Key components of mechanical work predict outcomes in robotic stroke therapy.

BACKGROUND: Clinical practice typically emphasizes active involvement during therapy. However, traditional approaches can offer only general guidance on the form of involvement that would be most helpful to recovery. Beyond assisting movement, robots allow comprehensive methods for measuring practice behaviors, including the energetic input of the learner. Using data from our previous study of robot-assisted therapy, we examined how separate components of mechanical work contribute to predicting training outcomes. METHODS: Stroke survivors (n = 11) completed six sessions in two-weeks of upper extremity motor exploration (self-directed movement practice) training with customized forces, while a control group (n = 11) trained without assistance. We employed multiple regression analysis to predict patient outcomes with computed mechanical work as independent variables, including separate features for elbow versus shoulder joints, positive (concentric) and negative (eccentric), flexion and extension. RESULTS: Our analysis showed that increases in total mechanical work during therapy were positively correlated with our final outcome metric, velocity range. Further analysis revealed that greater amounts of negative work at the shoulder and positive work at the elbow as the most important predictors of recovery (using cross-validated regression, R2 = 52%). However, the work features were likely mutually correlated, suggesting a prediction model that first removed shared variance (using PCA, R2 = 65-85%). CONCLUSIONS: These results support robotic training for stroke survivors that increases energetic activity in eccentric shoulder and concentric elbow actions. TRIAL REGISTRATION: ClinicalTrials.gov, Identifier: NCT02570256. Registered 7 October 2015 - Retrospectively registered.

Movement distributions of stroke survivors exhibit distinct patterns that evolve with training.

BACKGROUND: While clinical assessments provide tools for characterizing abilities in motor-impaired individuals, concerns remain over their repeatability and reliability. Typical robot-assisted training studies focus on repetition of prescribed actions, yet such movement data provides an incomplete account of abnormal patterns of coordination. Recent studies have shown positive effects from self-directed movement, yet such a training paradigm leads to challenges in how to quantify and interpret performance. METHODS: With data from chronic stroke survivors (n = 10, practicing for 3 days), we tabulated histograms of the displacement, velocity, and acceleration for planar motion, and examined whether modeling of distributions could reveal changes in available movement patterns. We contrasted these results with scalar measures of the range of motion. We performed linear discriminant analysis (LDA) classification with selected histogram features to compare predictions versus actual subject identifiers. As a basis of comparison, we also present an age-matched control group of healthy individuals (n = 10, practicing for 1 day). RESULTS: Analysis of range of motion did not show improvement from self-directed movement training for the stroke survivors in this study. However, examination of distributions indicated that increased multivariate normal components were needed to accurately model the patterns of movement after training. Stroke survivors generally exhibited more complex distributions of motor exploration compared to the age-matched control group. Classification using linear discriminant analysis revealed that movement patterns were identifiable by individual. Individuals in the control group were more difficult to identify using classification methods, consistent with the idea that motor deficits contribute significantly to unique movement signatures. CONCLUSIONS: Distribution analysis revealed individual patterns of abnormal coordination in stroke survivors and changes in these patterns with training. These findings were not apparent from scalar metrics that simply summarized properties of motor exploration. Our results suggest new methods for characterizing motor capabilities, and could provide the basis for powerful tools for designing customized therapy.

Evidence of multiple coordinate representations during generalization of motor learning.

Several studies have suggested that the motor system takes advantage of a coordinate system when learning a novel sensorimotor environment. Such investigations, however, have not distinguished between initial preferences of a coordinate system versus possible changes due to learning. Here, we present experimental methods that specifically entertain the possibility of multiple coordinate systems during generalization. Subjects trained with their right arm on a viscous force field. We evaluated their performances for both arms in an untrained workspace before and after training using three fields, each representing extrapolation with a candidate coordinate system. Surprisingly, our results showed evidence of improvement (pre to post) in all fields for both limbs. These findings are consistent with the hypothesis of multiple, simultaneous coordinate systems involved in generalization. We also investigated how feedback might affect the results and found in several cases that performance was better for visual displays that were aligned with the limb (in first person) versus non-aligned.

Error Fields: Personalized robotic movement training that augments one's more likely mistakes.

Control of movement is learned and uses error feedback during practice to predict actions for the next movement. We have shown that augmenting error can enhance learning, but while such findings are encouraging the methods need to be refined to accommodate a person's individual reactions to error. The current study evaluates error fields (EF) method, where the interactive robot tempers its augmentation when the error is less likely. 22 healthy participants were asked to learn moving with a visual transformation, and we enhanced the training with error fields. We found that training with error fields led to greatest reduction in error. EF training reduced error 264% more than controls who practiced without error fields, but subjects learned more slowly than our previous error magnification technique. We also found a relationship between the amount of learning and how much variability was induced by the error augmentation treatments, most likely leading to better exploration and discovery of the causes of error. These robotic training enhancements should be further explored in combination to optimally leverage error statistics to teach people how to move better.

