This study was supported by the I-CORE Program of the Planning and Budgeting Committee and the Israel Science Foundation (grant no. 51/11), and The Israel Science Foundation (grants no. 1771/13 and 2043/13) (R.M.); the Yosef Sagol Scholarship for Neuroscience Research, the Israeli Presidential Honorary Scholarship for Neuroscience Research, and the Sagol School of Neuroscience fellowship (O.O.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors thank E. Kagan and A. Hakim for help with data acquisition,&#160;Lihi Sadeh and Yuval Wilchfort with filming and setup, and O. Levy and Y. Siman-Tov from Rehabit-Tec System for providing access to the passive movement device.

The following protocol was conducted in accordance with guidelines approved by the Human Ethics Committee of Tel-Aviv University.The study includes 2 experiments &#8211; one using visual manipulation, and another combining visual with proprioceptive sensory manipulation. Subjects were healthy, right handed (according to the Edinburgh handedness questionnaire), with normal vision and no reported cognitive deficits or neurological problems. They were na&#239;ve to the purpose of the study and provided written informed consent to take part in the study.
1. Setting up the Virtual Reality environment
<ol>
	<li>Have the subjects sit in a chair with their hands forward and palms facing down.</li>
	<li>Put on the virtual reality (VR) headset with the head-mounted specialized 3D camera to provide online visual feedback of the real environment. Make sure the video from the camera is presented in the VR headset. 
	NOTE: The video is presented by C# codebase custom software, built based on an open-source, cross-platform 3D rendering engine.</li>
	<li>Put on the motion-sensing MR-compatible gloves that allow online monitoring of individual finger flexure in each hand. Ensure that the software embeds the virtual hands in a specific location in space such that the subjects see the virtual hands only when looking down towards the place where their real hands would normally be.</li>
	<li>Throughout the entire experiment, make sure the software records the hand configuration provided by the gloves. 
	NOTE: The embedded virtual hand movement is controlled by the same software that uses C-based application program interface (API) for accessing calibrated raw data and gesture information from the gloves including the angles between fingers&#39; joints.</li>
	<li>Place the subjects&#39; hands in a specialized motion control device and strap the right and left fingers individually to the pistons. Make sure the subjects can move their right hand fingers separately. 
	NOTE: The right hand finger pistons move a plunger on a potentiometer according to the degree of their flexion. This in turn controls a module that reads the location of every potentiometer on each finger of the right hand and powers motors that push/pull the corresponding left hand finger to the corresponding position.</li>
	<li>Verify that voluntary movement of the left hand fingers is restricted by asking the subjects to move their left hand while it is located inside the device. 
	NOTE: Since only the active (right) hand finger movement activates the motors, voluntary left hand finger movement is impossible when the device is turned on.</li>
</ol>
2. Conducting the experiment
NOTE: See Figure 1 for the experimental stages. Each subject underwent three instruction-evaluation-train-evaluation experimental sessions. The details of the instructions and evaluation stages are provided in the Representative results section.
<ol>
	<li>Unstrap subjects&#39; hands from the motion control device.</li>
	<li>Have the subjects perform a unimanual 5-digit finger sequence movement repeatedly as accurately and rapidly as possible with the non-training hand in a pre-defined time frame (e.g. 30 s). Each individual finger flexion should be at least 90 degrees. 
	NOTE: The fingers are numbered from index (1) to little finger (4) and the instructions include a specific 5-digit sequence. If the sequence is 4-1-3-2-4, have the subjects move their fingers in the following order: little-index-ring-middle-little.</li>
	<li>After the evaluation (step 2.2), strap the hands of the subject to the motion control device.</li>
	<li>Cue the patient to the upcoming training stage to perform the sequence of finger movements with the active hand in a self-paced manner.</li>
	<li>Repeat the evaluation stages 2.1 - 2.2 again.</li>
</ol>
3. Analyzing the behavioral data and calculating performance gains
<ol>
	<li>In the customized software that reads the data files of the gloves recorded during the experiments, click &#39;load left hand data&#39; and choose the files created in the &#39;Left Hand Capture&#39; folder under the relevant subject. 
	NOTE: There are no different folders for pre- and post-evaluations. The file names contain the evaluation step identification.</li>
	<li>Click &#39;load right hand data&#39; and choose the files created in the &#39;Right Hand Capture&#39; folder under the relevant subject.</li>
	<li>Click &#39;Go&#39; to replay and visualize the virtual hands movements during each evaluation stage based on the data recorded from the sensors in the motion-tracking glove.</li>
	<li>For each evaluation step and each subject separately, count the number of complete and correct finger sequences (P) performed with the non-trained hand. 
	NOTE: A finger movement is considered valid only when the angle between the proximal phalange and the metacarpal reached 90&#730;. A 5-digit sequence is considered complete and correct only if all finger movements were valid.</li>
	<li>Calculate performance gains index (G) according to the following formula: 
	<img alt="Equation" src="/files/ftp_upload/55965/55965eq1.jpg" /> 
	Where Ppost_training/Ppre_training correspond to the subject&#39;s performance (number of complete finger sequences) in the post/pre training evaluation stage respectively.</li>
</ol>

