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Neurotraumas and neurological diseases often result in compromised proprioceptive feedback, which plays a critical role in motor control by delivering real-time position information. Electro-prosthetic proprioception (EPP) using frequency-modulated electrotactile feedback is a promising solution, as it can deliver proprioceptive information such as a joint angle via tactile channel. Prior works demonstrated that EPP successfully delivered distance information between the end effector and the target object. In this study, we implemented the electronic skin (E-skin) monitoring the elbow joint angle and delivering it to the nervous system via tactile channel. We also demonstrated that EPP improved both accuracy and precision of the elbow joint angle control. The gyroscope measuring the elbow joint angle and electrodes delivering electrotactile feedback were integrated together as a skin using thin silicon coating and polyurethane film. We call this novel E-skin, monitoring and delivering joint angle information, as an electro-prosthetic E-skin. Elbow joint angle matching test with two healthy human subjects showed that the EPP, via electro-prosthetic E-skin, enhanced 101.7% accuracy and 63.8% precision in elbow joint angle control. Clinical Relevance-Presented electro-prosthetic E-skin will address the compromised proprioceptive feedback by delivering joint angle information by electro-prosthetic proprioception (EPP) via tactile channel. This novel E-skin will open up a new path to assist and rehabilitative motor control problems after neurotraumas and neurological diseases.

Sleep in epilepsy is best studied in longitudinal preclinical animal models, where state changes can have significant effects on epileptic activities. Voluminous data makes it very difficult to mark sleep stages manually. This demands an automated way to detect sleep and wake states. We developed an approach to characterize sleep-wake states in continuous video-electroencephalography (EEG) recordings in animals. We compared brute force approach based on frequency band-power based thresholding with machine learning algorithms to detect sleep in 600 hours of EEG data from 4 epileptic and 2 control animals. We found that conventional delta and theta band-powers were prominent in sleep; however, this was not sufficient to detect sleep algorithmically. We therefore extracted a set of novel frequency bands to robustly differentiate individual sleep states by using brute-force algorithm and machine learning models, among which k-nearest neighbors (KNN) was the best predictor of sleep with 94% accuracy. We subsequently characterized sleep patterns in animals with chronically induced epileptic spiking in the neocortex from tetanus toxin injections using brute-force algorithm. We found that epileptic spiking animals (without seizures) sleep more frequently, with significantly longer sleep segments and overall daily sleep time, as compared to control animals. This automated algorithm could help expedite sleep studies and help us understand the relationship between sleep and patients with epilepsy.

Electronic skin (E-skin) is an emerging wearable device typically used to mimic the function of the human skin, mainly by replicating the role of tactile sensory receptors in the skin. This study showed an interesting modification of the E-skin, called an electro-prosthetic E-skin, which adds the functionality of distance sensing and stimulation of the palmar digital nerve. The electro-prosthetic E-skin operates as a closed loop to deliver the finger aperture distance information to the nervous system. This E-skin was implemented as an additional layer mounted to the original human skin, to be worn on the fingertip with a thin silicone substrate. The E-skin was designed to be mounted onto the index fingertip, to deliver the distance information between the fingertips and to enhance the finger aperture distance control. In this study, we demonstrated that electro-prosthetic proprioception (EPP), implemented with the electro-prosthetic E-skin, successfully delivered the distance information between the fingertips and enhanced the finger aperture distance control accuracy. Clinical Relevance- Presented electro-prosthetic E-skin delivering finger aperture distance via electro-prosthetic proprioception (EPP) will enhance accuracy of the finger aperture distance control. This technology can be applied to the neurosurgery to minimize unforced errors caused by the limited human control accuracy over the fingertip.

Stroke rehabilitation is often terminated once a plateau in motor recovery is observed, but new training modalities have demonstrated that further functional improvement is possible after the onset of the chronic phase. In particular, feedback technologies augmenting error proved to foster the relearning process. Here we explore the possibility of a robot-free implementation of Error-Augmentation (EA), where only visual feedback is distorted. We present the interim results from our ongoing blinded, randomized, controlled clinical trial testing the efficacy of parallel bimanual reaching with visual EA. Subjects trained in the virtual environment in 45-minute sessions, three times a week, for three weeks, half with and half without EA. A blinded therapist performed clinical evaluations before, 1 week after, and two months after training. Available results showed that both groups significantly improved. An advantage in the treatment group could be tracked at all time points, but no statistical significance was detectable between groups. Gains in the two groups were found to be compatible with the results of previous studies using robots and may prove to have similar effectiveness without the need for a costly and complicated robotic device. One new finding was that EA caused significantly higher inter-trial variability.