Manual skill generalization enhanced by negative viscosity.

Recent human-machine interaction studies have suggested that movement augmented with negative viscosity can enhance performance and can even promote better motor learning. To test this, we investigated how negative viscosity influences motor adaptation to an environment where forces acted only in one axis of motion. Using a force-feedback device, subjects performed free exploratory movements with a purely inertia generating forces proportional to hand acceleration, negative viscosity generating destabilizing forces proportional to hand velocity, or a combination of the acceleration and velocity fields. After training, we evaluated each subject's ability to perform circular movements in only the inertial field. Combined training resulted in lowest error and revealed similar responses as inertia training in catch trials. These findings are remarkable because negative viscosity, available only during training, evidently enhanced learning when combined with inertia. This success in generalization is consistent with the ability of the nervous system to decompose the perturbing forces into velocity and acceleration dependent components. Compared with inertia, the combined group exhibited a broader range of speeds along the direction of maximal perturbing force. Broader exploration was also correlated with better performance in subsequent evaluation trials; this suggests that negative viscosity improved performance by enhancing identification of each force field. These findings shed light on a new way to enhance sensorimotor adaptation through robot-applied augmentation of mechanics.

Combining Metrics From Clinical Simulators and Sensorimotor Tasks Can Reveal the Training Background of Surgeons.

BACKGROUND: Skill assessment in surgery traditionally has relied on the expert observation and qualitative scoring. Our novel study design demonstrates how analysis of performance in sensorimotor tasks and bench-top surgical simulators can provide inferences about the technical proficiency as well as the training history of surgeons. METHODS: We examined metrics for basic sensorimotor tasks in a virtual reality interface as well as motion metrics in clinical scenario simulations. As indicators of the training level, we considered survey responses from surgery residents, including the number of postgraduation years (PGY, four levels), research years (RY, three levels), and clinical years (CY, three levels). Next, we performed a linear discriminant analysis with cross-validation (90% training, 10% testing) to relate the training levels to the selected metrics. RESULTS: Using combined metrics from all stations, we found greater than chance predictions for each survey category, with an overall accuracy of 43.4 ± 2.9% for identifying the level for post-graduate years, 79.1 ± 1.0% accuracy for research training years, and 64.2 ± 1.0% for clinical training years. Our main finding was that combining metrics from all stations resulted in more accurate predictions than using only sensorimotor or clinical scenario tasks. In addition, we found that metrics related to the ability to cope with changes in the task environment were the most important predictors of training level. CONCLUSIONS: These results suggest that each simulator-type provided crucial information for evaluating surgical proficiency. The methods developed in this paper could improve evaluations of a surgeon's clinical proficiency as well as training potential in terms of basic sensorimotor ability.

Post-stroke motor deficits are most evident at frequencies near 125 Hz in EMG multivariate probability distributions.

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.

Energetics during robot-assisted training predicts recovery in stroke.

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.

Robot Training With Vector Fields Based on Stroke Survivors' Individual Movement Statistics.

The wide variation in upper extremity motor impairments among stroke survivors necessitates more intelligent methods of customized therapy. However, current strategies for characterizing individual motor impairments are limited by the use of traditional clinical assessments (e.g., Fugl-Meyer) and simple engineering metrics (e.g., goal-directed performance). Our overall approach is to statistically identify the range of volitional movement capabilities, and then apply a robot-applied force vector field intervention that encourages under-expressed movements. We investigated whether explorative training with such customized force fields would improve stroke survivors' (n = 11) movement patterns in comparison to a control group that trained without forces (n = 11). Force and control groups increased Fugl-Meyer UE scores (average of 1.0 and 1.1, respectively), which is not considered clinically meaningful. Interestingly, participants from both groups demonstrated dramatic increases in their range of velocity during exploration following only six days of training (average increase of 166.4% and 153.7% for the Force and Control group, respectively). While both groups showed evidence of improvement, we also found evidence that customized forces affected learning in a systematic way. When customized forces were active, we observed broader distributions of velocity that were not present in the controls. Second, we found that these changes led to specific changes in unassisted motion. In addition, while the shape of movement distributions changed significantly for both groups, detailed analysis of the velocity distributions revealed that customized forces promoted a greater proportion of favorable changes. Taken together, these results provide encouraging evidence that patient-specific force fields based on individuals' movement statistics can be used to create new movement patterns and shape them in a customized manner. To the best of our knowledge, this paper is the first to directly link engineering assessments of stroke survivors' exploration movement behaviors to the design of customized robot therapy.