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The authors declare that they have no competing financial interests.

We describe a novel training setup and demonstrate how embedding virtual sensory feedback in a real-world environment optimizes motor learning in a hand that is not trained under voluntary control. We manipulated feedback in two modalities: visual and proprioceptive.
There are few critical steps in the presented protocol. First, the system consists of several separate components (gloves, VR headset, camera, and passive movement device) that should be carefully connected while setting up the VR environment. To that end, the experimenter should keep the exact order described in the protocol and verify subjects&#39; convenience.
The combination of visual and proprioceptive manipulation during training introduced significantly higher performance gains in the non-trained hand relative to other existing training types such as learning by observation17, and CE3 with and without passive hand movements24,25,26.
It is an open question whether the enhanced performance gains in the current demonstration generalizes to other tasks, training durations, feedback modalities or hand identities (left active hand, or bi-manual movements). The current study was limited to right-handed subjects using a simple finger sequence task. Additionally, the proprioception manipulation in the current setup is based on a system that allows very limited movements (such as finger flexion/extension) for a relatively short-term training. Further work is required to establish the generalizability of the presented setup to other types of behaviors.
The current setup can be extended in several ways. First, new types of modalities can be added for example, binding different auditory sounds to different finger movements during the sequence task. This might result in a supra-additive effect which will further optimize learning in the untrained hand. Second, the current design of the system enables an easy swap between the voluntary moving hand (right hand in the current description) and the passively yoked hand (left hand). Future studies can capitalize on this flexibility to examine how directionality of transfer (between dominant and non-dominant hands3) can modify the level of performance gains when using the presented sensory manipulations. Finally, the unique VR setup we developed may be adapted to more complex tasks (as opposed to the simple finger sequence task). Virtual simulation of external objects such as balls, pins, and boards can be embedded into the real environment providing a rich and engaging training experience.
As for future applications, the effect described in this study can be readily used with clinical populations such as patients with upper-limb hemiparesis by introducing physical training with the healthy hand and providing visual feedback as if the affected hand is moving. Given that voluntary control of the affected limb is limited in such populations, this training scheme has the potential of circumventing the challenges of direct physical therapy of the affected hand and perhaps resulting in better recovery rates30,31. This approach, exploiting the phenomenon of cross-education and mirror-therapy, together with well-established rehabilitation tasks, has not been previously tested in clinical patients and has the potential for providing a more efficient rehabilitation regime. Finally, since this setup is partially MR compatible, it enables the use of whole-brain functional magnetic resonance imaging (fMRI) to probe the relevant neural circuits engaged during such training12.

Physical practice is the most efficient form of training. Although this approach is well established1, it is very challenging in cases where the basic motor capability of the training hand is limited2. To bypass this problem, a large and growing body of literature examined various indirect approaches of motor training.
One such indirect training approach uses physical practice with one hand to introduce performance gains in the other (non-practiced) hand. This phenomenon, known as cross-education (CE) or intermanual transfer, has been studied extensively 3,4,5,6,7,8,9 and used to enhance performance in various motor tasks 10,11,12. For instance, in sport skill settings, studies have demonstrated that training basketball dribbling in one hand transfers to increased dribbling capabilities in the other, untrained hand 13,14,15.
In another indirect approach, motor learning is facilitated through the use of visual or sensory feedback. In learning by observation, it has been demonstrated that significant performance gains can be obtained simply by passively observing someone else perform the task16,17,18,19,20. Similarly, proprioceptive training, in which the limb is passively moved, was also shown to improve performance on motor tasks 12,21,22,23,24,25,26.
Together, these lines of research suggest that sensory input plays an important role in learning. Here, we demonstrate that manipulating online sensory feedback (visual and proprioceptive) during physical training of one limb results in augmented performance gain in the opposite limb. We describe a training regime that yields optimal performance outcome in a hand, in the absence of its voluntary physical training. The conceptual novelty of the proposed method resides in the fact that it combines the three different forms of learning - namely, learning by observation, CE, and passive movement. Here we examined whether the phenomenon of CE, together with mirrored visual feedback and passive movement, can be exploited to facilitate learning in healthy subjects in the absence of voluntary physical movement of the training limb.
The concept in this setup differs from direct attempts to physically train the hand. At the methodological level - we introduce a novel setup including advanced technologies such as 3D virtual reality, and custom built devices that allow manipulating visual and proprioceptive input in a natural environmental setting. Demonstrating improved outcome using the proposed training has key consequences for real-world learning. For example, children use sensory feedback in a manner that is different from that of adults27,28,29 and in order to optimize motor learning, children may require longer periods of practice. The use of CE together with manipulated sensory feedback might reduce training duration. Furthermore, acquisition of sport skills might be facilitated using this kind of sophisticated training. Finally, this can prove beneficial for the development of a new approach for rehabilitation of patients with unilateral motor deficits such as stroke.

