AK wishes to thank Gerry Loeb for useful discussions about the proposed Turing-like handshake test. AK and IN wish to thank Nathaniel Leibowitz and Lior Botzer who contributed to the design of the very first version of this protocol back in 2007. This research was supported by the Israel Science Foundation (grant No. 1018/08). SL is supported by a Kreitman Foundation postdoctoral fellowship. IN is supported by the Kreitman foundation and the Clore Scholarship Program.

1. Preparing the System
<ol>
<li>Hardware requirements:
<ul>
<li>Two PHANTOM desktop robots by SensAble Technologies, Inc.</li>
<li>2 Parallel cards.</li>
<li>Minimum system requirements: Intel or AMD-based PCs; Windows 2000/XP, 250 MB of disc space.</li>
</ul>
</li>
<li>Software requirements:
<ul>
<li>Driver- 
Download drivers from the SensAble technologies website <a href="http://www.sensable.com">http://www.sensable.com</a> according to the computer operating system.</li>
<li>H3DAPI- 
Download the H3DAPI source code and install according to the instructions displayed in the installation walkthrough section in <a href="http://www.h3dapi.org/modules/mediawiki/index.php/H3DAPI_Installation">http://www.h3dapi.org/modules/mediawiki/index.php/H3DAPI_Installation</a> 
We updated the code to fit our requirements that were not met in the existing code. The updated files can be downloaded from the handshake tournament website <a href="http://www.bgu.ac.il/~akarniel/HANDSHAKE//index.html">http://www.bgu.ac.il/~akarniel/HANDSHAKE//index.html</a> 
Each file should be placed in the appropriate folder as described in the website above. 
After performing the changes, download CMake and compile the code. The compiling instructions can be found in <a href="http://www.h3dapi.org/modules/mediawiki/index.php/H3DAPI_Installation">http://www.h3dapi.org/modules/mediawiki/index.php/H3DAPI_Installation</a></li>
<li>X3D and python 
<ul>
<li>Download python 2.5 from <a href="http://www.python.org/download/releases/2.5.5/">http://www.python.org/download/releases/2.5.5/</a></li>
<li>Download the python and x3d codes from the handshake website and place the code files in a dedicated directory, for instance: "C:\codeDirectory"</li>
<li>Each handshake force model should be written in a separate python file. See Figure 1 for an example of a spring force model.</li>
</ul>
</li>
<li>psignifit toolbox version 2.5.6 for Matlab, available at <a href="http://www.bootstrap-software.org/psignifit/">http://www.bootstrap-software.org/psignifit/</a></li>
</ul>
</li>
</ol>
<img src="/files/ftp_upload/2492/2492fig1.jpg" alt="Figure 1" /> 
Figure 1. Force function in python. An example of a spring force model for a handshake

2. Experimental Protocol
<ul>
<li>Open the command window and change to the python and x3d code directory by typing: cd C:\codeDirectory.</li>
<li>Create a folder with the subjects' names in C:\codesDirectory.</li>
<li>In order to run the experiment type: h3dload code_name.x3d.</li>
<li>Create a new random file by typing: random_file_name.txt. The random file defines the order in which the different handshakes appear.</li>
<li>Enter the subjects' names exactly as in the previously created folder.</li>
<li>Following the original concept of the classical Turing test, each experiment consists of 3 entities: a human, a computer, and an interrogator. Two subjects (human and interrogator) each hold the stylus of one Phantom haptic device and generate handshake movements. They are asked to follow the instructions that appear on the screen (e.g., :"press Page Up for the first handshake"), for handshake forces to be applied. In all of the following methods, each trial consists of 2 handshakes, and the subjects are required to compare between them. The computer is a simulated handshake model which generates a force signal as a function of time and the one dimensional hand position and its derivatives. 
(1)Fmodel(t) = &Phi;[x(t),t] 0 &le; t &le; T 
F[x,t] stands for any causal operator, e.g., non-linear time-varying mechanical model of the one dimensional stylus movement, and T is the duration of the handshake. In the current study T=5 seconds.</li>
</ul>
<ol>
<li>Conducting the "pure" test and calculating the model human likeness grade MHLGp 

