Regulations & exemptions during the COVID-19 pandemic for new medical technology, health services & data

The rapid evolution of the COVID-19 pandemic has sparked a large unmet need for new or additional medical technology and healthcare services to be made available urgently. Healthcare, Academic, Government and Industry organizations and individuals have risen to this challenge by designing, developing, manufacturing or implementing innovation. However, both they and healthcare stakeholders are hampered as it is unclear how to introduce and deploy the products of this innovation quickly and legally within the healthcare system. Our paper outlines the key regulations and processes innovators need to comply with, and how these change during a public health emergency via dedicated exemptions. Our work includes references to the formal documents regarding UK healthcare regulation and governance, and is meant to serve as a guide for those who wish to act quickly but are uncertain of the legal and regulatory pathways that allow new a device or service to be fast-tracked

Systematic and Realistic Testing in Simulation of Control Code for Robots in Collaborative Human-Robot Interactions

© Springer International Publishing Switzerland 2016. Industries such as flexible manufacturing and home care will be transformed by the presence of robotic assistants. Assurance of safety and functional soundness for these robotic systems will require rigorous verification and validation. We propose testing in simulation using Coverage-Driven Verification (CDV) to guide the testing process in an automatic and systematic way. We use a two-tiered test generation approach, where abstract test sequences are computed first and then concretized (e.g., data and variables are instantiated), to reduce the complexity of the test generation problem. To demonstrate the effectiveness of our approach, we developed a testbench for robotic code, running in ROS-Gazebo, that implements an object handover as part of a humanrobot interaction (HRI) task. Tests are generated to stimulate the robot’s code in a realistic manner, through stimulating the human, environment, sensors, and actuators in simulation. We compare the merits of unconstrained, constrained and model-based test generation in achieving thorough exploration of the code under test, and interesting combinations of human-robot interactions. Our results show that CDV combined with systematic test generation achieves a very high degree of automation in simulation-based verification of control code for robots in HRI

Self versus Environment Motion in Postural Control

To stabilize our position in space we use visual information as well as non-visual physical motion cues. However, visual cues can be ambiguous: visually perceived motion may be caused by self-movement, movement of the environment, or both. The nervous system must combine the ambiguous visual cues with noisy physical motion cues to resolve this ambiguity and control our body posture. Here we have developed a Bayesian model that formalizes how the nervous system could solve this problem. In this model, the nervous system combines the sensory cues to estimate the movement of the body. We analytically demonstrate that, as long as visual stimulation is fast in comparison to the uncertainty in our perception of body movement, the optimal strategy is to weight visually perceived movement velocities proportional to a power law. We find that this model accounts for the nonlinear influence of experimentally induced visual motion on human postural behavior both in our data and in previously published results

Bayesian Inference Underlies the Contraction Bias in Delayed Comparison Tasks

Delayed comparison tasks are widely used in the study of working memory and perception in psychology and neuroscience. It has long been known, however, that decisions in these tasks are biased. When the two stimuli in a delayed comparison trial are small in magnitude, subjects tend to report that the first stimulus is larger than the second stimulus. In contrast, subjects tend to report that the second stimulus is larger than the first when the stimuli are relatively large. Here we study the computational principles underlying this bias, also known as the contraction bias. We propose that the contraction bias results from a Bayesian computation in which a noisy representation of a magnitude is combined with a-priori information about the distribution of magnitudes to optimize performance. We test our hypothesis on choice behavior in a visual delayed comparison experiment by studying the effect of (i) changing the prior distribution and (ii) changing the uncertainty in the memorized stimulus. We show that choice behavior in both manipulations is consistent with the performance of an observer who uses a Bayesian inference in order to improve performance. Moreover, our results suggest that the contraction bias arises during memory retrieval/decision making and not during memory encoding. These results support the notion that the contraction bias illusion can be understood as resulting from optimality considerations

The Second-Agent Effect: Communicative Gestures Increase the Likelihood of Perceiving a Second Agent

Background: Beyond providing cues about an agent’s intention, communicative actions convey information about the presence of a second agent towards whom the action is directed (second-agent information). In two psychophysical studies we investigated whether the perceptual system makes use of this information to infer the presence of a second agent when dealing with impoverished and/or noisy sensory input. Methodology/Principal Findings: Participants observed point-light displays of two agents (A and B) performing separate actions. In the Communicative condition, agent B’s action was performed in response to a communicative gesture by agent A. In the Individual condition, agent A’s communicative action was replaced with a non-communicative action. Participants performed a simultaneous masking yes-no task, in which they were asked to detect the presence of agent B. In Experiment 1, we investigated whether criterion c was lowered in the Communicative condition compared to the Individual condition, thus reflecting a variation in perceptual expectations. In Experiment 2, we manipulated the congruence between A’s communicative gesture and B’s response, to ascertain whether the lowering of c in the Communicative condition reflected a truly perceptual effect. Results demonstrate that information extracted from communicative gestures influences the concurrent processing of biological motion by prompting perception of a second agent (second-agent effect). Conclusions/Significance: We propose that this finding is best explained within a Bayesian framework, which gives