<table><tbody><tr><td>Oculus Development Kit 1</td><td>Oculus VR</td><td></td><td>The Oculus Rift DK1 is a virtual reality headset developed and manufactured by Oculus VR, and contains development kit.</td></tr><tr><td>5DT Data Glove 14 MRI Right-handed and left handed</td><td>Fifth dimension Technologies</td><td>100-0009 and 100-0010</td><td>The 5DT Data Glove Ultra is designed to satisfy the stringent requirements of modern Motion Capture and Animation Professionals. It offers comfort, ease of use, a small form factor and multiple application drivers. The high data quality, low cross-correlation and high data rate make it ideal for realistic realtime animation.</td></tr><tr><td>PlayStation Eye Camera</td><td>Sony</td><td></td><td>The PlayStation Eye (trademarked PLAYSTATION Eye) is a digital camera device, similar to a webcam, for thePlayStation 3. The technology uses computer vision and gesture recognition to process images taken by the camera.</td></tr><tr><td>REHABILITATION SYSTEM REHABIT-TEC</td><td>Rehabit-Tec</td><td>www.rehabit-tec.com</td><td>The Rehabit-Tec Rehabilitation system is a rehabilitation system intended to allow a CVA injured individual advance self rehabilitation on the basis of mirror movements</td></tr></tbody></table>

using virtual reality to transfer motor skill knowledge from one hand to another

As far as acquiring motor skills is concerned, training by voluntary physical movement is superior to all other forms of training (e.g. training by observation or passive movement of trainee&#39;s hands by a robotic device). This obviously presents a major challenge in the rehabilitation of a paretic limb since voluntary control of physical movement is limited. Here, we describe a novel training scheme we have developed that has the potential to circumvent this major challenge. We exploited the voluntary control of one hand and provided real-time movement-based manipulated sensory feedback as if the other hand is moving. Visual manipulation through virtual reality (VR) was combined with a device that yokes left-hand fingers to passively follow right-hand voluntary finger movements. In healthy subjects, we demonstrate enhanced within-session performance gains of a limb in the absence of voluntary physical training. Results in healthy subjects suggest that training with the unique VR setup might also be beneficial for patients with upper limb hemiparesis by exploiting the voluntary control of their healthy hand to improve rehabilitation of their affected hand.