The experiment starts with 12 practice trials in which all the handshakes (n=24) are human, such that the subjects simply shake hands with each other through the telerobotic system. The purpose of these practice trials is to enable the participants to be acquainted with a human handshake in the system. 
In the experiment, we compare four computer models. Each experimental block consists of 4 trials in which we compare the 4 tested models to a human handshake. One of the handshakes in each trial is an interaction with a force generated from one of the four models (a computer), and the other is with a human (the second subject). Therefore, the subjects function as both humans and interrogators. The order of the trials within each block is random and predetermined. Each experiment consists of 10 blocks, such that each computer handshake is repeated 10 times. An initial unanalyzed block is added for general acquaintance with the system and the task. 
For each model, the proportion of handshakes in which the subject chooses the model handshake over the human handshake as more human-like is calculated, to provide a value which is 0.5 when the model is indistinguishable from a human. We multiply this value by two in order to obtain the MHLGp, such that MHLG=0 is clearly non-human like and MHLG=1 means that the tested handshake is indistinguishable from the human handshake.</li>
<li>Conducting the "weighted human-model" test and calculating the model human likeness grade MHLGw 

In this protocol, there is only one interrogator subject. The other subject functions as the human entity in the handshakes. 
The experiment starts with 30 practice trials in which the interrogator experiences one human handshake and one computer handshake in each trial. In the end of the trial they are asked to choose which of the 2 handshakes was the human handshake. If they succeed, the screen displays "Correct!", and if they didn't choose the right handshake, a "Wrong!" message appears. 
After the practice block the experiment is conducted as follows: A trial consists of two handshakes. In one of the handshakes- the stimulus - the interrogator interacts with a combination of forces that comes from the human and a computer handshake model. 
(2) F = &alpha;stimulus&#8226; Fhuman + (1-&alpha;stimulus)&#8226;FstimulusModel 
&alpha;stimulus is equally distributed from 0 to 1, e.g: 
&alpha;stimulus= {0, 0.142, 0.284, 0.426, 0.568, 0.710, 0.852, 1} 
The other handshake - the reference - is a fixed combination of forces generated from the human and a reference model: 
(3) F = &alpha;reference&#8226; Fhuman + (1-&alpha;reference)&#8226;FreferenceModel ; &alpha;reference=0.5 

 At the end of each trial the interrogator is requested to choose the handshake that felt more human-like. 
In each experiment we compare two test models and one base model. 
Each experimental block consists of 24 trials comprising each of the linear combinations of the stimulus and the human (eq. 2) for each of the 3 model combinations: 
<img src="/files/ftp_upload/2492/2492model1.jpg" alt="Model 1" /> 
The order of the trials within each block is random and predetermined. Each experiment consists of 10 blocks, such that each combination is repeated 10 times. An initial unanalyzed block is added for general acquaintance with the system and the task. 
We fit a logistic psychometric function1 to the answers of the interrogator using the psignifit toolbox version 2.5.6 for Matlab, available at <a href="http://www.bootstrap-software.org/psignifit/">http://www.bootstrap-software.org/psignifit/</a> , with a constrained maximum likelihood method for estimation of the parameters, and find confidence intervals by the bias-corrected and accelerated (BCa) bootstrap method. The curve displays the probability of the interrogator to answer that a stimulus handshake is more human-like, as a function of &alpha;stimulus - &alpha;reference. The point of subjective equality (PSE) is extracted from the 0.5 threshold level of the psychometric curve, indicating the difference between the &alpha;stimulus and &alpha;reference for which the handshakes are perceived to be equally human like. The PSE is used for calculating the MHLGw according to: 
(4) MHLGw =0.5-PSE 
A model which is perceived to be as human-like as the reference model yields the MHLGw value 0.5. The models that are perceived as the least or the most human-like possible, yield MHLGw values of 0 or 1, respectively.</li>

<li>Conducting the "added noise" test and calculating the model human likeness grade MHLGn 

Similarly to the weighted model-human test, there is only one interrogator subject. The other subject functions as the human entity in the handshakes. The practice block is also the same as in the previous method. 
After the practice, in one of the 2 handshakes - the stimulus - the interrogator interacts with a computer handshake model. 
The other handshake - the reference - is a force generated from a combination of the human and white noise with a frequency range filtered according to the frequencies that appear in the human handshake. 
(5) F = &alpha; &#8226; Fhuman + (1-&alpha;)&#8226;Fnoise; 
&alpha; is equally distributed from 0 to 1, e.g: 
&alpha; = {0, 0.142, 0.284, 0.426, 0.568, 0.710, 0.852, 1} 

