The Likelihood That Remedial Continuing Medical Education (CME) Reduces Disciplinary Recidivism Among Physicians.

PURPOSE: State medical boards are charged through their medical practice acts to regulate physician practice and, when necessary, discipline physicians for incompetent or inappropriate behavior. Boards often authorize remedial continuing medical education (CME) as part of a disciplinary action; however, it is unclear how effective remedial CME is in reducing the likelihood of physicians receiving additional discipline. This study examined the relationship between physicians who were required to complete remedial CME as part of their first discipline by state medical boards and the likelihood of additional discipline. METHOD: The national-level sample included 4,061 MD-physicians whose first discipline included license restrictions, probation, or other conditions imposed by state medical boards between 2011 and 2015. A multivariate logistic regression model examined whether physicians required to complete remedial CME as part of their first discipline were less likely to receive additional discipline by boards within 5 years. RESULTS: Of the 4,061 physicians, 36% (n = 1,449) were required to complete remedial CME as part of their first discipline, and 35% (n = 1,426) received additional discipline within 5 years. After accounting for other factors, physicians who were required to complete remedial CME as part of their first discipline by boards were less likely to receive additional discipline (odds ratio = 0.597; 95% confidence interval = 0.513, 0.696; P < .001) within 5 years compared to physicians who were not required to complete remedial CME. CONCLUSIONS: Findings support remedial CME as a means to help reduce physician disciplinary recidivism in certain circumstances. Physicians required to complete remedial CME as part of their first discipline were less likely to receive additional discipline by state medical boards within 5 years.

An atomic boson sampler.

A boson sampler implements a restricted model of quantum computing. It is defined by the ability to sample from the distribution resulting from the interference of identical bosons propagating according to programmable, non-interacting dynamics1. An efficient exact classical simulation of boson sampling is not believed to exist, which has motivated ground-breaking boson sampling experiments in photonics with increasingly many photons2-12. However, it is difficult to generate and reliably evolve specific numbers of photons with low loss, and thus probabilistic techniques for postselection7 or marked changes to standard boson sampling10-12 are generally used. Here, we address the above challenges by implementing boson sampling using ultracold atoms13,14 in a two-dimensional, tunnel-coupled optical lattice. This demonstration is enabled by a previously unrealized combination of tools involving high-fidelity optical cooling and imaging of atoms in a lattice, as well as programmable control of those atoms using optical tweezers. When extended to interacting systems, our work demonstrates the core abilities required to directly assemble ground and excited states in simulations of various Hubbard models15,16.

Improving Biological Joint Moment Estimation During Real-World Tasks with EMG and Instrumented Insoles.

OBJECTIVE: Real-time measurement of biological joint moment could enhance clinical assessments and generalize exoskeleton control. Accessing joint moments outside clinical and laboratory settings requires harnessing non-invasive wearable sensor data for indirect estimation. Previous approaches have been primarily validated during cyclic tasks, such as walking, but these methods are likely limited when translating to non-cyclic tasks where the mapping from kinematics to moments is not unique. METHODS: We trained deep learning models to estimate hip and knee joint moments from kinematic sensors, electromyography (EMG), and simulated pressure insoles from a dataset including 10 cyclic and 18 non-cyclic activities. We assessed estimation error on combinations of sensor modalities during both activity types. RESULTS: Compared to the kinematics-only baseline, adding EMG reduced RMSE by 16.9% at the hip and 30.4% at the knee (p<0.05) and adding insoles reduced RMSE by 21.7% at the hip and 33.9% at the knee (p<0.05). Adding both modalities reduced RMSE by 32.5% at the hip and 41.2% at the knee (p<0.05) which was significantly higher than either modality individually (p<0.05). All sensor additions improved model performance on non-cyclic tasks more than cyclic tasks (p<0.05). CONCLUSION: These results demonstrate that adding kinetic sensor information through EMG or insoles improves joint moment estimation both individually and jointly. These additional modalities are most important during non-cyclic tasks, tasks that reflect the variable and sporadic nature of the real-world. SIGNIFICANCE: Improved joint moment estimation and task generalization is pivotal to developing wearable robotic systems capable of enhancing mobility in everyday life.