36 subjects in two experiments trained to execute rapid sequences of right hand finger movements while sensory (visual/proprioceptive) feedback was manipulated. Fingers were numbered from index (1) to little finger (4) and each subject was asked to learn three different sequences in three consecutive experimental sessions such as: 4-1-3-2- 4, 4-2-3-1-4, and 3-1-4-2-3. Each sequence/session was associated with a specific training type and the association between sequence and training type was counterbalanced across subjects. At the beginning of each session, subjects were presented with an instruction slide that depicted two hand illustrations (right and left) with numbered fingers and a specific 5 number sequence underneath, representing the sequence of finger movements to be learned (see Figure 1). The instructions slide (12 s) was followed by the pre-training evaluation stage (30 s). At this stage, online visual feedback consisted of a display of two virtual hands whose finger movements were yoked in real-time to the subjects&#39; actual finger movements (virtual hands were based on a model available in 5DT gloves toolbox). Thus, real left hand movement was accompanied by visual feedback of left (congruent) virtual hand movement. Subjects were instructed to repeatedly execute the sequence as fast and as accurately as possible with their left hand. In the following training stage, subjects trained on the sequence under a specific experimental condition in a self-paced manner. The training stage contained 20 blocks, each training block lasted 15 s followed by 9 s of yellow blank screen, which served as cue for resting period. We used 20 training blocks, which in our case were sufficient to obtain significant differences between conditions. Finally, a post-training evaluation stage identical to the pre-training evaluation was conducted. Each subject underwent three such instruction-evaluation-train-evaluation experimental sessions. Each experimental session was associated with a unique training condition and finger sequence. In experiment 1, we compared the G index values across the following training conditions: (1) training by observation - subjects passively observed the virtual left hand performing the sequence while both their real hands were immobile; (2) CE - subjects physically trained with their right hand while receiving congruent online visual feedback of right virtual hand movement; (3) CE + Visual manipulation (VM) - importantly, the VR setup allowed us to create a unique 3d experimental condition in which subjects physically trained with their right hand while receiving online visual feedback of left (incongruent) virtual hand movement (CE + VM condition). Left virtual hand finger movement was based on real right hand finger movement detected by the gloves (step 1.4). In all conditions - the palm of the subjects&#39; hands were facing up. The pace of virtual hand finger movement in the training by observation condition (condition 1) was set based on the average pace of the subject during previous active right hand conditions (conditions 2 and 3). In cases where the order of training conditions due to counterbalancing was such that training-by-observation was first, the pace was set based on the average pace of the previous subject. All G index comparisons were performed in a within-subject paired-fashion across the different training conditions.
Left hand performance gains following training in condition 3 (CE + Visual manipulation) were significantly higher relative to the gains obtained following training by left hand observation (condition 1; p&#60;0.01; two-tailed paired t-test) or following right hand training with congruent visual feedback &#8211; the traditional form of CE (condition 2; p&#60;0.05; two tailed paired t-test; Figure 2 and Table 1). Interestingly, the training with incongruent visual feedback (CE + VM) yielded higher performance gain than the sum of gains obtained by two basic training types: physical training of the right hand, and training by observation of left-hand without physical movement. This super additive effect demonstrates that performance gains in the left hand are non-linearly enhanced when right hand training is supplemented with left hand visual feedback that is controlled by the subject. This implies that CE and learning by observation are interacting processes that can be combined to a novel learning scheme.
We also examined in another set of 18 healthy subjects whether the addition of passive left hand movement can further enhance left hand performance gains. To this end in study 2, subjects underwent a similar protocol with 3 training types while their hands were placed inside the aforementioned custom-built device (step 1.7) that controls left hand finger movement. In this experiment, subjects trained for 10 blocks. Each training block lasted 50 s followed by 10 s of a yellow blank screen which served as cue for resting period. The following three training types were used: (1) CE + VM &#8211; cross education accompanied by manipulated visual feedback (similar to condition 3 from study 1); (2) CE+PM &#8211; standard cross-education (i.e. right hand active movement + visual feedback of right virtual hand movement), together with yoked passive movement (PM) of the left hand; (3) CE+VM+ PM &#8211; subjects physically trained with their right hand while visual input was manipulated such that corresponding left virtual hand movement was displayed (similar to condition 3 used in the first study). However, in addition, right hand active finger movement resulted in yoked passive left hand finger movement through the device.
The addition of passive left-hand finger movement to the visual manipulation, yielded the highest left-hand performance gains (Figure 3 and Table 2), that were significantly higher than performance gains following the visual manipulation alone (condition 1; p&#60;0.01; two-tailed paired t-test). It should be noted that although the CE+VM training condition was similar to that in study 1, absolute G values are only comparable across conditions within the same study. This is due to the fact that (1) training design was slightly different (in study 2 the palms faced down and not up due to the device, different duration/number of training blocks) and (2) each experiment was conducted on a different group of subjects. Importantly, within each study, each subject performed all three training types and G indices across conditions are compared in a paired fashion.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/55965/55965fig1.jpg" /> 
Figure 1. Experiment Design. Schematic illustration of a single experimental session in study 1. Each subject&#160;performed 3 such sessions. In each session, a unique sequence of five digits was presented together with a sketch of the mapped fingers. After instructions, subjects performed the sequence as fast and as accurate as possible using their left hand for initial evaluation of performance level. Next, subjects trained on the sequence by one of the training types (see representative results) in a self-paced manner. After training, subjects repeated the evaluation stage for re-assessment of performance level. In study 2, the overall design was similar, with different durations/amount of training blocks (detailed in the representative results). Hands in the illustration represent the active hand only (the visual feedback always contained two virtual hands).&#160;<a href="https://cloudflare.jove.com/files/ftp_upload/55965/55965fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/55965/55965fig2.jpg" /> 
Figure 2. Study 1 &#8211; left hand performance gains. Physical training with the right hand while receiving online visual feedback as if the left hand is moving (CE + visual manipulation; VM; red) resulted in highest left hand performance gains relative to the other training conditions examined: left hand observation (yellow), and cross-education without visual manipulation (i.e. right hand training + congruent visual feedback of right virtual hand movement; green). Error bars denote SEM across 18 subjects. <a href="https://cloudflare.jove.com/files/ftp_upload/55965/55965fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/55965/55965fig3.jpg" /> 
Figure 3. Study 2 &#8211; left hand performance gains. The highest left hand performance gain was obtained when cross education with visual manipulation was combined with passive left hand finger movement by the device (CE+VM+PM; light red). This improvement was significantly higher than that obtained following cross education with visual manipulation (CE+VM; red) and cross education with proprioceptive manipulation (CE+PM; green). Error bars denote SEM across 18 subjects. <a href="https://cloudflare.jove.com/files/ftp_upload/55965/55965fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