 At the end of each trial the interrogator is requested to choose the handshake that felt more human-like. 
The experiment is built in the same way the weighted model-human test mentioned above is built. However, while the calibration in the weighted human-model test is performed by comparing different combinations of a base model to a fix combination of itself, in this experiment the base model is substitute by the noise. 
The PSE is extracted from the psychometric curve and defines the MHLGn 

(6) MHLGn = 1-PSE 
The models that are perceived as the least or the most human-like possible, yield MHLGn values of 0 or 1, respectively.</li>
</ol>
3. Representative Results: 
Figure 2 demonstrates the results of one subject for each of the 3 methods. The tested models in all three experiments are 2 viscoelastic models- KB1: spring K=50 N/m, damper B=2 Ns/m; KB2: spring K=20 N/m, damper B=1.3 Ns/m. In the weighted model-human test, the MHLGw is evaluated by comparing each of the tested models to the elastic base model K=50 N/m.
<img src="/files/ftp_upload/2492/2492fig2.jpg" alt="Figure 2" /> 
Figure 2. The MHLG values of two viscoelastic models according to the "pure" test protocol (a), the "weighted model-human protocol" (b) and the "added noise" protocol (c). The error bars in (b) and (c) represent the psychometric curves' confidence intervals. The black bars represent the MHLG grades for the models, and the gray bars represent those of the base model in (b) and the noise in (c).
The results demonstrate that the viscoelastic model KB2 is perceived as more human like than the other viscoelastic model KB1 using all three evaluation methods.

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No conflicts of interest declared.

We have presented a new protocol for a forced-choice Turing-like handshake test administered via a simple telerobotic system. This protocol is a platform for comparing artificial handshake models, rather than a platform for determining absolute human likeness. This protocol was presented in a few conferences2-5
We have shown here that this test is helpful in finding the parameters of the passive characteristics of motion that provide the most human-like feeling. It can be used in further studies in order to develop a model for a handshake which will be as human-like as possible. We will employ this platform in the first Turing-like handshake tournament which will take place in summer 2011 [[<a href="http://www.bgu.ac.il/~akarniel/HANDSHAKE/index.html">http://www.bgu.ac.il/~akarniel/HANDSHAKE/index.html</a>]], where competing models will be graded for their human likeness. The ultimate model should probably consider the nonlinearities and time-varying nature of human impedance21, mutual adaptation with the interrogator and many other aspects of a natural human handshake which should be tested and ranked using this forced-choice Turing-like handshake test.
The proposed test is one dimensional and performed via a telerobotic interface, and therefore is limited: it hides many aspects of the handshake such as tactile information, temperature, moisture, and grasping forces. Nevertheless, in several studies a telerobotic interface was used for exploring handshakes6-11and other forms of human-human interaction12. In addition, in this version of the test, we didn't consider the duration of the handshake, the initiation and release times, its multi-dimensional nature and the hand trajectories before and after the physical contact. There are also many types of handshakes depending on gender and culture of the person13-14 and therefore, one cannot expect to generate a single optimal human-like handshake model. Nevertheless, we believe that the simplicity of the proposed test is an advantage, at least at this preliminary stage of the study. Once the key features of such one dimensional handshake are properly characterized we can move on to consider these limitations and extend the test accordingly.
It should be noted that a Turing-like handshake test could be reversed, with the computer instead of the person being asked about the identity of the other party. In this framework, we consider the following reverse handshake hypothesis: the purpose of a handshake is to probe the shaken hand; according to the reverse handshake hypothesis, the optimal handshake algorithm - in the sense that it will be indistinguishable from a human handshake - will best facilitate the discrimination between people and machines. In other words, the model will yield the best handshake such that an appropriately tuned classifier can distinguish between human and machine handshakes.
If the reverse handshake hypothesis is indeed correct it yields a clinical application for our test: identifying motor impairments in people suffering from various neurological motor-related disorders, such as Cerebral Palsy (CP). Previous studies have shown differences in the kinematic parameters between CP patients and healthy subjects when performing reaching movements15-16. We recently showed that the characteristics of movements differ between healthy individuals and individuals with CP when shaking hands through a telerobotic system4. These findings strengthen our claim that people with motor impairments can be distinguished from healthy people by examining and exploring the handshake movement of each individual. One should also note that the test discussed herein is a perceptual test and recent studies distinguish between perception and action17-20. Future studies should explore three versions of the test in order to accurately assess the nature of the human-like handshake: (1) a psychometric test of the perceived similarity; (2) a motor behavior test (motormetric test) that will explore the motor reaction of the interrogator which may differ from his/her cognitively perceived similarity; (3) an ultimate optimal discriminator which attempts to distinguish between human and machine handshakes based on the force and position trajectories. 
In general terms, we assert that understanding the motor control system is a necessary condition for understanding the brain function, and that such understanding could be demonstrated by building a humanoid robot indistinguishable from a human. The current study focuses on handshakes via a telerobotic system. We assert that by ranking the prevailing scientific hypotheses about the nature of human hand movement control using the proposed Turing-like handshake test, we should be able to extract salient properties of human motor control or at least the salient properties required to build an artificial appendage that is indistinguishable from a human arm.