A Data-Driven Approach to Estimate Human Center of Mass State During Perturbed Locomotion Using Simulated Wearable Sensors.

Center of mass (COM) state, specifically in a local reference frame (i.e., relative to center of pressure), is an important variable for controlling and quantifying bipedal locomotion. However, this metric is not easily attainable in real time during human locomotion experiments. This information could be valuable when controlling wearable robotic exoskeletons, specifically for stability augmentation where knowledge of COM state could enable step placement planners similar to bipedal robots. Here, we explored the ability of simulated wearable sensor-driven models to rapidly estimate COM state during steady state and perturbed walking, spanning delayed estimates (i.e., estimating past state) to anticipated estimates (i.e., estimating future state). We used various simulated inertial measurement unit (IMU) sensor configurations typically found on lower limb exoskeletons and a temporal convolutional network (TCN) model throughout this analysis. We found comparable COM estimation capabilities across hip, knee, and ankle exoskeleton sensor configurations, where device type did not significantly influence error. We also found that anticipating COM state during perturbations induced a significant increase in error proportional to anticipation time. Delaying COM state estimates significantly increased accuracy for velocity estimates but not position estimates. All tested conditions resulted in models with R2 > 0.85, with a majority resulting in R2 > 0.95, emphasizing the viability of this approach. Broadly, this preliminary work using simulated IMUs supports the efficacy of wearable sensor-driven deep learning approaches to provide real-time COM state estimates for lower limb exoskeleton control or other wearable sensor-based applications, such as mobile data collection or use in real-time biofeedback.

Estimating human joint moments unifies exoskeleton control, reducing user effort.

Robotic lower-limb exoskeletons can augment human mobility, but current systems require extensive, context-specific considerations, limiting their real-world viability. Here, we present a unified exoskeleton control framework that autonomously adapts assistance on the basis of instantaneous user joint moment estimates from a temporal convolutional network (TCN). When deployed on our hip exoskeleton, the TCN achieved an average root mean square error of 0.142 newton-meters per kilogram across 35 ambulatory conditions without any user-specific calibration. Further, the unified controller significantly reduced user metabolic cost and lower-limb positive work during level-ground and incline walking compared with walking without wearing the exoskeleton. This advancement bridges the gap between in-lab exoskeleton technology and real-world human ambulation, making exoskeleton control technology viable for a broad community.

User- and Speed-Independent Slope Estimation for Lower-Extremity Wearable Robots.

Wearable robots can help users traverse unstructured slopes by providing mode-specific hip, knee, and ankle joint assistance. However, generalizing the same assistance pattern across different slopes is not optimal. Control strategies that scale assistance based on slope are expected to improve the feel of the device and improve outcome measures such as decreasing metabolic cost. Prior numerical methods for slope estimation struggled to estimate slopes at variable walking speeds or were limited to a single estimation per gait cycle. This study overcomes these limitations by developing machine-learning methods that yield continuous, user- and speed-independent slope estimators for a variety of wearable robot applications using an able-bodied wearable sensor dataset. In a leave-one-subject-out cross-validation (N = 9), four-phase XGBoost regression models were trained on static-slope (fixed-slope) data and evaluated on a novel subject's static-slope and dynamic-slope (variable-slope) data. Using all available sensors, we achieved an average error of 0.88° and 1.73° mean absolute error (MAE) on static and dynamic slopes, respectively. Ankle prosthesis, knee-ankle prosthesis, and hip exoskeleton sensor suites yielded average errors under 2° MAE on static and dynamic slopes, except for the ankle prosthesis and hip exoskeleton cases on dynamic slopes which yielded an average error of 2.2° and 3.2° MAE, respectively. We found that the thigh inertial measurement unit contributed the most to a reduction in average error. Our findings suggest that reliable slope estimators can be trained using only static-slope data regardless of the type of lower-extremity wearable robot.

A human lower-limb biomechanics and wearable sensors dataset during cyclic and non-cyclic activities.