<img alt="Table 1" src="/files/ftp_upload/55965/55965table1.jpg" /> 
Table 1. Study 1 data. Individual subject&#39;s performance (P) during pre- and post-training evaluation stages in study 1. Each cell represents the number of correctly performed complete 5-digit sequences within 30 s. S &#8211; Subject number. <a href="http://cloudflare.jove.com/files/ftp_upload/55965/Table1.pdf">Please click here to download this Table.</a>

<img alt="Table 2" src="/files/ftp_upload/55965/55965table2.jpg" /> 
Table 2. Study 2 data. Same as Table 1 for study 2. Note that training duration and hand orientation in this experiment were different than experiment 1 (see text). <a href="http://cloudflare.jove.com/files/ftp_upload/55965/Table2.pdf">Please click here to download this Table.</a>

Watch this Scientific Journal Video about Using Virtual Reality to Transfer Motor Skill Knowledge from One Hand to Another at JoVE.com

Using Virtual Reality to Transfer Motor Skill Knowledge from One Hand to Another

We describe a novel virtual-reality based setup which exploits voluntary control of one hand to improve motor-skill performance in the other (non-trained) hand. This is achieved by providing real-time movement-based sensory feedback as if the non-trained hand is moving. This new approach may be used to enhance rehabilitation of patients with unilateral hemiparesis.

As far as acquiring motor skills is concerned, training by voluntary physical movement is superior to all other forms of training (e.g. training by ...

using-virtual-reality-to-transfer-motor-skill-knowledge-from-one-hand

Research

JoVE Journal

Behavior

545.8K Views.  Tel-Aviv University. We describe a novel virtual-reality based setup which exploits voluntary control of one hand to improve motor-skill performance in the other (non-trained) hand. This is achieved by providing real-time movement-based sensory feedback as if the non-trained hand is moving. This new approach may be used to enhance rehabilitation of patients with unilateral hemiparesis.

Video: Using Virtual Reality to Transfer Motor Skill Knowledge from One Hand to Another

Haptic/Graphic Rehabilitation: Integrating a Robot into a Virtual Environment Library and Applying it to Stroke Therapy

Recent research that tests interactive devices for prolonged therapy practice has revealed new prospects for robotics combined with graphical and other forms of biofeedback. Previous human-robot interactive systems have required different software commands to be implemented for each robot leading to unnecessary developmental overhead time each time a new system becomes available. For example, when a haptic/graphic virtual reality environment has been coded for one specific robot to provide haptic feedback, that specific robot would not be able to be traded for another robot without recoding the program. However, recent efforts in the open source community have proposed a wrapper class approach that can elicit nearly identical responses regardless of the robot used. The result can lead researchers across the globe to perform similar experiments using shared code. Therefore modular "switching out"of one robot for another would not affect development time. In this paper, we outline the successful creation and implementation of a wrapper class for one robot into the open-source H3DAPI, which integrates the software commands most commonly used by all robots.

Recently, a vast amount of prospects have come available for human-robot interactive systems. In this paper we outline the integration of a new robotic device with open source software that can rapidly make possible a library of interactive functionality. We then outline a clinical application for a neurorehabilitation application.

Recent research that tests interactive devices for prolonged therapy practice has revealed new prospects for robotics combined with graphical and other ...