Little is known about how two people physically coupled together (a dyad) can accomplish tasks. In a pilot study we tested how healthy inexperienced and experienced dyads learn to repeatedly reach to a target and stop while challenged with a 30 degree visuomotor rotation. We employed the Pantograph investigational device that haptically couples partners movements while providing cursor feedback, and we measured the amount and speed of learning to test a prevailing hypothesis: dyads with no experience learn faster than an experienced person coupled with a novice. We found significant straightening of movements for dyads in terms of amount of learning (2.662±0.102 cm and 2.576±0.024 cm for the novice-novice and novice-experienced groups) at rapid rates (time constants of 17.83 ± 2.85 and 18.17.17±6.72 movements), which was nearly half the learning time as solo individuals' studies. However, we found no differences between the novice-novice and experienced-novice groups, though retrospectively our power was only 3 percent. This pilot study demonstrates new opportunities to investigate the advantages of partner-facilitated learning with solely haptic communication which and can lead to insights on control in human physical interactions and can guide the design of future human-robot-human interaction systems.

In dyadic motor learning, pairs of people learn the same motion while their limbs are loosely coupled together using haptic devices. Such coupled learning has been shown to outperform solo learning (including robot-guided learning) for simple one-degree-of-freedom tasks. However, results from more complex tasks are limited and sometimes conflicting. We thus evaluated coupled learning in a two-degree-of-freedom tracking task where participants also had to compensate for a simple force field. Participant pairs were split into two groups: an experiment group that experienced a compliant haptic coupling between participants' hands and a control group that did not. The study protocol consisted of 70 repetitions of 18.9-second tracking trials: 10 initial solo trials with no coupling, 50 "learning" trials (where participants in the experiment group were coupled), and 10 final solo trials with no coupling. The experiment group (coupled) improved their solo tracking performance both in the presence (p = 0.008) and absence (p <; 0.001) of the force field; however, the control group (no coupling) only improved their solo performance in the absence of the force field (p <; 0.001) but not in the presence of the field (p = 0.81). This suggests that dyadic motor learning can outperform solo learning for two-dimensional tracking motions in the presence of a simple force field, though the mechanism by which learning is improved is not yet clear.Clinical Relevance-As motor learning is critical for applications such as motor rehabilitation, dyadic training could be used to achieve a better overall outcome and a faster learning speed in these applications compared to solo training.

Muscle activity is widely measured to assess muscle condition in post-stroke patients. While many clinical researchers have relied on time-series analysis of muscle activity, the frequency domain could offer additional insight on motor impairment. Our previous work has characterized movement capabilities in stroke survivors across endpoint and joint kinematic variables while performing a self-directed motor exploration task. Our solution to managing such large volumes of data is to create personalized statistical profiles using multivariate probability distributions. In this study, we present frequency domain analysis of EMG distributions for chronic post-stroke survivors (N = 6) and healthy subjects (N = 5) to identify between group differences in muscle activity. Comparing probability density of muscle activity magnitudes, differences from healthy were most evident at 275 Hz. Unique aspects of each patient's deficits were most evident at 125 Hz. This is the first study to explore distributions of EMG in specific frequency bands for this patient population. Such identifiability could pinpoint specific motor deficits and track progress in neurologically impaired individuals that often have widely differing disease states.

This study used evidence from trial-by-trial errors to understand how humans can generalize what they learn across different movement directions while reaching. We trained 15 healthy subjects to reach in six directions in the presence of challenging visuomotor distortions. We then tested a number of candidate models suggested by the literature of how the brain might use error to improve performance. Our cross-validated results point to a discrete affine model whose generalization, or influence of practice in one direction to neighboring directions, is reduced nearly to zero by 60 degrees away, and the subjects learned 6.25 times more from the error that was observed at a movement direction than neighboring directions.