Tasks of daily living are often sporadic, highly variable, and asymmetric. Analyzing these real-world non-cyclic activities is integral for expanding the applicability of exoskeletons, protheses, wearable sensing, and activity classification to real life, and could provide new insights into human biomechanics. Yet, currently available biomechanics datasets focus on either highly consistent, continuous, and symmetric activities, such as walking and running, or only a single specific non-cyclic task. To capture a more holistic picture of lower limb movements in everyday life, we collected data from 12 participants performing 20 non-cyclic activities (e.g. sit-to-stand, jumping, squatting, lunging, cutting) as well as 11 cyclic activities (e.g. walking, running) while kinematics (motion capture and IMUs), kinetics (force plates), and electromyography (EMG) were collected. This dataset provides normative biomechanics for a highly diverse range of activities and common tasks from a consistent set of participants and sensors.

A Review of Current State-of-the-Art Control Methods for Lower-Limb Powered Prostheses.

Lower-limb prostheses aim to restore ambulatory function for individuals with lower-limb amputations. While the design of lower-limb prostheses is important, this paper focuses on the complementary challenge - the control of lower-limb prostheses. Specifically, we focus on powered prostheses, a subset of lower-limb prostheses, which utilize actuators to inject mechanical power into the walking gait of a human user. In this paper, we present a review of existing control strategies for lower-limb powered prostheses, including the control objectives, sensing capabilities, and control methodologies. We separate the various control methods into three main tiers of prosthesis control: high-level control for task and gait phase estimation, mid-level control for desired torque computation (both with and without the use of reference trajectories), and low-level control for enforcing the computed torque commands on the prosthesis. In particular, we focus on the high- and mid-level control approaches in this review. Additionally, we outline existing methods for customizing the prosthetic behavior for individual human users. Finally, we conclude with a discussion on future research directions for powered lower-limb prostheses based on the potential of current control methods and open problems in the field.

Realizing spin squeezing with Rydberg interactions in an optical clock.

Neutral-atom arrays trapped in optical potentials are a powerful platform for studying quantum physics, combining precise single-particle control and detection with a range of tunable entangling interactions1-3. For example, these capabilities have been leveraged for state-of-the-art frequency metrology4,5 as well as microscopic studies of entangled many-particle states6-11. Here we combine these applications to realize spin squeezing-a widely studied operation for producing metrologically useful entanglement-in an optical atomic clock based on a programmable array of interacting optical qubits. In this demonstration of Rydberg-mediated squeezing with a neutral-atom optical clock, we generate states that have almost four decibels of metrological gain. In addition, we perform a synchronous frequency comparison between independent squeezed states and observe a fractional-frequency stability of 1.087(1) × 10-15 at one-second averaging time, which is 1.94(1) decibels below the standard quantum limit and reaches a fractional precision at the 10-17 level during a half-hour measurement. We further leverage the programmable control afforded by optical tweezer arrays to apply local phase shifts to explore spin squeezing in measurements that operate beyond the relative coherence time with the optical local oscillator. The realization of this spin-squeezing protocol in a programmable atom-array clock will enable a wide range of quantum-information-inspired techniques for optimal phase estimation and Heisenberg-limited optical atomic clocks12-16.

Encoding integers and rationals on neuromorphic computers using virtual neuron.

Neuromorphic computers emulate the human brain while being extremely power efficient for computing tasks. In fact, they are poised to be critical for energy-efficient computing in the future. Neuromorphic computers are primarily used in spiking neural network-based machine learning applications. However, they are known to be Turing-complete, and in theory can perform all general-purpose computation. One of the biggest bottlenecks in realizing general-purpose computations on neuromorphic computers today is the inability to efficiently encode data on the neuromorphic computers. To fully realize the potential of neuromorphic computers for energy-efficient general-purpose computing, efficient mechanisms must be devised for encoding numbers. Current encoding mechanisms (e.g., binning, rate-based encoding, and time-based encoding) have limited applicability and are not suited for general-purpose computation. In this paper, we present the virtual neuron abstraction as a mechanism for encoding and adding integers and rational numbers by using spiking neural network primitives. We evaluate the performance of the virtual neuron on physical and simulated neuromorphic hardware. We estimate that the virtual neuron could perform an addition operation using just 23 nJ of energy on average with a mixed-signal, memristor-based neuromorphic processor. We also demonstrate the utility of the virtual neuron by using it in some of the µ-recursive functions, which are the building blocks of general-purpose computation.