Learning Priors for Bayesian Computations in the Nervous System

Our nervous system continuously combines new information from our senses with information it has acquired throughout life. Numerous studies have found that human subjects manage this by integrating their observations with their previous experience (priors) in a way that is close to the statistical optimum. However, little is known about the way the nervous system acquires or learns priors. Here we present results from experiments where the underlying distribution of target locations in an estimation task was switched, manipulating the prior subjects should use. Our experimental design allowed us to measure a subject's evolving prior while they learned. We confirm that through extensive practice subjects learn the correct prior for the task. We found that subjects can rapidly learn the mean of a new prior while the variance is learned more slowly and with a variable learning rate. In addition, we found that a Bayesian inference model could predict the time course of the observed learning while offering an intuitive explanation for the findings. The evidence suggests the nervous system continuously updates its priors to enable efficient behavior

The management of the painful bipartite patella: a systematic review

Purpose: This study aimed to identify the most effective method for the treatment of the symptomatic bipartite patella. Methods: A systematic review of the literature was completed, and all studies assessing the management of a bipartite patella were included. Owing to the paucity of randomised controlled trials, a narrative review of 22 studies was completed. A range of treatments were assessed: conservative measures, open and arthroscopic fixation or excision and soft tissue release and excision. Results: All of the methods provided results ranging from good to excellent, with acceptable complication rates. Conclusions: This is a poorly answered treatment question. No firm guidance can be given as to the most appropriate method of treating the symptomatic bipartite patella. This study suggests that there are a number of effective treatments with acceptable complication rates and it may be that treatments that conserve the patella are more appropriate for larger fragments

Towards plant-odor-related olfactory neuroethology in Drosophila

Drosophila melanogaster is today one of the three foremost models in olfactory research, paralleled only by the mouse and the nematode. In the last years, immense progress has been achieved by combining neurogenetic tools with neurophysiology, anatomy, chemistry, and behavioral assays. One of the most important tasks for a fruit fly is to find a substrate for eating and laying eggs. To perform this task the fly is dependent on olfactory cues emitted by suitable substrates as e.g. decaying fruit. In addition, in this area, considerable progress has been made during the last years, and more and more natural and behaviorally active ligands have been identified. The future challenge is to tie the progress in different fields together to give us a better understanding of how a fly really behaves. Not in a test tube, but in nature. Here, we review our present state of knowledge regarding Drosophila plant-odor-related olfactory neuroethology to provide a basis for new progress

An Empirical Explanation of the Speed-Distance Effect

Understanding motion perception continues to be the subject of much debate, a central challenge being to account for why the speeds and directions seen accord with neither the physical movements of objects nor their projected movements on the retina. Here we investigate the varied perceptions of speed that occur when stimuli moving across the retina traverse different projected distances (the speed-distance effect). By analyzing a database of moving objects projected onto an image plane we show that this phenomenology can be quantitatively accounted for by the frequency of occurrence of image speeds generated by perspective transformation. These results indicate that speed-distance effects are determined empirically from accumulated past experience with the relationship between image speeds and moving objects

Inferring Visuomotor Priors for Sensorimotor Learning

Sensorimotor learning has been shown to depend on both prior expectations and sensory evidence in a way that is consistent with Bayesian integration. Thus, prior beliefs play a key role during the learning process, especially when only ambiguous sensory information is available. Here we develop a novel technique to estimate the covariance structure of the prior over visuomotor transformations – the mapping between actual and visual location of the hand – during a learning task. Subjects performed reaching movements under multiple visuomotor transformations in which they received visual feedback of their hand position only at the end of the movement. After experiencing a particular transformation for one reach, subjects have insufficient information to determine the exact transformation, and so their second reach reflects a combination of their prior over visuomotor transformations and the sensory evidence from the first reach. We developed a Bayesian observer model in order to infer the covariance structure of the subjects' prior, which was found to give high probability to parameter settings consistent with visuomotor rotations. Therefore, although the set of visuomotor transformations experienced had little structure, the subjects had a strong tendency to interpret ambiguous sensory evidence as arising from rotation-like transformations. We then exposed the same subjects to a highly-structured set of visuomotor transformations, designed to be very different from the set of visuomotor rotations. During this exposure the prior was found to have changed significantly to have a covariance structure that no longer favored rotation-like transformations. In summary, we have developed a technique which can estimate the full covariance structure of a prior in a sensorimotor task and have shown that the prior over visuomotor transformations favor a rotation-like structure. Moreover, through experience of a novel task structure, participants can appropriately alter the covariance structure of their prior