Development of a Virtual Laparoscopic Trainer using Accelerometer Augmented Tools to Assess Performance in Surgical training

Previous research suggests that virtual reality (VR) may supplement conventional training in laparoscopy. It may prove useful in the selection of surgical trainees in terms of their dexterity and spatial awareness skills in the near future. Current VR training solutions provide levels of realism and in some instances, haptic feedback, but they are cumbersome by being tethered and not ergonomically close to the actual surgical instruments for weight and freedom of use factors. In addition, they are expensive hence making them less accessible to departments than conventional box trainers. The box trainers in comparison, although more economical, lack tangible feedback and realism for handling delicate tissue structures. We have previously reported on the development of a modified digitally enhanced surgical instrument for laparoscopic training, named the Parkar Tool. This tool contains wireless accelerometer and gyroscopic sensors integrated into actual laparoscopic instruments. By design, it alleviates the need for both tethered and physically different shaped tools thereby enhancing the realism when performing surgical procedures. Additionally the software (Valhalla) has the ability to digitally record surgical motions, thereby enabling it to remotely capture surgical training data to analyse and objectively evaluate performance. We have adapted and further developed our initial single training tool method as used with a laparoscopic pyloromyotomy scenario, to an enhanced method using multiple Parkar wireless tools simultaneously, for use in several different case scenarios. This allows the use and measurement of right and left handed dexterity with the benefit of using several tasks of differing complexity. The development of a 3D tissue-surface deformations solution written in OpenGL gives us several different virtual surgical training scenario approximations to use with the instruments. The trainee can start with learning simple tasks e.g. incising tissue, grasping, squeezing and stretching tissue, to more complex procedures such as suturing, herniotomies, bowel anastomoses, as well as the original pyloromyotomy as used in the first model

QL-BT: Enhancing Behaviour Tree Design and Implementation with Q-Learning

Artificial intelligence has become an increasingly important aspect of computer game technology, as designers attempt to deliver engaging experiences for players by creating characters with behavioural realism to match advances in graphics and physics. Recently, behaviour trees have come to the forefront of games AI technology, providing a more intuitive approach than previous techniques such as hierarchical state machines, which often required complex data structures producing poorly structured code when scaled up. The design and creation of behaviour trees, however, requires experienceand effort. This research introduces Q-learning behaviour trees (QL-BT), a method for the application of reinforcement learning to behaviour tree design. The technique facilitates AI designers' use of behaviour trees by assisting them in identifying the most appropriate moment to execute each branch of AI logic, as well as providing an implementation that can be used to debug, analyse and optimize early behaviour tree prototypes. Initial experiments demonstrate that behaviour trees produced by the QL-BT algorithm effectively integrate RL, automate tree design, and are human-readable

State-to-State Differential and Relative Integral Cross Sections for Rotationally Inelastic Scattering of H2O by Hydrogen

State-to-state differential cross sections (DCSs) for rotationally inelastic scattering of H2O by H2 have been measured at 71.2 meV (574 cm-1) and 44.8 meV (361 cm-1) collision energy using crossed molecular beams combined with velocity map imaging. A molecular beam containing variable compositions of the (J = 0, 1, 2) rotational states of hydrogen collides with a molecular beam of argon seeded with water vapor that is cooled by supersonic expansion to its lowest para or ortho rotational levels (JKaKc= 000 and 101, respectively). Angular speed distributions of fully specified rotationally excited final states are obtained using velocity map imaging. Relative integral cross sections are obtained by integrating the DCSs taken with the same experimental conditions. Experimental state-specific DCSs are compared with predictions from fully quantum scattering calculations on the most complete H2O-H2 potential energy surface. Comparison of relative total cross sections and state-specific DCSs show excellent agreement with theory in almost all detailsComment: 46 page

Conditional Regressive Random Forest Stereo-based Hand Depth Recovery

This paper introduces Conditional Regressive Random Forest (CRRF), a novel method that combines a closed-form Conditional Random Field (CRF), using learned weights, and a Regressive Random Forest (RRF) that employs adaptively selected expert trees. CRRF is used to estimate a depth image of hand given stereo RGB inputs. CRRF uses a novel superpixel-based regression framework that takes advantage of the smoothness of the hand’s depth surface. A RRF unary term adaptively selects different stereo-matching measures as it implicitly determines matching pixels in a coarse-to-fine manner. CRRF also includes a pair-wise term that encourages smoothness between similar adjacent superpixels. Experimental results show that CRRF can produce high quality depth maps, even using an inexpensive RGB stereo camera and produces state-of-the-art results for hand depth estimation

