I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

This paper provides a broad overview of medical robot systems used in surgery. After introducing basic concepts of computer-integrated surgery, surgical CAD/CAM, and surgical assistants, it discusses some of the major design issues particular to medical robots. It then illustrates these issues and the broader themes introduced earlier with examples of current surgical CAD/CAM and surgical assistant systems. Finally, it provides a brief synopsis of current research challenges and closes with a few thoughts on the research/industry/clinician teamwork that is essential for progress in the field.

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Medical robotics in computer-integrated surgery

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Enhancing Our Lives with Immersive Virtual Reality

Purpose of Review
Robot-assisted minimally invasive surgery (RMIS) holds great promise for improving the accuracy and dexterity of a surgeon while minimizing trauma to the patient. However, widespread clinical success with RMIS has been marginal. It is hypothesized that the lack of haptic (force and tactile) feedback presented to the surgeon is a limiting factor. This review explains the technical challenges of creating haptic feedback for robot-assisted surgery and provides recent results that evaluate the effectiveness of haptic feedback in mock surgical tasks.

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Haptic Feedback in Robot-Assisted Minimally Invasive Surgery

Background
Virtual reality (VR) as surgical training tool has become a state-of-the-art technique in training and teaching skills for minimally invasive surgery (MIS). Although intuitively appealing, the true benefits of haptic (VR training) platforms are unknown. Many questions about haptic feedback in the different areas of surgical skills (training) need to be answered before adding costly haptic feedback in VR simulation for MIS training. This study was designed to review the current status and value of haptic feedback in conventional and robot-assisted MIS and training by using virtual reality simulation.

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The value of haptic feedback in conventional and robot-assisted minimal invasive surgery and virtual reality training: a current review

Physical rehabilitation programs frequently begin with a brief stay in the hospital and continue with home-based rehabilitation. Lack of feedback on exercise correctness is a significant issue in home-based rehabilitation. Automated movement quality assessment (MQA) using skeletal movement data (hereafter referred to as skeletal data) collected via depth imaging devices can assist with home-based rehabilitation by providing the necessary quantitative feedback. This paper aims to use recent advances in deep learning to address the problem of MQA. Movement quality score generation is an essential component of MQA. We propose three novel skeletal data augmentation schemes. We show that using the proposed augmentations for generating movement quality scores result in significant performance boosts over existing methods. Finally, we propose a novel transformer based architecture for MQA. Four novel feature extractors are proposed and studied that allow the transformer network to operate on skeletal data. We show that adding the attention mechanism in the design of the proposed feature extractor allows the transformer network to pay attention to specific body parts that make a significant contribution towards executing a movement. We report an improvement in movement quality score prediction of 12% on UI-PRMD dataset and 21% on KIMORE dataset compared to the existing methods.

A Robust and Scalable Attention Guided Deep Learning Framework for Movement Quality Assessment

The interconnection between vision and somatosensation is already well-established and is further supplemented by the evolutionary link between eyes and photoreceptors, and the functional connection between photosensation and thermoreception. However, our analysis hypothesizes a possibility that vision is not just linked to somatosensation, but may not exist without somatosensation. Surprisingly, our photoreceptor itself needs somatosensory proteins for its functioning, and our entire visual pathway depends on somatosensory cues for its functioning.

Manivannan Muniyandi

Papers

A Robust and Scalable Attention Guided Deep Learning Framework for Movement Quality Assessment

Can Vision Exist Without Somatosensation

Effect of Subthreshold Vibration on the Perception of Electrovibration

Objective Quantification of Circular Vection in Immersive Environments

Grasp Intent Detection Using Multi Sensorial Data