (iObj) and a static, target object (tObj). Participants must rotate iObj using the mouse until they consider its orientation to be identical to that of tObj. The computer tracks all interaction data in real time. The participant&#39;s gaze data are also recorded using an eye tracker. The measurement frequency is 10 Hz on the computer and 60 Hz on the eye tracker.
The orientation data of iObj with respect to tObj are recorded in rotation quaternions. The gaze data are synchronized to the orientation of iObj and referenced using this same system. This method enables us to obtain the following visualizations of the human interaction process with iObj and tObj: (1) angular disparity synchronized with other time-dependent data; (2) 3D rotation trajectory inside what we decided to call a &#34;ball of rotations&#34;; (3) 3D fixation heatmap. All steps of the protocol have used free software, such as GNU Octave and Jmol, and all scripts are available as supplementary material.
With this approach, we can conduct detailed quantitative studies of the task-solving process involving mental or physical rotations, rather than only the outcome reached. It is possible to measure precisely how important each part of the 3D models is for the participant in solving tasks, and thus relate the models to relevant variables such as the characteristics of the objects, cognitive abilities of the individuals, and the characteristics of human-machine interface.

Author Spotlight: Insights into the Analysis of Human Interaction with 3D Virtual Objects

We have developed a simple, customizable, and efficient method for recording quantitative processual data from interactive spatial tasks and mapping these rotation data with eye-tracking data.

We present a method for real-time recording of human interaction with three-dimensional (3D) virtual objects. The approach consists of associating ...

Relative Motion Analysis using Rotating Axes

Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x&#39;y&#39;, is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it instrumental in understanding the relative position. The velocity of point B is then calculated as a vector sum of the absolute velocity of point A and the relative rotational velocity of point B with respect to point A. The relative velocity of point B is made up of two terms.
The first term represents the velocity components of point B, but relative to the rotating frame of reference. The second term, on the other hand, signifies the rate of change of the unit vectors of the rotating frame. This can be expressed in terms of angular velocity.
Therefore, in conclusion, the absolute velocity of point B is determined by adding the absolute velocity of point A, the relative velocity of point B in the rotating frame, and the effects caused by the angular velocity within the rotating frame. This comprehensive approach allows for a more accurate and nuanced understanding of the movements within the system.

Consider a member AB in linear motion. Simultaneously point B&#160; rotates around point A.
The position vectors of A and B are determined using a ...

KEY TERMS AND CONCEPTS

Relative Motion Analysis using Rotating Axes-Problem Solving

Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point D, relative motion analysis is used. For the given situation, point D is translating and rotating with respect to point C, so the relative motion analysis can be done using a rotating frame of reference. At the considered instant, the linear velocity and linear acceleration of point C are zero. At the same time, the angular velocity and angular acceleration of point D are along the negative z-axis as the boom is rotating in the clockwise direction.
The relative velocity and relative acceleration equations for the rotating frame of reference for point D are used to calculate the magnitudes of linear velocity and linear acceleration of point D.
<img alt="Equation 1" src="/files/ftp_upload/15541/15541_PT_Equation_001.svg" style="text-align: center; height: 20px;" />
<img alt="Equation 2" src="/files/ftp_upload/15541/15541_PT_Equation_002v2.svg" style="text-align: center; height: 22px;" />
Substituting the known values, magnitudes of linear velocity and linear acceleration of point D are found to be 5.55 m/s and 1.4 m/s2, respectively.

The crane&#39;s telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2.
Simultaneously the boom extends ...

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

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