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1. Experimental Evaluation of a 3-Armed 6-DOF Parallel Robot for Femur Fracture Surgery

   https://doi.org/10.1142/S2424905X22410094  

   Alruwaili, Fayez; Saeedi-Hosseiny, Marzieh S.; Clancy, Michael; McMillan, Sean; Iordachita, Iulian I.; Abedin-Nasab, Mohammad H. (December 2022, Journal of Medical Robotics Research)

   This paper presents the experimental position and force testing of a 3-armed 6-DOF Parallel Robot, Robossis, that is specifically designed for the application of long-bone femur fracture surgery. Current surgical techniques require a significant amount of time and effort to restore the fractured femur fragments’ length, alignment and rotation. To address these issues, the Robossis system will facilitate the femur fracture surgical procedure and oppose the large traction forces/torques of the muscle groups surrounding the femur. As such, Robossis would subsequently improve patient outcomes by eliminating intraoperative injuries, reducing radiation exposure from X-rays during surgery and decreasing the likelihood of follow-up operations. Specifically, in this paper, we study the accuracy of the Robossis system while moving in the operational workspace under free and simulated traction loads of ([Formula: see text]–1100[Formula: see text]N). Experimental testing in this study demonstrates that Robossis can reach the most extreme points in the workspace, as defined by the theoretical workspace, while maintaining minimal deviation from those points with an average deviation of 0.324[Formula: see text]mm. Furthermore, the force testing experiment shows that Robossis can counteract loads that are clinically relevant to restoring the fractured femur fragments’ length, alignment and rotation. In addition, we study the accuracy of Robossis motion while coupled with the master controller Sigma 7. The results show that Robossis can follow the desired trajectory in real-time with an average error of less than 1[Formula: see text]mm. To conclude, these results further establish the ability of the Robossis system to facilitate the femur fracture surgical procedure and eliminate limitations faced with the current surgical techniques. 

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2. Concurrent Prediction of Finger Forces Based on Source Separation and Classification of Neuron Discharge Information

   https://doi.org/10.1142/S0129065721500106  

   Zheng, Yang; Hu, Xiaogang (April 2021, International Journal of Neural Systems)

   null (Ed.)

   A reliable neural-machine interface is essential for humans to intuitively interact with advanced robotic hands in an unconstrained environment. Existing neural decoding approaches utilize either discrete hand gesture-based pattern recognition or continuous force decoding with one finger at a time. We developed a neural decoding technique that allowed continuous and concurrent prediction of forces of different fingers based on spinal motoneuron firing information. High-density skin-surface electromyogram (HD-EMG) signals of finger extensor muscle were recorded, while human participants produced isometric flexion forces in a dexterous manner (i.e. produced varying forces using either a single finger or multiple fingers concurrently). Motoneuron firing information was extracted from the EMG signals using a blind source separation technique, and each identified neuron was further classified to be associated with a given finger. The forces of individual fingers were then predicted concurrently by utilizing the corresponding motoneuron pool firing frequency of individual fingers. Compared with conventional approaches, our technique led to better prediction performances, i.e. a higher correlation ([Formula: see text] versus [Formula: see text]), a lower prediction error ([Formula: see text]% MVC versus [Formula: see text]% MVC), and a higher accuracy in finger state (rest/active) prediction ([Formula: see text]% versus [Formula: see text]%). Our decoding method demonstrated the possibility of classifying motoneurons for different fingers, which significantly alleviated the cross-talk issue of EMG recordings from neighboring hand muscles, and allowed the decoding of finger forces individually and concurrently. The outcomes offered a robust neural-machine interface that could allow users to intuitively control robotic hands in a dexterous manner. 