<table><tbody><tr><td>Two PHANTOM desktop robots</td><td>SensAble, Geomagic</td><td></td><td>2 Parallel cards&lt;br/&gt;Minimum system requirements: Intel or AMD-based PCs; Windows 2000/XP, 250 MB of disc space</td></tr><tr><td>SensAble technologies Drivers</td><td>SensAble, Geomagic</td><td></td><td>&lt;a href=&quot;http://www.sensable.com&quot;&gt;http://www.sensable.com&lt;/a&gt;</td></tr><tr><td>H3DAPI source code</td><td>H3DAPI</td><td></td><td>&lt;a href=&quot;http://www.h3dapi.org/modules/mediawiki/index.php/H3DAPI_Installation&quot;&gt;http://www.h3dapi.org/modules/mediawiki/index.php/H3DAPI_Installation&lt;/a&gt;</td></tr><tr><td>Python 2.5</td><td>Python Products</td><td></td><td>&lt;a href=&quot;http://www.python.org/download/releases/2.5.5/&quot;&gt;http://www.python.org/download/releases/2.5.5/&lt;/a&gt;</td></tr><tr><td>x3d codes</td><td></td><td></td><td></td></tr><tr><td>psignifit toolbox version 2.5.6</td><td>Matlab</td><td></td><td>&lt;a href=&quot;http://www.bootstrap-software.org/psignifit/&quot;&gt;http://www.bootstrap-software.org/psignifit/&lt;/a&gt;</td></tr></tbody></table>

one dimensional turing-like handshake test for motor intelligence

In the Turing test, a computer model is deemed to "think intelligently" if it can generate answers that are not distinguishable from those of a human. However, this test is limited to the linguistic aspects of machine intelligence. A salient function of the brain is the control of movement, and the movement of the human hand is a sophisticated demonstration of this function. Therefore, we propose a Turing-like handshake test, for machine motor intelligence. We administer the test through a telerobotic system in which the interrogator is engaged in a task of holding a robotic stylus and interacting with another party (human or artificial). Instead of asking the interrogator whether the other party is a person or a computer program, we employ a two-alternative forced choice method and ask which of two systems is more human-like. We extract a quantitative grade for each model according to its resemblance to the human handshake motion and name it "Model Human-Likeness Grade" (MHLG). We present three methods to estimate the MHLG. (i) By calculating the proportion of subjects' answers that the model is more human-like than the human; (ii) By comparing two weighted sums of human and model handshakes we fit a psychometric curve and extract the point of subjective equality (PSE); (iii) By comparing a given model with a weighted sum of human and random signal, we fit a psychometric curve to the answers of the interrogator and extract the PSE for the weight of the human in the weighted sum. Altogether, we provide a protocol to test computational models of the human handshake. We believe that building a model is a necessary step in understanding any phenomenon and, in this case, in understanding the neural mechanisms responsible for the generation of the human handshake.