Bioengineering

Multifunctional Setup for Studying Human Motor Control Using Transcranial Magnetic Stimulation, Electromyography, Motion Capture, and Virtual Reality

The study of neuromuscular control of movement in humans is accomplished with numerous technologies. Non-invasive methods for investigating neuromuscular function include transcranial magnetic stimulation, electromyography, and three-dimensional motion capture. The advent of readily available and cost-effective virtual reality solutions has expanded the capabilities of researchers in recreating &#8220;real-world&#8221; environments and movements in a laboratory setting. Naturalistic movement analysis will not only garner a greater understanding of motor control in healthy individuals, but also permit the design of experiments and rehabilitation strategies that target specific motor impairments (e.g. stroke). The combined use of these tools will lead to increasingly deeper understanding of neural mechanisms of motor control. A key requirement when combining these data acquisition systems is fine temporal correspondence between the various data streams. This protocol describes a multifunctional system&#8217;s overall connectivity, intersystem signaling, and the temporal synchronization of recorded data. Synchronization of the component systems is primarily accomplished through the use of a customizable circuit, readily made with off the shelf components and minimal electronics assembly skills.

Transcranial magnetic stimulation, electromyography, and 3D motion capture are commonly used non-invasive techniques for investigating neuromuscular function in humans. In this paper, we describe a protocol that synchronously samples data generated by all three of these tools along with the unique addition of virtual reality stimulus presentation and feedback.

The study of neuromuscular control of movement in humans is accomplished with numerous technologies. Non-invasive methods for investigating neuromuscular ...

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

Advances in robotic systems have resulted in prostheses for the upper limb that can produce multifunctional movements. However, these sophisticated systems require upper limb amputees to learn complex control schemes. Humans have the ability to learn new movements through imitation and other learning strategies. This protocol describes a structured rehabilitation method, which includes imitation, repetition, and reinforcement learning, and aims to assess if this method can improve multifunctional prosthetic control. A left below elbow amputee, with 4 years of experience in prosthetic use, took part in this case study. The prosthesis used was a Michelangelo hand with wrist rotation, and the added features of wrist flexion and extension, which allowed more combinations of hand movements. The participant&#8217;s Southampton Hand Assessment Procedure score improved from 58 to 71 following structured training. This suggests that a structured training protocol of imitation, repetition and reinforcement may have a role in learning to control a new prosthetic hand. A larger clinical study is however required to support these findings.

As prosthetic development moves towards the goal of natural control, harnessing amputees&#8217; inherent ability to learn new motor skills may enable proficiency. This manuscript describes a structured rehabilitation protocol, which includes imitation, repetition, and reinforcement learning strategies, for improved multifunctional prosthetic control.

Advances in robotic systems have resulted in prostheses for the upper limb that can produce multifunctional movements. However, these sophisticated ...

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.

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 ...

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation

Studies investigating how people represent themselves and their own body often use variants of &#34;ownership illusions&#34;, such as the traditional rubber-hand illusion or the more recently discovered enfacement illusion. However, these examples require rather artificial experimental setups, in which the artificial effector needs to be stroked in synchrony with the participants&#39; real hand or face&#8212;a situation in which participants have no control over the stroking or the movements of their real or artificial effector. Here, we describe a technique to establish ownership illusions in a setup that is more realistic, more intuitive, and of presumably higher ecological validity. It allows creating the virtual-hand illusion by having participants control the movements of a virtual hand presented on a screen or in virtual space in front of them. If the virtual hand moves in synchrony with the participants&#39; own real hand, they tend to perceive the virtual hand as part of their own body. The technique also creates the virtual-face illusion by having participants control the movements of a virtual face in front of them, again with the effect that they tend to perceive the face as their own if it moves in synchrony with their real face. Studying the circumstances that illusions of this sort can be created, increased, or reduced provides important information about how people create and maintain representations of themselves.

Here, we describe virtual-hand and virtual-face illusion paradigms that can be used to study body-related self-perception/-representation. They have already been used in various studies to demonstrate that, under specific conditions, a virtual hand or face can be incorporated into one&#39;s body representation, suggesting that body representations are rather flexible.

Studies investigating how people represent themselves and their own body often use variants of &#34;ownership illusions&#34;, such as the traditional ...