Visual and haptic feedback contribute to tuning and online control during object manipulation.

The authors employed a virtual environment to investigate how humans use haptic and visual feedback in a simple, rhythmic object-manipulation task. The authors hypothesized that feedback would help participants identify the appropriate resonant frequency and perform online control adjustments. The 1st test was whether sensory feedback is needed at all; the 2nd was whether the motor system combines visual and haptic feedback to improve performance. Task performance was quantified in terms of work performed on the virtual inertia, ability to identify the correct rhythm, and variability of movement. Strict feedforward control was found to be ineffective for this task, even when participants had previous knowledge of the rhythm. Participants (N = 11) performed far better when feedback was available (11 times more work, 2.2 times more precise frequency, 30% less variability; p < .05 for all 3 performance measures). Using sensory feedback, participants were able to rapidly identify 4 different spring-inertia systems without foreknowledge of the corresponding resonant frequencies. They performed over 20% more work with 24% less variability when provided with both visual and haptic feedback than they did with either feedback channel alone (p < .05), providing evidence that they integrated online sensory channels. Whereas feedforward control alone led to poor performance, feedback control led to fast tuning or calibration of control according to the resonant frequency of the object, and to better control of the rhythmic movement itself.

Simulation of variable impedance as an intervention for upper extremity motor exploration.

Current methods in robot-assisted therapy are limited in providing predictions of the effectiveness of interventions. Our approach focuses on how robotic interaction can impact the distribution of movements expressed in the arm. Using data from a previous study with stroke survivors (n=10), we performed simulations to examine how changes in hand endpoint impedance would alter exploratory motion. We present methods for designing a custom training intervention, by relating the desired change in acceleration covariance in planar motion with a corresponding change in inertia matrix. We first characterized motor exploration in terms of overall covariance in acceleration, and secondly as covariance that varies with position in the workspace. Using a forward dynamics simulation of the hand endpoint impedance, we found that the variable change in endpoint inertia resulted in better recovery of acceleration covariance compared to the fixed change in inertia method. These results could significantly impact rehabilitation firstly in terms of design principles for altering coordination patterns through direct assistance. Furthermore, our work might serve to improve therapy by facilitating access to repeated practice of independent joint motion.

Customized therapy using distributions of reaching errors.

While there has been recent success with robotic therapy approaches, individual differences in motor impairments motivate the need for customized therapy. Our latest work with healthy participants considered the likelihood of one's error to construct a customized force field training environment, which we termed an error field. We believe error statistics could characterize individual motor impairments for stroke survivors. Here we present preliminary results from a pilot study testing this therapy technique on individuals following stroke. We tracked the changes in error for three stroke survivors across multiple days using error field training, and found that participants' errors reduced for all target directions across sessions. We also used a modeling approach to test whether the changes in error reflected the specific mathematical structure of the intervention. These results provide encouraging preliminary evidence that error field training can be valuable for both characterizing deficits and custom-tailoring therapy.

Movement augmentation to evaluate human control of locomotor stability.

Controlling center of mass (COM) position and velocity within a dynamic base of support is essential for gait stability. This skill is often compromised following neurologic injury, creating a need to develop effective interventions to enhance gait stability. A movement augmentation paradigm applied to walking could potentially be used to improve control of COM dynamics. We have developed a cable robot system, the Agility Trainer, to apply continuous frontal-plane forces to the pelvis during treadmill walking. This cable robot system uses a set of series elastic actuators powered by linear motors to create bilateral forces. Here we use the Agility Trainer to create a negative viscosity force field proportional to the subject's lateral velocity. Two healthy young subjects performed two 10-minute walking trials, Baseline and Negative Viscosity. During the first minute of walking in the Negative Viscosity field, participants' lateral COM motion became less controlled when compared to the rhythmic sinusoidal motion observed during Baseline walking. By the 10th minute of walking in the Negative Viscosity field the participants had adapted their gait patterns, decreasing their variation in peak lateral COM speed each stride. These results demonstrate that it is feasible to use the Agility Trainer to apply a movement augmentation paradigm to human walking.

Human adaptation to interaction forces in visuo-motor coordination.