Towards meaningful community ambulation in individuals post stroke through use of a smart hip exoskeleton: a preliminary investigation.

Stroke is the leading cause of long-term disability in the United States, leaving survivors with profound mobility challenges that impact independent community ambulation. Evidence shows assistance at the hip during walking may be beneficial for stroke survivors. In this cross-over design study, we examine the impact of a novel hip exoskeleton on both functional and patient reported outcomes measuring speed, fall risk, gait symmetry, energy expenditure and perceived walking ability during both indoors and outdoors in single and serial counting dual task paradigms. Nine ambulatory stroke survivors with hemiplegia were included. No differences were seen between the exoskeleton and baseline conditions for any outcomes. Only the patient reported outcome in which subjects were asked to rate their ability to walk outdoors approached statistical significance (p = 0.051) with greater improvement reported for the exoskeleton condition. When asked to rate several key factors about the exoskeleton, weight and assistance emerged as primary perceived negative factors of the exoskeleton underscoring the need for improvements to the technology in this area. Despite lack of differences across groups, some individuals responded positively to the exoskeleton for several functional outcomes measured, highlighting the need for additional exploration into the use of personalized hip exoskeletons for post-stroke rehabilitation.

Reducing Knee Hyperextension With an Exoskeleton in Children and Adolescents With Genu Recurvatum: A Feasibility Study.

Genu recurvatum, or knee hyperextension, is a complex gait pattern with a variety of etiologies, and is often connected with knee weakness, lack of motor control, and spasticity. Because of the atypical forces placed on the soft tissues, early treatment or prevention of knee hyperextension may help prevent further degradation of the knee joint. In this study, we assessed the feasibility of a knee exoskeleton to mitigate hyperextension and increase swing range of motion in five children/adolescents who presented with unilateral genu recurvatum. Over the course of three visits, each participant practiced walking with the exoskeleton, which provided torque assistance during both stance and swing based on an impedance control law. In final validation trials, the exoskeleton was effective in reducing knee hyperextension (0.2 ± 4.7° average peak knee extension without exo to 9.9 ± 10.3° with exo) and improving swing range of motion by 14.0 ± 4.5° increase on average. However, while the exoskeleton was effective in normalizing the kinematics, it did not lead to improved spatio-temporal asymmetry measures. This work showcases a promising potential application of a robotic knee exoskeleton for improving the kinematic characteristics of genu recurvatum gait.

Linking whole-body angular momentum and step placement during perturbed human walking.

Human locomotion is remarkably robust to environmental disturbances. Previous studies have thoroughly investigated how perturbations influence body dynamics and what recovery strategies are used to regain balance. Fewer studies have attempted to establish formal links between balance and the recovery strategies that are executed to regain stability. We hypothesized that there would be a strong relationship between the magnitude of imbalance and recovery strategy during perturbed walking. To test this hypothesis, we applied transient ground surface translations that varied in magnitude, direction and onset time while 11 healthy participants walked on a treadmill. We measured stability using integrated whole-body angular momentum (iWBAM) and recovery strategy using step placement. We found the strongest relationships between iWBAM and step placement in the frontal plane for earlier perturbation onset times in the perturbed step (R2=0.52, 0.50) and later perturbation onset times in the recovery step (R2=0.18, 0.25), while correlations were very weak in the sagittal plane (all R2≤0.13). These findings suggest that iWBAM influences step placement, particularly in the frontal plane, and that this influence is sensitive to perturbation onset time. Lastly, this investigation is accompanied by an open-source dataset to facilitate research on balance and recovery strategies in response to multifactorial ground surface perturbations, including 96 perturbation conditions spanning all combinations of three magnitudes, eight directions and four gait cycle onset times.