Nerve function loss can result from a variety of conditions that are either sudden onset like head and spinal cord trauma or slowly develop from chronic pressure as in the case of carpal tunnel syndrome. In either case we see compression ofthe nerve ultimately resulting in axon demyelination and loss of signal conduction. For chronic conditions such as carpal tunnel syndrome, treatments focus on alleviating symptoms. Some patients undergo surgery which can be successful in relieving pressure on the median nerve by inflamed surrounding tendons. Symptoms of classical carpal tunnel syndrome have been debated and sometimes patients experiencing similar numbness and pain in the hand do not necessarily have the underlying condition of a compressed median nerve. Therefore, better markers are needed for determining true cases of nerve compression as well as clinical measures to indicate the need for surgical treatment. We have demonstrated a correlation between clinically observed nerve compression derived from MRI slides and clinically observed increases in conduction delay. We have done this by computationally modeling a myelinated axon with various levels of compression and finding the increase in conduction delay from a normal control with no compression. We show that conduction delay measurements could be used as a clinical tool to determine the amount of nerve compression present in a patient of mild carpal tunnel syndrome although further data is required to create a fully predictive model.

Clinical investigators have asserted patients should be active participants in the therapy process in stroke rehabilitation. While robotics introduces new tools for measurement and treatment of motor impairments, it also presents challenges for evaluating how much a patient contributes to observed movements during training. Our approach employs established methods of inverse dynamics combined with measurements of human motion and interaction forces between the human and robot. Here, we investigated whether measures of patient active involvement predict the level of upper limb recovery due to robot-assisted therapy. Stroke survivors (n=11) completed "exploration" training with customizable forces that increased their velocities (i.e., negative damping). While our results showed a mild trend between mechanical work during training and expanded velocity capability (Pearson r = 0.57), we found significant correlations with the amount of positive work (i.e., propulsion; r = 0.77), but not negative work (i.e., braking; r = 0.41). This work supports robotic tools that encourage more positive work.

Manipulation of error feedback has been of great interest to recent studies in motor control and rehabilitation. Typically, motor adaptation is shown as a change in performance with a single scalar metric for each trial, yet such an approach might overlook details about how error evolves through the movement. We believe that statistical distributions of movement error through the extent of the trajectory can reveal unique patterns of adaption and possibly reveal clues to how the motor system processes information about error. This paper describes different possible ordinate domains, focusing on representations in time and state-space, used to quantify reaching errors. We hypothesized that the domain with the lowest amount of variability would lead to a predictive model of reaching error with the highest accuracy. Here we showed that errors represented in a time domain demonstrate the least variance and allow for the highest predictive model of reaching errors. These predictive models will give rise to more specialized methods of robotic feedback and improve previous techniques of error augmentation.

Human movement ability should be described not only by its typical behavior, but also by the wide variation in capabilities. This would mean that subjects that are encouraged to move throughout their workspace but otherwise free to move any way they like might reveal their unique movement tendencies. In this study, we investigate how much information (data) is needed to reliably construct a movement distribution that predicts an individual's movement tendencies. We analyzed the distributions of position, velocity and acceleration data derived during self-directed motor exploration by stroke survivors (n=10 from a previous study) and healthy individuals (n=5). We examined whether these simple kinematic variables differed in terms of the amount of data required. We found a trend of decreasing time needed for characterization with the order of kinematic variable, for position, velocity, and acceleration, respectively. In addition, we investigated whether data requirements differ between stroke survivors and healthy. Our results suggest that healthy individuals may require more data samples (time for characterization), though the trend was only significant for position data. Our results provide an important step towards using statistical distributions to describe movement tendencies. Our findings could serve as more comprehensive tools to track recovery in or design more focused training intervention in neurorehabiliation applications.

It has been widely accepted that the CNS develops a representation (model) of the environment, but what is not entirely clear is the coordinate reference frame used. We explored how visual feedback influenced the coordinate frame in which the CNS stores and recalls these memories of learned skills in a reaching-generalization task. Four groups of subjects trained to perform reaching movements in a perturbing force field, two with aligned (first person) visual feedback and two with non-aligned (vertical screen). After 170 trials of practice, we asked subjects to extrapolate (generalize) what they learned to a new part of the workspace in novel force environments (endpoint-based versus joint-based extrapolated force fields). Regardless of the test condition, all subjects improved their ability to generalize skills to the new workspace. There was evidence that the extrapolation of their learned skills was based on both object-centered and joint-based coordinates. Consistent with previous studies, subjects performed significantly better in joint-extrapolated force field, but only if the visual feedback was vertical. Subjects performed equivalently in both force fields with aligned (first person) feedback. These findings suggest that the type of visual feedback biases the way subjects perform, and that learning results can be significantly influenced by feedback.