Quantized Census for Stereoscopic Image Matching

Current depth capturing devices show serious drawbacks in certain applications, for example ego-centric depth recovery: they are cumbersome, have a high power requirement, and do not portray high resolution at near distance. Stereo-matching techniques are a suitable alternative, but whilst the idea behind these techniques is simple it is well known that recovery of an accurate disparity map by stereo-matching requires overcoming three main problems: occluded regions causing absence of corresponding pixels; existence of noise in the image capturing sensor and inconsistent color and brightness in the captured images. We propose a modified version of the Census-Hamming cost function which allows more robust matching with an emphasis on improving performance under radiometric variations of the input images

Data-driven Recovery of Hand Depth using Conditional Regressive Random Forest on Stereo Images

Hand pose is emerging as an important interface for human-computer interaction. This paper presents a data-driven method to estimate a high-quality depth map of a hand from a stereoscopic camera input by introducing a novel superpixel based regression framework that takes advantage of the smoothness of the depth surface of the hand. To this end, we introduce Conditional Regressive Random Forest (CRRF), a method that combines a Conditional Random Field (CRF) and a Regressive Random Forest (RRF) to model the mapping from a stereo RGB image pair to a depth image. The RRF provides a unary term that adaptively selects different stereo-matching measures as it implicitly determines matching pixels in a coarse-to-fine manner. While the RRF makes depth prediction for each super-pixel independently, the CRF unifies the prediction of depth by modeling pair-wise interactions between adjacent superpixels. Experimental results show that CRRF can generate a depth image more accurately than the leading contemporary techniques using an inexpensive stereo camera

Hand Pose Estimation Using Deep Stereovision and Markov-chain Monte Carlo

Hand pose is emerging as an important interface for human-computer interaction. The problem of hand pose estimation from passive stereo inputs has received less attention in the literature compared to active depth sensors. This paper seeks to address this gap by presenting a datadriven method to estimate a hand pose from a stereoscopic camera input, by introducing a stochastic approach to propose potential depth solutions to the observed stereo capture and evaluate these proposals using two convolutional neural networks (CNNs). The first CNN, configured in a Siamese network architecture, evaluates how consistent the proposed depth solution is to the observed stereo capture. The second CNN estimates a hand pose given the proposed depth. Unlike sequential approaches that reconstruct pose from a known depth, our method jointly optimizes the hand pose and depth estimation through Markov-chain Monte Carlo (MCMC) sampling. This way, pose estimation can correct for errors in depth estimation, and vice versa. Experimental results using an inexpensive stereo camera show that the proposed system more accurately measures pose better than competing methods

Cumulative and Differential Effects of Early Child Care and Middle Childhood Out-of-School Time on Adolescent Functioning.

Effects associated with early child care and out-of-school time (OST) during middle childhood were examined in a large sample of U.S. adolescents (N = 958). Both higher quality early child care AND more epochs of organized activities (afterschool programs and extracurricular activities) during middle childhood were linked to higher academic achievement at age 15. Differential associations were found in the behavioral domain. Higher quality early child care was associated with fewer externalizing problems, whereas more hours of early child care was linked to greater impulsivity. More epochs of organized activities was associated with greater social confidence. Relations between early child care and adolescent outcomes were not mediated or moderated by OST arrangements in middle childhood, consistent with independent, additive relations of these nonfamilial settings

Two state scattering problem to Multi-channel scattering problem: Analytically solvable model

Starting from few simple examples we have proposed a general method for finding an exact analytical solution for the two state scattering problem in presence of a delta function coupling. We have also extended our model to deal with general one dimensional multi-channel scattering problems

Pulse-driven near-resonant quantum adiabatic dynamics: lifting of quasi-degeneracy

We study the quantum dynamics of a two-level system driven by a pulse that starts near-resonant for small amplitudes, yielding nonadiabatic evolution, and induces an adiabatic evolution for larger amplitudes. This problem is analyzed in terms of lifting of degeneracy for rising amplitudes. It is solved exactly for the case of linear and exponential rising. Approximate solutions are given in the case of power law rising. This allows us to determine approximative formulas for the lineshape of resonant excitation by various forms of pulses such as truncated trig-pulses. We also analyze and explain the various superpositions of states that can be obtained by the Half Stark Chirped Rapid Adiabatic Passage (Half-SCRAP) process.Comment: 21 pages, 12 figure