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.

Transcriptomic analysis of lung development in wildtype and CFTR-/- sheep suggests an early inflammatory signature in the CF distal lung.

The precise molecular events initiating human lung disease are often poorly characterized. Investigating prenatal events that may underlie lung disease in later life is challenging in man, but insights from the well-characterized sheep model of lung development are valuable. Here, we determine the transcriptomic signature of lung development in wild-type sheep (WT) and use a sheep model of cystic fibrosis (CF) to characterize disease associated changes in gene expression through the pseudoglandular, canalicular, saccular, and alveolar stages of lung growth and differentiation. Using gene ontology process enrichment analysis of differentially expressed genes at each developmental time point, we define changes in biological processes (BP) in proximal and distal lung from WT or CF animals. We also compare divergent BP in WT and CF animals at each time point. Next, we establish the developmental profile of key genes encoding components of ion transport and innate immunity that are pivotal in CF lung disease and validate transcriptomic data by RT-qPCR. Consistent with the known pro-inflammatory phenotype of the CF lung after birth, we observe upregulation of inflammatory response processes in the CF sheep distal lung during the saccular stage of prenatal development. These data suggest early commencement of therapeutic regimens may be beneficial.

Cell function and identity revealed by comparative scRNA-seq analysis in human nasal, bronchial and epididymis epithelia.

The evolutionary relationship of cells within tissues having a similar function but located in different anatomical sites is of considerable biological interest. The development of single-cell RNA sequencing (scRNA-seq) protocols has greatly enhanced opportunities to address this topic. Here we focus on cells in the epithelium which lines two regions of the human respiratory tract and the male genital ducts to delineate the shared, differentiated functions of the different cell populations. Transcriptomic data were used to assess the gene expression profiles of human bronchial, nasal, and epididymal epithelium (HBE, HNE, and HEE). Bulk RNA-seq showed many shared genes expressed in cells from the nasal and bronchial epithelium and highlighted their divergence from the epididymal epithelium. ScRNA-seq in HBE and HNE cells demonstrated overlapping gene expression patterns within basal and secretory cell populations. Moreover, the distribution of cell types was altered in HNE cells derived from donors with cystic fibrosis (CF) when compared to cells from healthy donors. Next, the HBE and HNE datasets were merged and confirmed intersection of cell type gene expression profiles from the two sites. However, secretory and ciliated cells were the most abundant types in the HBE samples, while more basal cells were seen in the HNE populations. We then merged single-cell data from the epididymis to determine if overlapping functions of these cells corresponded to those in the airway. Of note, only the pulmonary ionocytes/epididymis clear cells showed a strongly conserved identity, which was confirmed by imputation in bulk RNA-seq datasets from the same cells.

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.

Sensors and Psychomotor Metrics: A Unique Opportunity to Close the Gap on Surgical Processes and Outcomes.

The surgical process remains elusive to many. This paper presents two independent empirical investigations where psychomotor skill metrics were used to quantify elements of the surgical process in a procedural context during surgical tasks in a simulated environment. The overarching goal of both investigations was to address the following hypothesis: Basic motion metrics can be used to quantify specific aspects of the surgical process including instrument autonomy, psychomotor efficiency, procedural readiness, and clinical errors. Electromagnetic motion tracking sensors were secured to surgical trainees' (N = 64) hands for both studies, and several motion metrics were investigated as a measure of surgical skill. The first study assessed performance during a bowel repair and laparoscopic ventral hernia (LVH) repair in comparison to a suturing board task. The second study assessed performance in a VR task in comparison to placement of a subclavian central line. The findings of the first study support our subhypothesis that motion metrics have a generalizable application to surgical skill by showing significant correlations in instrument autonomy and psychomotor efficiency during the suturing task and bowel repair (idle time: r = 0.46, p < 0.05; average velocity: r = 0.57, p < 0.05) and the suturing task and LVH repair (jerk magnitude: r = 0.36, p < 0.05; bimanual dexterity: r = 0.35, p < 0.05). In the second study, performance in VR (steering and jerkiness) correlated to clinical errors (r = 0.58, p < 0.05) and insertion time (r = 0.55, p < 0.05) in placement of a subclavian central line. Both gross (dexterity) and fine motor skills (steering) were found to be important as well as efficiency (i.e., idle time, duration, velocity) when seeking to understand the quality of surgical performance. Both studies support our hypotheses that basic motion metrics can be used to quantify specific aspects of the surgical process and that the use of different technologies and metrics are important for comprehensive investigations of surgical skill.

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.

Strain analysis for the identification of hypertensive cardiac end-organ damage in the emergency department.

