Stay Out of the Blast Radius: Influence of Surgical Masks on Virtual Pedestrian Interactions

To combat the global pandemic caused by COVID-19, a series of mitigation strategies have been proposed by governments around the world. While responses varied across different governing bodies, recommendations such as social distancing and the use of facial masks were nearly universal. Considering that even with restrictions in place, walking in community environments remained an important component of everyday life, these public health recommendations, as well as the anxiety generated by the pandemic, are likely to have influenced pedestrian interactions. In this study, we have examined the effect of facial masks and anxiety related to community ambulation in the context of the COVID-19 pandemic. Using virtual reality, obstacle circumvention strategies in response to approaching pedestrians with and without facial masks were measured in a sample of 11 healthy young individuals. Additionally, a questionnaire was developed and used to gain insights into the participant's behaviours during and after a strict period of restrictions that were in effect before the summer of 2020. Results showed that participants maintained a larger obstacle clearance when virtual pedestrians wore a facial mask. The extent of obstacle clearance was also positively associated with anxiety towards community ambulation in the context of the pandemic. Our findings provide evidence that mask-wearing results in an increase in physical distancing during pedestrian interactions, which may help to reduce the risk of infection. Furthermore, results demonstrate the effects of social context and psychological status on pedestrian interactions and highlight the potential of virtual reality simulations to study locomotion in natural community settings.

Joint-level force sensing for indirect hybrid force/position control of continuum robots with friction

Continuum robots offer the dexterity and obstacle circumvention capabilities necessary to enable surgery in deep surgical sites. They also can enable joint-level ex situ force sensing (JEFS), which provides an estimate of end-effector wrenches given joint-level forces. Prior works on JEFS relied on a restrictive embodiment with minimal actuation line friction and captured model and frictional actuation transmission uncertainties using a configuration space formulation. In this work, we overcome these limitations. First, frictional losses are canceled using a feed-forward term based on support vector regression in joint space. Then, regression maps and their interpolation are used to account for actuation hysteresis. The residual joint-force error is then further minimized using a least-squares model parameter update. An indirect hybrid force/position controller using JEFS is presented with evaluation carried out on a realistic pre-clinically deployable insertable robotic effectors platform (IREP) for single-port access surgery. Automated mock force-controlled ablation, exploration, and knot tightening are evaluated. A user study involving the daVinci Research Kit surgeon console and the IREP as a surgical slave was carried out to compare the performance of users with and without force feedback based on JEFS for force-controlled ablation and knot tightening. Results in automated experiments and a user study of telemanipulated experiments suggest that intrinsic force-sensing can achieve levels of force uncertainty and force regulation errors of the order of 0.2 N. Using JEFS and automated task execution, repeatability, and force regulation accuracy is shown to be comparable to using a commercial force sensor for human-in-the-loop feedback.

Modeling spatial navigation in the presence of dynamic obstacles: a differential games approach

Obstacle circumvention strategies can be shaped by the dynamic interaction of an individual (evader) and an obstacle (pursuer). We have developed a mathematical model with predictive and emergent components, using experimental data from seven healthy young adults walking toward a target while avoiding collision with a stationary or moving obstacle (approaching head-on, or diagonally 30° left or right) in a virtual environment. Two linear properties from the predictive component enable the evader to predict the minimum distance between itself and the obstacle at all times, including the future intersection of trajectories. The emergent component uses the classical differential games model to solve for an optimal circumvention while reaching the target, wherein the locomotor strategy is influenced by the obstacle, target, and the evader velocity. Both model components were fitted to a different set of experimental data obtained from five poststroke and healthy participants to derive the minimum predicted distance (predictive component) and obstacle influence dimensions (emergent component) during circumvention. Minimum predicted distance between evader and pursuer was kept constant when the evader was closest to the obstacle in all participants. Obstacle influence dimensions varied depending on obstacle approach condition and preferred side of circumvention, reflecting differences in locomotor strategies between poststroke and healthy individuals. Additionally, important associations between model outputs and observed experimental outcomes were found. The model, supported by experimental data, suggests that both predictive and emergent processes can shape obstacle circumvention strategies in healthy and poststroke individuals.NEW & NOTEWORTHY Obstacle circumvention during goal-directed locomotion is modeled with a new mathematical approach comprising both predictive and emergent elements. The major novelty is using differential games solutions to illustrate the dynamic interactions between the individual as an evader and the approaching obstacle as a pursuer. The model is supported by experimental evidence that explains the behavior along the continuum of locomotor adaptation displayed by healthy subjects and individuals with stroke.