Mobile Game-based Virtual Reality Program for Upper Extremity Stroke Rehabilitation

Stroke rehabilitation requires repetitive, intensive, goal-oriented therapy. Virtual reality (VR) has the potential to satisfy these requirements. Game-based therapy can promote patients&#39; engagement in rehabilitation therapy as a more interesting and a motivating tool. Mobile devices such as smartphones and tablet PCs can provide personalized home-based therapy with interactive communication between patients and clinicians. In this study, a mobile VR upper extremity rehabilitation program using game applications was developed. The findings from the study show that the mobile game-based VR program effectively promotes upper extremity recovery in patients with stroke. In addition, patients completed two weeks of treatment using the program without adverse effects and were generally satisfied with the program. This mobile game-based VR upper extremity rehabilitation program can substitute for some parts of the conventional therapy that are delivered one-on-one by an occupational therapist. This time-efficient, easy to implement, and clinically effective program would be a good candidate tool for tele-rehabilitation for upper extremity recovery in patients with stroke. Patients and therapists can collaborate remotely through these e-health rehabilitation programs while reducing economic and social costs.

Here, we present a protocol to develop and apply a mobile game-based virtual reality program for the recovery of upper limb dysfunction in patients with stroke. The present study shows that the mobile program is feasible and effectively promotes upper limb recovery in stroke patients.

Stroke rehabilitation requires repetitive, intensive, goal-oriented therapy. Virtual reality (VR) has the potential to satisfy these requirements. ...

Virtual Hand with Ambiguous Movement between the Self and Other Origin: Sense of Ownership and 'Other-Produced' Agency

The feeling that a body part is one&#8217;s own body (sense of ownership; SoO) and the feeling based on the causal relationship between one&#8217;s will and action (sense of agency; SoA) have been recognized as the basis of our bodily self-consciousness. Previously, the illusory SoO over a fake body part (e.g., rubber hand) was introduced as the rubber hand illusion (RHI). Furthermore, it was determined that one could also evoke a SoA over an object with movements linked to the one&#8217;s prior intention. On the other hand, the postdictivity of our spontaneity implies that it is essentially inseparable whether actions originate from self or others. In other words, our SoA or daily experiences are obtained in such as inseparable scenario. Previous research, however, has maintained the premise that self- and other-origin movements are perceptually distinguishable. Here, we implement a protocol to make these aspects ambiguous for the participants and to estimate whether they can feel SoO and/or SoA and how. To this end, we employ an experiment using virtual reality, under which participants observe virtual fingers moving very slowly (or quickly or not moving) while their own fingers do not move. For evaluation of the illusory SoO, measurements of skin conductance responses against a knife threat are adopted. Additionally, we introduce face-to-face interviews to determine whether the feelings regarding the slow movement match the conventional SoA definition. Our representative results suggest that the SoO is evoked over the hand, and various attitudes to accept its movement as the participant&#8217;s own with awareness that they did not originate it are reported by the majority. As the results show, the novelty of this protocol is discovering that in such a situation, the SoO cooperates with an externally produced SoA to establish one&#8217;s own bodily experience rather than the independence of the SoO and SoA.

While previous research on bodily self-consciousness assumed that self- and other-origin movements were perceptually distinguishable, this protocol allows them to be ambiguous on a virtual hand with unintentional slight movements. This enables us to observe one&#8217;s experience formed by SoO and other-produced SoA, rather than the absence of SoA.

The feeling that a body part is one&#8217;s own body (sense of ownership; SoO) and the feeling based on the causal relationship between one&#8217;s will ...

Motor Imagery Using VR Digital Avatars in a Brain-Computer Interface System

Motor Imagery Performance Through Embodied Digital Twins in a Virtual Reality-Enabled Brain-Computer Interface Environment

This study introduces an innovative framework for neurological rehabilitation by integrating brain-computer interfaces (BCI) and virtual reality (VR) technologies with the customization of three-dimensional (3D) avatars. Traditional approaches to rehabilitation often fail to fully engage patients, primarily due to their inability to provide a deeply immersive and interactive experience. This research endeavors to fill this gap by utilizing motor imagery (MI) techniques, where participants visualize physical movements without actual execution. This method capitalizes on the brain's neural mechanisms, activating areas involved in movement execution when imagining movements, thereby facilitating the recovery process. The integration of VR's immersive capabilities with the precision of electroencephalography (EEG) to capture and interpret brain activity associated with imagined movements forms the core of this system. Digital Twins in the form of personalized 3D avatars are employed to significantly enhance the sense of immersion within the virtual environment. This heightened sense of embodiment is crucial for effective rehabilitation, aiming to bolster the connection between the patient and their virtual counterpart. By doing so, the system not only aims to improve motor imagery performance but also seeks to provide a more engaging and efficacious rehabilitation experience. Through the real-time application of BCI, the system allows for the direct translation of imagined movements into virtual actions performed by the 3D avatar, offering immediate feedback to the user. This feedback loop is essential for reinforcing the neural pathways involved in motor control and recovery. The ultimate goal of the developed system is to significantly enhance the effectiveness of motor imagery exercises by making them more interactive and responsive to the user's cognitive processes, thereby paving a new path in the field of neurological rehabilitation.