Watch this Scientific Journal Video about One Dimensional Turing-Like Handshake Test for Motor Intelligence at JoVE.com

One Dimensional Turing-Like Handshake Test for Motor Intelligence

We present a Turing-like Handshake test administered through a telerobotic system in which the interrogator is holding a robotic stylus and interacting with another party (human or artificial). We use a forced choice method, and extract a measure for the similarity of the artificial model to a human handshake.

In the Turing test, a computer model is deemed to "think intelligently" if it can generate answers that are not distinguishable from those of a human.  ...

one-dimensional-turing-like-handshake-test-for-motor-intelligence

Universidad de Cartagena UDEC

Research

JoVE Journal

Neuroscience

26.7K Views.  Ben-Gurion University. We present a Turing-like Handshake test administered through a telerobotic system in which the interrogator is holding a robotic stylus and interacting with another party (human or artificial). We use a forced choice method, and extract a measure for the similarity of the artificial model to a human handshake.

Video: One Dimensional Turing-Like Handshake Test for Motor Intelligence

The Vermicelli and Capellini Handling Tests: Simple quantitative measures of dexterous forepaw function in rats and mice

Previous characterizations of rodent eating behavior have revealed that they use coordinated forepaw movements to manipulate food pieces. We have extended upon this work to develop a simple quantitative measure of forepaw dexterity that is sensitive to lateralized impairments and age-dependent changes. Rodents learn skillful forepaw and digit movements to manage thin pasta pieces, which they eagerly consume. We have previously described methods for quantifying vermicelli handling in rats and showed that the measures are very sensitive to forelimb impairments resulting from unilateral ischemic lesions, middle cerebral artery occlusions and unilateral striatal dopamine depletion [Allred, R.P., Adkins, D.L., Woodlee, M.T., Husbands, L.C., Maldonado M.A., Kane, J.R., Schallert, T. &amp; Jones, T.A. The Vermicelli Handling Test: a simple quantitative measure of dexterous forepaw function in rats. J. Neurosci. Methods 170, 229-244 (2008)]. Here we present a more detailed protocol for this test in rats and compare it with a newly developed version for mice, the Capellini Handling Test. Rats and mice are videotaped while handling short lengths of uncooked vermicelli or capellini pasta, respectively, with a camera positioned to optimize the view of paw movements. Slow motion video playback allows for the identification of forepaw adjustments, defined as any distinct removal and replacement of the paw, or of any number of digits, on the pasta piece after eating commences. Forepaw adjustments per piece are averaged over trials per each testing session. Repeated testing permits sensitive quantitative analysis of changes in forepaw dexterity over time. Protocols for pre-testing habituation and handling practice, as well as procedures for characterizing atypical handling patterns, are described. Because rats and mice perform the pasta handling tests slightly differently, species-specific differences in administration and scoring of these tests are highlighted. All animal use was in accordance with protocols approved by the University of Texas at Austin Animal Care and Use Committee.

The Vermicelli and Capellini Handling Tests of forepaw dexterity take advantage of the natural inclination of rodents to manipulate food items using skillful forepaw and digit movements. Animals are videotaped while handling short strands of uncooked dry pasta. Slow motion video playback allows for the quantification of forepaw adjustments.

Previous characterizations of rodent eating behavior have revealed that they use coordinated forepaw movements to manipulate food pieces. We have extended ...

Measuring Motor Coordination in Mice

Mice are increasingly being used in behavioral neuroscience, largely replacing rats as the behaviorist's animal of choice. Before aspects of behavior such as emotionality or cognition can be assessed, however, it is vital to determine whether the motor capabilities of e.g. a mutant or lesioned mouse allow such an assessment. Performance on a maze task requiring strength and coordination, such as the Morris water maze, might well be impaired in a mouse by motor, rather than cognitive, impairments, so it is essential to selectively dissect the latter from the former. For example, sensorimotor impairments caused by NMDA antagonists have been shown to impair water maze performance2. 
Motor coordination has traditionally been assessed in mice and rats by the rotarod test, in which the animal is placed on a horizontal rod that rotates about its long axis; the animal must walk forwards to remain upright and not fall off. Both set speed and accelerating versions of the rotarod are available.
The other three tests described in this article (horizontal bar, static rods and parallel bars) all measure coordination on static apparatus. The horizontal bar also requires strength for adequate performance, particularly of the forelimbs as the mouse initially grips the bar just with the front paws. 
Adult rats do not perform well on tests such as the static rods and parallel bars (personal observations); they appear less well coordinated than mice. I have only tested male rats, however, and male mice seem generally less well coordinated than females. Mice appear to have a higher strength:weight ratio than rats; the Latin name, Mus musculus, seems entirely appropriate. The rotarod, the variations of the foot fault test12 or the Catwalk (Noldus)15 apparatus are generally used to assess motor coordination in rats.