Upper limb extremity rehabilitation practices are increasingly involving robotic interaction for repetitive practice, and there is increasing skepticism whether such systems can provide the relevant practice that can be generalized (or transferred) to functional activities in the real world. Most importantly, will patients be able to generalize in three critical ways: (1) to unpracticed directions, (2) to unpracticed movement distances, and (3) to unpracticed weight-eliminated conditions? Rather than presuming that patients could generalize in three conditions, this study tested whether there was any evidence of such generalization ability in healthy individuals. We found that there was some evidence in all conditions except for the ability of healthy subjects to generalize to large movements after practicing small. Such results suggest that larger robotic systems are advantageous for training the functional motions that can include large actions.

We devised an interactive environment in which subjects could perform simulated laparoscopic maneuvers, using either unconstrained movements or standard mechanical contact typical of a box-trainer. During training the virtual tool responded to the absolute position in space (Position-Based) or the orientation (Orientation-Based) of a hand-held sensor. Volunteers were further assigned to different sequences of target distances (Near-Far-Near or Far-Near-Far). Orientation-Based control produced much lower error and task times during training, which suggests that the motor system more easily accommodates tool use with degrees of freedom that match joint angles. When evaluated in constrained (physical box-trainer) conditions, each group exhibited improved performance from training. However, Position-Based training enabled greater reductions in movement error relative to Orientation-Based (mean -13.7%, CI:-27.1, -0.4). Furthermore, the Near-Far-Near schedule allowed a greater decrease in task time relative to the Far-Near-Far sequence (mean -13.5%, CI:-19.5, -7.5). Training at shallow insertion in virtual laparoscopy might promote more efficient movement strategies by emphasizing the curvature of tool motion. In addition, our findings suggest that an understanding of absolute tool position is critical to coping with mechanical interactions between the tool and trochar.

Recent human motor learning and neuro-rehabilitation experiments have identified the benefits of assisting the learning process by artificially enhancing the errors one might experience. A yet untested question is just how far the nervous system will trust such treatments, especially in transformations with very large sensorimotor discrepancies. Our study asked 10 healthy subjects to perform targeted reaching in a virtual reality environment, where the transformation of the hand position matrix was a complete reversal - rotated 180 degrees about an arbitrary axis (hence 2 of the 3 coordinates are reversed). Our data show that after 500 practice trials, subject who received 2x Error Augmentation (EA) were able to reach their desired target 0.4 seconds more quickly and with a Maximum Perpendicular Trajectory deviation of 0.9 cm less, when compared to the control group. Furthermore, the manner in which subjects practiced was influenced by the error augmentation, resulting in more continuous motions for this group. These data further support that this type of enhancement, as well as possibly other distorted reality methods, may promote more complete adaptation/learning when compared to regular training.

A well known and major component of movement control is the feedforward component, also known as the internal model. This model predicts and compensates for expected forces seen during a movement, based on recent experience, so that a well-learned task such as reaching to a target can be executed in a smooth straight manner. It has recently been shown that the state of preparation of planned movements can be tested using a startling acoustic stimulus (SAS). SAS, presented 500, 250 or 0 ms before the expected "go" cue resulted in the early release of the movement trajectory associated with the after-effects of the force field training (i.e. the internal model). In a typical motor adaptation experiment with a robot-applied force field, we tested if a SAS stimulus influences the size of after-effects that are typically seen. We found that in all subjects the after-effect magnitudes were significantly reduced when movements were released by SAS, although this effect was not further modulated by the timing of SAS. Reduced after-effects reveal at least partial existence of learned preparatory control, and identify startle effects that could influence performance in tasks such as piloting, teleoperation, and sports.

Recent research has suggested that enhanced retraining for stroke patients using haptics (robotic forces) and graphics (visual display) to generate a practice environment that can artificially enhance error rather than reducing it, can stimulate new learning and foster accelerated recovery. We present an evaluation of early results of this novel post-stroke robotic-aided therapy trial that incorporates these ideas in a large VR system and simultaneously employs the patient, the therapist, and the technology to accomplish effective therapy.