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality

Motor imagery in a virtual reality environment has wide applications in brain-computer interface systems. This manuscript outlines the use of personalized digital avatars that resemble the participants performing movements imagined by the participant in a virtual reality environment to enhance immersion and a sense of body ownership.

This study introduces an innovative framework for neurological rehabilitation by integrating brain-computer interfaces (BCI) and virtual reality (VR) ...

Enhancing Stroke Rehabilitation with Virtual Reality-Based Occupational Training

Cognitive Function and Upper Limb Rehabilitation Training Post-Stroke Using a Digital Occupational Training System

Stroke rehabilitation often requires frequent and intensive therapy to improve functional recovery. Virtual reality (VR) technology has shown the potential to meet these demands by providing engaging and motivating therapy options. The digital occupational training system is a VR application that utilizes cutting-edge technologies, including multi-touch screens, virtual reality, and human-computer interaction, to offer diverse training techniques for advanced cognitive capacity and hand-eye coordination abilities. The objective of this study was to assess the effectiveness of this program in enhancing cognitive function and upper extremity rehabilitation in stroke patients. The training and assessment consist of five cognitive modules covering perception, attention, memory, logical reasoning, and calculation, along with hand-eye coordination training. This research indicates that after eight weeks of training, the digital occupational training system can significantly improve cognitive function, daily living skills, attention, and self-care abilities in stroke patients. This software can be employed as a time-saving and clinically effective rehabilitation aid to complement traditional one-on-one occupational therapy sessions. In summary, the digital occupational training system shows promise and offers potential financial benefits as a tool to support the functional recovery of stroke patients.

Author Spotlight: Rehabilitation of Stroke Patients With a Digital Occupational Training System

The current protocol outlines how the VR-based digital occupational training system enhances the rehabilitation of patients with cognitive impairment and upper limb dysfunction following a stroke.

Stroke rehabilitation often requires frequent and intensive therapy to improve functional recovery. Virtual reality (VR) technology has shown the ...

Medicine

Integrated Robotic and Conventional Therapy for Post-Stroke Upper Limb Recovery

Enhancing Upper Limb Function and Motor Skills Post-Stroke Through an Upper Limb Rehabilitation Robot

Cerebrovascular accidents, commonly known as strokes, represent a prevalent neurological event leading to significant upper limb disabilities, thereby profoundly affecting individuals' activities of daily living and diminishing their quality of life. Traditional rehabilitation methods for upper limb recovery post-stroke are often hindered by limitations, including therapist and patient fatigue, reliance on singular training methodologies, and lack of sustained motivation. Addressing these challenges, this study introduces an upper limb rehabilitation robot, which uses intelligent feedback motion control to improve therapeutic outcomes. The system is distinguished by its capability to adjust the direction and magnitude of force feedback dynamically, based on the detection of spastic movements during exercises, thereby offering a tailored therapeutic experience. This system is equipped with four distinct training modes, intelligent assessment of joint range of motion, and the ability to personalize training programs. Moreover, it provides an immersive interactive gaming experience coupled with comprehensive safety measures. This multifaceted approach not only elevates the engagement and interest of participants beyond traditional rehabilitation protocols but also demonstrates significant improvements in upper limb functionality and the activities of daily living among hemiplegic patients. The system exemplifies an advanced tool in upper limb rehabilitation, offering a synergistic blend of precision, personalization, and interactive engagement, thereby broadening the therapeutic options available to stroke survivors.

Author Spotlight: Enhancing Post-Stroke Upper Limb Rehabilitation with Robotic Technologies for Improved Motor Recovery and Functional Outcomes

This protocol describes an upper limb rehabilitation robot that provides intelligent feedback through four modes. These modes enhance upper limb function and flexibility, thereby improving patients' quality of life.

Cerebrovascular accidents, commonly known as strokes, represent a prevalent neurological event leading to significant upper limb disabilities, thereby ...