Protocols are presented for two established motor coordination tasks, the accelerating rotarod and horizontal bar, also two tests developed in Oxford recently, the static rods and parallel bars. These tests can detect motor impairments potentially of interest in their own right, as well as being possible variables in tests of other areas of behavior.

Mice are increasingly being used in behavioral neuroscience, largely replacing rats as the behaviorist's animal of choice. Before aspects of behavior such ...

Behavioral Assessment of Manual Dexterity in Non-Human Primates

The corticospinal (CS) tract is the anatomical support of the exquisite motor ability to skillfully manipulate small objects, a prerogative mainly of primates1. In case of lesion affecting the CS projection system at its origin (lesion of motor cortical areas) or along its trajectory (cervical cord lesion), there is a dramatic loss of manual dexterity (hand paralysis), as seen in some tetraplegic or hemiplegic patients. Although there is some spontaneous functional recovery after such lesion, it remains very limited in the adult. Various therapeutic strategies are presently proposed (e.g. cell therapy, neutralization of inhibitory axonal growth molecules, application of growth factors, etc), which are mostly developed in rodents. However, before clinical application, it is often recommended to test the feasibility, efficacy, and security of the treatment in non-human primates. This is especially true when the goal is to restore manual dexterity after a lesion of the central nervous system, as the organization of the motor system of rodents is different from that of primates1,2. Macaque monkeys are illustrated here as a suitable behavioral model to quantify manual dexterity in primates, to reflect the deficits resulting from lesion of the motor cortex or cervical cord for instance, measure the extent of spontaneous functional recovery and, when a treatment is applied, evaluate how much it can enhance the functional recovery.
The behavioral assessment of manual dexterity is based on four distinct, complementary, reach and grasp manual tasks (use of precision grip to grasp pellets), requiring an initial training of adult macaque monkeys. The preparation of the animals is demonstrated, as well as the positioning with respect to the behavioral set-up. The performance of a typical monkey is illustrated for each task. The collection and analysis of relevant parameters reflecting precise hand manipulation, as well as the control of force, are explained and demonstrated with representative results. These data are placed then in a broader context, showing how the behavioral data can be exploited to investigate the impact of a spinal cord lesion or of a lesion of the motor cortex and to what extent a treatment may enhance the spontaneous functional recovery, by comparing different groups of monkeys (treated versus sham treated for instance). Advantages and limitations of the behavioral tests are discussed. The present behavioral approach is in line with previous reports emphasizing the pertinence of the non-human primate model in the context of nervous system diseases2,3.

As manual dexterity is a prerogative mainly of primates, behavioral tasks have been developed in macaque monkeys. Four reach and grasp prehension tasks, measuring hand manipulation ability and force, allow to establish functional recovery after a lesion of the central nervous system and to test the effect of a treatment.

The corticospinal (CS) tract is the anatomical support of the exquisite motor ability to skillfully manipulate small objects, a prerogative mainly of ...

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.

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 ...

Behavior

The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task

This protocol describes a modified serial reaction time (SRT) task used to study implicit motor sequence learning. Unlike the classic SRT task that involves finger-pressing movements while sitting, the modified SRT task requires participants to step with both feet while maintaining a standing posture. This stepping task necessitates whole body actions that impose postural challenges. The foot-stepping task complements the classic SRT task in several ways. The foot-stepping SRT task is a better proxy for the daily activities that require ongoing postural control, and thus may help us better understand sequence learning in real-life situations. In addition, response time serves as an indicator of sequence learning in the classic SRT task, but it is unclear whether response time, reaction time (RT) representing mental process, or movement time (MT) reflecting the movement itself, is a key player in motor sequence learning. The foot-stepping SRT task allows researchers to disentangle response time into RT and MT, which may clarify how motor planning and movement execution are involved in sequence learning. Lastly, postural control and cognition are interactively related, but little is known about how postural control interacts with learning motor sequences. With a motion capture system, the movement of the whole body (e.g., the center of mass (COM)) can be recorded. Such measures allow us to reveal the dynamic processes underlying discrete responses measured by RT and MT, and may aid in elucidating the relationship between postural control and the explicit and implicit processes involved in sequence learning. Details of the experimental set-up, procedure, and data processing are described. The representative data are adopted from one of our previous studies. Results are related to response time, RT, and MT, as well as the relationship between the anticipatory postural response and the explicit processes involved in implicit motor sequence learning.