We tested whether humans can learn to sense and compensate for interaction forces in contact tasks. Many tasks, such as use of hand tools, involve significant interaction forces between hand and environment. One control strategy would be to use high hand impedance to reduce sensitivity to these forces. But an alternative would be to learn feedback compensation for the extrinsic dynamics and associated interaction forces, with the potential for lower control effort. We observed subjects as they learned control of a ball-and-beam system, a visuo-motor task where the goal was to quickly position a ball rolling atop a rotating beam, through manual rotation of the beam alone. We devised a ball-and-beam apparatus that could be operated in a real mode, where a physical ball was present; or in a virtual training mode, where the ball's dynamics were simulated in real time. The apparatus presented the same visual feedback in all cases, and optionally produced haptic feedback of the interaction forces associated with the ball's motion. Two healthy adult subject groups, vision-only and vision-haptics (each n= 10), both trained for 80 trials on the simulated system, and then were evaluated on the real system to test for skill transfer effects. If humans incorporate interaction forces in their learning, the vision-haptics group would be expected to exhibit a smoother transfer, as quantified by changes in completion time of a ball-positioning task. During training, both groups adapted well to the task, with reductions of 64%-70% in completion time. At skill transfer to the real system, the vision-only group had a significant 35% increase in completion time (p < 0.05). There was no significant change in the vision-haptics group, indicating that subjects had learned to compensate for interaction forces. These forces could potentially be incorporated in virtual environments to assist with motor training or rehabilitation.

Distributions in the error space: goal-directed movements described in time and state-space representations.

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.

Data sample size needed for prediction of movement distributions.

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.

Augmented dynamics and motor exploration as training for stroke.

With chronic stroke survivors (n = 30), we investigated how upper extremity training with negative viscosity affects coordination under unperturbed conditions. Subjects trained with a planar robotic interface simulating 1) negative viscosity augmented to elbow and shoulder joints; 2) negative viscosity combined with inertia; or 3) a null-field condition. Two treatment groups practiced with both force conditions (cross-over design), while a control group practiced with a null-field condition. Training (exploratory movement) and evaluations (prescribed circular movement) alternated in several phases to facilitate transfer from forces to the null field. Negative viscosity expanded exploration especially in the sagittal axis, and resulted in significant within-day improvements. Both treatment groups exhibited next day retention unobserved in the control. Our results suggest enhanced learning from forces that induce a broader range of kinematics. This study supports the use of robot-assisted training that encourages active patient involvement by preserving efferent commands for driving movement.

Individual patterns of motor deficits evident in movement distribution analysis.

Recent studies in rehabilitation have shown potential benefits of patient-initiated exploratory practice. Such findings, however, lead to new challenges in how to quantify and interpret movement patterns. We posit that changes in coordination are most evident in statistical distributions of movements. In a test on 10 chronic stroke subjects practicing for 3 days, we found that inter-quartile range of motion did not show improvement. However, a multivariate Gaussians analysis required more complexity at the end of training. Beyond simply characterizing movement, linear discriminant classification of each patient's movement distribution also identified that each patient's motor deficit left a unique signature. The greatest distinctions were observed in the space of accelerations (rather than position or velocity). These results suggest that unique deficits are best detected with such a distribution analysis, and also point to the need for customized interventions that consider such patient-specific motor deficits.

Learning Kinematic Constraints in Laparoscopic Surgery.

To better understand how kinematic variables impact learning in surgical training, we devised an interactive environment for simulated laparoscopic maneuvers, using either 1) mechanical constraints typical of a surgical "box-trainer" or 2) virtual constraints in which free hand movements control virtual tool motion. 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). Training with the Orientation-Based constraint enabled much lower path error and shorter movement times during training, which suggests that tool motion that simply mirrors joint motion is easier to learn. When evaluated in physically 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 difference: 14.0 percent; CI: 0.7, 28.6). Furthermore, the Near-Far-Near schedule allowed a greater decrease in task time relative to the Far-Near-Far sequence (mean -13:5 percent, CI: -19:5, -7:5). Training that focused on shallow tool insertion (near targets) 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 trocar.

Evaluation of negative viscosity as upper extremity training for stroke survivors.

With stroke survivors (n=30) as the test population, we investigated how upper extremity training with negative viscosity affects coordination in unassisted conditions. Using a planar force-feedback device, subjects performed exploratory movements within an environment that simulated 1) negative viscosity added to elbow and shoulder joints 2) augmented inertia to the upper and lower arm combined with negative viscosity, or 3) a null force field (control). After training, we evaluated each subject's ability to perform circular movements in the null field. Negative viscosity training resulted in greater within-day reductions in error compared with the combined field training. Negative viscosity promoted greater distributions of accelerations during free exploration, especially in the sagittal axis, while combined field training diminished overall activity. Both force field training groups exhibited next day retention, while this was not observed for the control group. The improvement in performance suggests that greater range of kinematic experiences contribute to learning, even despite novel force field environments. These findings provide support for the use of movement amplifying environments for upper extremity rehabilitation, allowing greater access to training while maintaining user engagement.