Emulator-Based Optimization of a Semi-Active Hip Exoskeleton Concept: Sweeping Impedance Across Walking Speeds.

OBJECTIVE: Semi-active exoskeletons combining lightweight, low powered actuators and passive-elastic elements are a promising approach to portable robotic assistance during locomotion. Here, we introduce a novel semi-active hip exoskeleton concept and evaluate human walking performance across a range of parameters using a tethered robotic testbed. METHODS: We emulated semi-active hip exoskeleton (exo) assistance by applying a virtual torsional spring with a fixed rotational stiffness and an equilibrium angle established in terminal swing phase (i.e., via pre-tension into stance). We performed a 2-D sweep of spring stiffness x equilibrium position parameters (30 combinations) across walking speed (1.0, 1.3, and 1.6 m/s) and measured metabolic rate to identify device parameters for optimal metabolic benefit. RESULTS: At each speed, optimal exoskeleton spring settings provided a ∼10% metabolic benefit compared to zero-impedance (ZI). Higher walking speeds required higher exoskeleton stiffness and lower equilibrium angle for maximal metabolic benefit. Optimal parameters tuned to each individual (user-dependent) provided significantly larger metabolic benefit than the average-best settings (user-independent) at all speeds except the fastest (p = 0.021, p = 0.001, and p = 0.098 at 1.0, 1.3, and 1.6 m/s, respectively). We found significant correlation between changes in user's muscle activity and changes in metabolic rate due to exoskeleton assistance, especially for muscles crossing the hip joint. CONCLUSION: A semi-active hip exoskeleton with spring-parameters personalized to each user could provide metabolic benefit across functional walking speeds. Minimizing muscle activity local to the exoskeleton is a promising approach for tuning assistance on-line on a user-dependent basis.

Effects of Bilateral Assistance for Hemiparetic Gait Post-Stroke Using a Powered Hip Exoskeleton.

Hemiparetic gait due to stroke is characterized by an asymmetric gait due to weakness in the paretic lower limb. These inter-limb asymmetries increase the biomechanical demand and reduce walking speed, leading to reduced community mobility and quality of life. With recent progress in the field of wearable technologies, powered exoskeletons have shown great promise as a potential solution for improving gait post-stroke. While previous studies have adopted different exoskeleton control methodologies for restoring gait post-stroke, the results are highly variable due to limited understanding of the biomechanical effect of exoskeletons on hemiparetic gait. In this study, we investigated the effect of different hip exoskeleton assistance strategies on gait function and gait biomechanics of individuals post-stroke. We found that, compared to walking without a device, powered assistance from hip exoskeletons improved stroke participants' self-selected overground walking speed by 17.6 ± 2.5% and 11.1 ± 2.7% with a bilateral and unilateral assistance strategy, respectively (p < 0.05). Furthermore, both bilateral and unilateral assistance strategies significantly increased the paretic and non-paretic step length (p < 0.05). Our findings suggest that powered assistance from hip exoskeletons is an effective means to increase walking speed post-stroke and tuning the balance of assistance between non-paretic and paretic limbs (i.e., a bilateral strategy) may be most effective to maximize performance gains.

Biomechanical Evaluation of Stair Ambulation Using Impedance Control on an Active Prosthesis.

Active prostheses can provide net positive work to individuals with amputation, offering more versatility across locomotion tasks than passive prostheses. However, the effect of powered joints on bilateral biomechanics has not been widely explored for ambulation modes different than level ground and treadmill walking. In this study, we present the bilateral biomechanics of stair ascent and descent with a powered knee-ankle prosthesis compared to the biomechanical profiles of able-bodied subjects at different configurations of stair height between 102 mm and 178 mm. In addition, we include reference profiles from users with passive prostheses for the nominal stair height of 152 mm to place our findings in relation to the typical solution for individuals with transfemoral amputation (TFA). We report the biomechanical profiles of kinematics, kinetics, and power, together with temporal and waveform symmetry and distribution of mechanical energy across the joints. We found that an active prosthesis provides a substantial contribution to mechanical power during stair ascent and power absorption during stair descent and gait patterns like able-bodied subjects. The active prosthesis enables step-over-step gait in stair ascent. This translates into a lower mechanical energy requirement on the intact side, with a 57% reduction of energy at the knee and 26% at the hip with respect to the passive prosthesis. For stair descent, we found a 28% reduction in the negative work done by the intact ankle. These results reflect the benefit of active prostheses, allowing the users to complete tasks more efficiently than passive legs. However, in comparison to able-bodied biomechanics, the results still differ from the ideal patterns. We discuss the limitations that explain this difference and suggest future directions for the design of impedance controllers by taking inspiration from the biological modulation of the knee moment as a function of the stair height.