We introduce the foot-stepping serial reaction time (SRT) task. This modified SRT task, complementing the classic SRT task that involves only finger-pressing movement, better approximates daily sequenced activities and allows researchers to study the dynamic processes underlying discrete response measures and disentangle the explicit process operating in implicit sequence learning.

This protocol describes a modified serial reaction time (SRT) task used to study implicit motor sequence learning. Unlike the classic SRT task that ...

Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses

This work describes a methodological framework that can be used to explicitly and implicitly characterize the sense of agency developed over the neural-machine interface (NMI) control of sensate virtual or robotic prosthetic hands. The formation of agency is fundamental in distinguishing the actions that we perform with our limbs as being our own. By striving to incorporate advanced upper-limb prostheses into these same perceptual mechanisms, we can begin to integrate an artificial limb more closely into the user&#39;s existing cognitive framework for limb control. This has important implications in promoting user acceptance, use, and effective control of advanced upper-limb prostheses. In this protocol, participants control a virtual prosthetic hand and receive kinesthetic sensory feedback through their preexisting NMIs. A series of virtual grasping tasks are performed and perturbations are systematically introduced to the kinesthetic feedback and virtual hand movements. Two separate measures of agency are employed: established psychophysical questionnaires (to capture the explicit experience of agency) and a time interval estimate task to capture the implicit sense of agency (intentional binding). Results of this protocol (questionnaire scores and time interval estimates) can be analyzed to quantify the extent of agency formation.

Here we present a protocol which characterizes the sense of agency developed over the control of sensate virtual or robotic prosthetic hands. Psychophysical questionnaires are employed to capture the explicit experience of agency, and time interval estimates (intentional binding) are employed to implicitly measure the sense of agency.

This work describes a methodological framework that can be used to explicitly and implicitly characterize the sense of agency developed over the ...

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) ...

Bioengineering

Restoring Post-Stroke Motor Function by Motor Imagery Brain-Computer Interface

Motor Imagery Brain-Computer Interface in Rehabilitation of Upper Limb Motor Dysfunction After Stroke

The rehabilitation effect of patients with moderate or severe upper limb motor dysfunction after stroke is poor, which has been the focus of research owing to the difficulties encountered. Brain-computer interface (BCI) represents a hot frontier technology in brain neuroscience research. It refers to the direct conversion of the sensory perception, imagery, cognition, and thinking of users or subjects into actions, without reliance on peripheral nerves or muscles, to establish direct communication and control channels between the brain and external devices. Motor imagery brain-computer interface (MI-BCI) is the most common clinical application of rehabilitation as a non-invasive means of rehabilitation. Previous clinical studies have confirmed that MI-BCI positively improves motor dysfunction in patients after stroke. However, there is a lack of clinical operation demonstration. To that end, this study describes in detail the treatment of MI-BCI for patients with moderate and severe upper limb dysfunction after stroke and shows the intervention effect of MI-BCI through clinical function evaluation and brain function evaluation results, thereby providing ideas and references for clinical rehabilitation application and mechanism research.

Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients

The purpose of this study is to provide an important reference for the standard clinical operation of motor imagery brain-computer interface (MI-BCI) for upper limb motor dysfunction after stroke.

The rehabilitation effect of patients with moderate or severe upper limb motor dysfunction after stroke is poor, which has been the focus of research ...