Hypertensive emergency is a life-threatening state. End-organ damage affecting the heart accounts for up to 52% of hypertensive emergencies commonly encountered in the emergency department. Recent evidence indicates that strain echocardiography with computerized speckle-tracking is more sensitive at identifying hypertension induced changes in the left ventricle (LV) mechanical function than traditional 2-D echocardiography. We present a case demonstrating the use of emergency physician performed point-of-care strain echocardiography to identify and quantify LV mechanical dysfunction during a hypertensive crisis and to monitor improvement over 6 h.

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.

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.

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.

Comparison of Intravenous Ketorolac at Three Single-Dose Regimens for Treating Acute Pain in the Emergency Department: A Randomized Controlled Trial.

STUDY OBJECTIVE: Nonsteroidal anti-inflammatory drugs are used extensively for the management of acute and chronic pain, with ketorolac tromethamine being one of the most frequently used parenteral analgesics in the emergency department (ED). The drugs may commonly be used at doses above their analgesic ceiling, offering no incremental analgesic advantage while potentially adding risk of harm. We evaluate the analgesic efficacy of 3 doses of intravenous ketorolac in ED patients with acute pain. METHODS: We conducted a randomized, double-blind trial to assess the analgesic efficacy of 3 doses of intravenous ketorolac (10, 15, and 30 mg) in patients aged 18 to 65 years and presenting to the ED with moderate to severe acute pain, defined by a numeric rating scale score greater than or equal to 5. We excluded patients with peptic ulcer disease, gastrointestinal hemorrhage, renal or hepatic insufficiency, allergies to nonsteroidal anti-inflammatory drugs, pregnancy or breastfeeding, systolic blood pressure less than 90 or greater than 180 mm Hg, and pulse rate less than 50 or greater than 150 beats/min. Primary outcome was pain reduction at 30 minutes. We recorded pain scores at baseline and up to 120 minutes. Intravenous morphine 0.1 mg/kg was administered as a rescue analgesic if subjects still desired additional pain medication at 30 minutes after the study drug was administered. Data analyses included mixed-model regression and ANOVA. RESULTS: We enrolled 240 subjects (80 in each dose group). At 30 minutes, substantial pain reduction was demonstrated without any differences between the groups (95% confidence intervals 4.5 to 5.7 for the 10-mg group, 4.5 to 5.6 for the 15-mg group, and 4.2 to 5.4 for the 30-mg group). The mean numeric rating scale pain scores at baseline were 7.7, 7.5, and 7.8 and improved to 5.1, 5.0, and 4.8, respectively, at 30 minutes. Rates of rescue analgesia were similar, and there were no serious adverse events. Secondary outcomes showed similar rates of adverse effects per group, of which the most common were dizziness, nausea, and headache. CONCLUSION: Ketorolac has similar analgesic efficacy at intravenous doses of 10, 15, and 30 mg, showing that intravenous ketorolac administered at the analgesic ceiling dose (10 mg) provided effective pain relief to ED patients with moderate to severe pain without increased adverse effects.

Meta-analysis to Determine Risk for Serious Bacterial Infection in Febrile Outpatient Neonates With RSV Infection.

OBJECTIVES: This study aimed to analyze a large group of febrile neonates 28 days or younger who received outpatient sepsis evaluation and nasopharyngeal aspirate antigen testing (NPAT) for respiratory syncytial viral (RSV) infection to determine whether there is a clinically significant association between viral study results and risk for serious bacterial infection (SBI: bacterial meningitis, bacteremia, urinary tract infection, bacterial enteritis). METHODS: We evaluated consecutive febrile neonates 28 days or younger presenting to our urban pediatric emergency department [Maimonides Medical Center (MMC)] during a 6-year period, all of whom received a sepsis evaluation (cerebrospinal fluid, blood, urine cultures) and RSV NPAT. To achieve adequate power (80%), the MMC data were combined with similar data reported from a prior prospective PEM-CRC study of like-aged febrile neonates who received similar evaluation. RESULTS: From the MMC data of consecutively evaluated cases, the prevalence rate of +RSV in 387 febrile neonates was 6%. Of these, 378 (98%) received both a sepsis evaluation and RSV NPAT; +SBI occurred in 4/22 (18.1%) with +RSV versus 58/356 (16.2%) with -RSV (P = 0.77). Combined with the PEM-CRC cohort of 411 febrile neonates 28 days or younger who received similar evaluation, a total of 789 cases were analyzed using meta-analysis. Overall, there were 117 (14.8%) cases of +SBI and 104 (13.2%) cases of +RSV. The rate of +SBI was 11.5% in those with +RSV versus 15.3% in those with -RSV. Meta-analysis performed showed no significant difference in rates of +SBI between those with and without +RSV (odds ratio, 0.78; 95% confidence interval, 0.41-1.50; P = 0.46). CONCLUSIONS: Rates of +SBI are not significantly different between febrile neonates 28 days or younger with and without +RSV. Respiratory viral infection status is not an accurate clinical determinant in distinguishing SBI risk in febrile neonates.

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.

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.

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.