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3. Impact of gaps on the flow statistics in an emergent rigid canopy

   https://doi.org/10.1063/5.0088527  

   Ranjan, Pallav; Mittal, Ketan; Chamorro, Leonardo P.; Tinoco, Rafael O. (June 2022, Physics of Fluids)

   High-resolution large eddy simulations and complementary laboratory experiments using particle image velocimetry were performed to provide a detailed quantitative assessment of flow response to gaps in cylinder arrays. The base canopy consists of a dense array of emergent rigid cylinders placed in a regular staggered pattern. The gaps varied in length from [Formula: see text] to 24, in intervals of 4 d, where d is the diameter of the cylinders. The analysis was performed under subcritical conditions with Froude numbers [Formula: see text] and bulk Reynolds numbers [Formula: see text]. Results show that the gaps affect the flow statistics at the upstream and downstream proximity of the canopy. The affected zone was [Formula: see text] for the mean flow and [Formula: see text] for the second-order statistics. Dimensionless time-averaged streamwise velocity within the gap exhibited minor variability with gap spacing; however, in-plane turbulent kinetic energy, k, showed a consistent decay rate when normalized with that at [Formula: see text] from the beginning of the gap. The emergent canopy acts as a passive turbulence generator for the gap flow for practical purposes. The streamwise dependence of k follows an exponential trend within [Formula: see text] and transitions to a power-law at [Formula: see text]. The substantially lower maximum values of k within the gap compared to k within the canopy evidence a limitation of gap measurements representative of canopy flow statistics. We present a base framework for estimating representative in-canopy statistics from measurements in the gap. 

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4. A Reactive Autonomous Camera System for the RAVEN II Surgical Robot

   https://doi.org/10.1109/ISMR48331.2020.9312934  

   Hutchinson, Kay; Yasar, Mohammad Samin; Bhatia, Harshneet; Alemzadeh, Homa (November 2020, 2020 International Symposium on Medical Robotics (ISMR))

   null (Ed.)

   The endoscopic camera of a surgical robot pro- vides surgeons with a magnified 3D view of the surgical field, but repositioning it increases mental workload and operation time. Poor camera placement contributes to safety-critical events when surgical tools move out of the view of the camera. This paper presents a proof of concept of an autonomous camera system for the Raven II surgical robot that aims to reduce surgeon workload and improve safety by providing an optimal view of the workspace showing all objects of interest. This system uses transfer learning to localize and classify objects of interest within the view of a stereoscopic camera. The positions and centroid of the objects are estimated and a set of control rules determines the movement of the camera towards a more desired view. Our perception module had an accuracy of 61.21% overall for identifying objects of interest and was able to localize both graspers and multiple blocks in the environment. Comparison of the commands proposed by our system with the desired commands from a survey of 13 participants indicates that the autonomous camera system proposes appropriate movements for the tilt and pan of the camera. 

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5. Shear thickening in dense bidisperse suspensions

   https://doi.org/10.1122/8.0000495  

   Malbranche, Nelya; Chakraborty, Bulbul; Morris, Jeffrey F. (January 2023, Journal of Rheology)

   Discrete-particle simulations of bidisperse shear thickening suspensions are reported. The work considers two packing parameters, the large-to-small particle radius ratio ranging from [Formula: see text] (nearly monodisperse) to [Formula: see text], and the large particle fraction of the total solid loading with values [Formula: see text], 0.5, and 0.85. Particle-scale simulations are performed over a broad range of shear stresses using a simulation model for spherical particles accounting for short-range lubrication forces, frictional interaction, and repulsion between particles. The variation of rheological properties and the maximum packing fraction [Formula: see text] with shear stress [Formula: see text] are reported. At a fixed volume fraction [Formula: see text], bidispersity decreases the suspension relative viscosity [Formula: see text], where [Formula: see text] is the suspension viscosity and [Formula: see text] is the suspending fluid viscosity, over the entire range of shear stresses studied. However, under low shear stress conditions, the suspension exhibits an unusual rheological behavior: the minimum viscosity does not occur as expected at [Formula: see text], but instead decreases with further increase of [Formula: see text] to [Formula: see text]. The second normal stress difference [Formula: see text] acts similarly. This behavior is caused by particles ordering into a layered structure, as is also reflected by the zero slope with respect to time of the mean-square displacement in the velocity gradient direction. The relative viscosity [Formula: see text] of bidisperse rate-dependent suspensions can be predicted by a power law linking it to [Formula: see text], [Formula: see text] in both low and high shear stress regimes. The agreement between the power law and experimental data from literature demonstrates that the model captures well the effect of particle size distribution, showing that viscosity roughly collapses onto a single master curve when plotted against the reduced volume fraction [Formula: see text]. 

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