Motor Exam I

Source:Tracey A. Milligan, MD; Tamara B. Kaplan, MD; Neurology, Brigham and Women&#39;s/Massachusetts General Hospital, Boston, Massachusetts, USA
Abnormalities in the motor function are associated with a wide range of diseases, from movement disorders and myopathies to strokes. The motor assessment starts with observation of the patient. When the patient enters the examination area, the clinician observes the patient&#39;s ability to walk unassisted and the speed and coordination while moving. Taking the patient&#39;s history provides an additional opportunity to observe for evidence of tremors or other abnormal movements, such as chorea or tardive dyskinesia. Such simple but important observations can yield valuable clues to the diagnosis and help to focus the rest of the examination. The motor assessment continues in a systematic fashion, including inspection for muscle atrophy and abnormal movements, assessment of muscle tone, muscle strength testing, and finally the examination of the muscle reflexes and coordination. The careful systematic testing of the motor system and the integration of all the findings provide insight to the level at which the motor pathway is affected, and also help the clinician to formulate the differential diagnosis and determine the course of the subsequent evaluation and treatment.

Motor Exam I: Evaluation of Muscle Tone and Strength

Source:Tracey A. Milligan, MD; Tamara B. Kaplan, MD; Neurology, Brigham and Women&#39;s/Massachusetts General Hospital, Boston, Massachusetts, USA
...

Education

JoVE Science Education

Clinical Skills

Physical Examinations III

Motor Exam II

Source:Tracey A. Milligan, MD; Tamara B. Kaplan, MD; Neurology, Brigham and Women&#39;s/Massachusetts General Hospital, Boston, Massachusetts, USA
There are two main types of reflexes that are tested on a neurological examination: stretch (or deep tendon reflexes) and superficial reflexes. A deep tendon reflex (DTR) results from the stimulation of a stretch-sensitive afferent from a neuromuscular spindle, which, via a single synapse, stimulates a motor nerve leading to a muscle contraction. DTRs are increased in chronic upper motor neuron lesions (lesions of the pyramidal tract) and decreased in lower motor neuron lesions and nerve and muscle disorders. There is a wide variation of responses and reflexes graded from 0 to 4+ (Table 1).
DTRs are commonly tested to help localize neurologic disorders. A common method of recording findings during the DTR examination is using a stick figure diagram. The DTR test can help distinguish upper and lower motor neuron problems, and can assist in localizing nerve root compression as well. Although the DTR of nearly any skeletal muscle could be tested, the reflexes that are routinely tested are: brachioradialis, biceps, triceps, patellar, and Achilles (Table 2).
Superficial reflexes are segmental reflex responses that result from stimulation of a specific sensory input (cutaneous or conjunctival) and the corresponding motor response. These reflexes include the corneal, conjunctival, abdominal, cremasteric, anal wink and plantar (Babinski) reflexes. The plantar reflex is a polysynaptic reflex elicited by stroking the lateral aspect of the sole with the normal response being plantar flexion of the great toe. This reflex changes with the normal development of the nervous system. In infants the toe will dorsiflex, but by 2 years of age the toe responds by plantar flexing. With damage to the pyramidal system, there is an unmasking of the more primitive reflex and the toe becomes &#34;upgoing&#34; or a positive Babinski sign.
The evaluations of coordination and gait are performed as a part of the neurological motor examination and can help a clinician localize lesions or recognize movement disorders. The coordination of movements and gait has complex multi-level regulation and requires an integrated function of different components of the nervous system. This part of the neurological examination allows the examiner to assess the function of the cerebellum, the cerebellar connections, and other tracts including brainstem structures. Coordination is assessed by looking for smooth and accurate movement, and requires the integration of sensory feedback with motor output, most of which occurs in the cerebellum. An impaired ability to coordinate the rate, range, timing, direction, or force of voluntary movement is called ataxia. Testing coordination includes evaluation of rapid alternating movements and point-to-point coordination, both of which can be altered as a result of cerebellar dysfunction. As in other parts of the examination, observation is the first step in the evaluation of the patient. A careful observation of a patient&#39;s gait can help the clinician screen for problems including weakness, movement disorders, spasticity, and cerebellar disease. No neurologic examination is complete without the assessment of gait. Occasionally, the only sign of a serious neurologic disorder is an impaired gait.

Motor Exam II: Reflex, Coordination, Gait and Station Testing

Source:Tracey A. Milligan, MD; Tamara B. Kaplan, MD; Neurology, Brigham and Women&#39;s/Massachusetts General Hospital, Boston, Massachusetts, USA
There ...