Exoskeletal solutions to enable mobility with a lower leg fracture in austere environments.

The treatment and evacuation of people with lower limb fractures in austere environments presents unique challenges that assistive exoskeletal devices could address. In these dangerous situations, independent mobility for the injured can preserve their vital capabilities so that they can safely evacuate and minimize the need for additional personnel to help. This expert view article discusses how different exoskeleton archetypes could provide independent mobility while satisfying the requisite needs for portability, maintainability, durability, and adaptability to be available and useful within austere environments. The authors also discuss areas of development that would enable exoskeletons to operate more effectively in these scenarios as well as preserve the health of the injured limb so that definitive treatment after evacuation will produce better outcomes.

Tweezer-programmable 2D quantum walks in a Hubbard-regime lattice.

Quantum walks provide a framework for designing quantum algorithms that is both intuitive and universal. To leverage the computational power of these walks, it is important to be able to programmably modify the graph a walker traverses while maintaining coherence. We do this by combining the fast, programmable control provided by optical tweezers with the scalable, homogeneous environment of an optical lattice. With these tools we study continuous-time quantum walks of single atoms on a square lattice and perform proof-of-principle demonstrations of spatial search with these walks. When scaled to more particles, the capabilities demonstrated can be extended to study a variety of problems in quantum information science, including performing more effective versions of spatial search using a larger graph with increased connectivity.

An Examination of the Associations Among USMLE Step 3 Scores and the Likelihood of Disciplinary Action in Practice.

PURPOSE: As the last examination in the United States Medical Licensing Examination (USMLE) sequence, Step 3 provides a safeguard before physicians enter into unsupervised practice. There is, however, little validity research focusing on Step 3 scores beyond examining its associations with other educational and professional assessments thought to cover similar content. This study examines the associations between Step 3 scores and subsequent receipt of disciplinary action taken by state medical boards for problematic behavior in practice. It analyzes Step 3 total, Step 3 computer-based case simulation (CCS), and Step 3 multiple-choice question (MCQ) scores. METHOD: The final sample included 275,392 board-certified physicians who graduated from MD-granting medical schools and who passed Step 3 between 2000 and 2017. Cross-classified multilevel logistic regression models were used to examine the effects of Step 3 scores on the likelihood of receiving a disciplinary action, controlling for other USMLE scores and accounting for jurisdiction and specialty. RESULTS: Results showed that physicians with higher Step 3 total, CCS, and MCQ scores tended to have lower chances of receiving a disciplinary action, after accounting for other USMLE scores. Specifically, a 1-standard-deviation increase in Step 3 total, CCS, and MCQ score was associated with a 23%, 11%, and 17% decrease in the odds of receiving a disciplinary action, respectively. The effect of Step 2 CK score on the likelihood of receiving a disciplinary action was statistically significant, while the effect of Step 1 score became statistically nonsignificant when other Step scores were included in the analysis. CONCLUSIONS: Physicians who perform better on Step 3 are less likely to receive a disciplinary action from a state medical board for problematic behavior in practice. These findings provide some validity evidence for the use of Step 3 scores when making medical licensure decisions in the United States.

Expanding Our Reach: Development of a Civilian Helicopter Air Ambulance K9 Response Program.

This short communication highlights the development and implementation of the first civilian helicopter air ambulance canine response program in the United States.