A Survey on the Status of Smart Healthcare from the Universal Village Perspective

This survey paper discusses the condition of smart healthcare implementation. It discusses the current healthcare problems and how smart healthcare technologies ease the problems. Our group, Universal Village, realizes that the integration and interaction between parties in a system will maximize the effectiveness and benefit for the system. Based on this idea, this paper considers the smart city system as a whole, and talks about how smart healthcare interacts with infrastructures and functions inside and outside of the smart healthcare field. Then, it analyzes how a more powerful integrated system can be built from the smart healthcare system. In the end, several case studies are listed. Based on our analysis and the case studies, this paper then ended with the future prospects of the smart healthcare.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Collision-Aware Fast Simulation for Soft Robots by Optimization-Based Geometric Computing

Soft robots can safely interact with environments because of their mechanical compliance. Self-collision is also employed in the modern design of soft robots to enhance their performance during different tasks. However, developing an efficient and reliable simulator that can handle the collision response well, is still a challenging task in the research of soft robotics. This paper presents a collision-aware simulator based on geometric optimization, in which we develop a highly efficient and realistic collision checking / response model incorporating a hyperelastic material property. Both actuated deformation and collision response for soft robots are formulated as geometry-based objectives. The collision-free body of a soft robot can be obtained by minimizing the geometry-based objective function. Unlike the FEA-based physical simulation, the proposed pipeline performs a much lower computational cost. Moreover, adaptive remeshing is applied to achieve the improvement of the convergence when dealing with soft robots that have large volume variations. Experimental tests are conducted on different soft robots to verify the performance of our approach

Geometry-based Direct Simulation for Multi-Material Soft Robots

Robots fabricated by soft materials can provide higher flexibility and thus better safety while interacting with natural objects with low stiffness such as food and human beings. However, as many more degrees of freedom are introduced, the motion simulation of a soft robot becomes cumbersome, especially when large deformations are presented. Moreover, when the actuation is defined by geometry variation, it is not easy to obtain the exact loads and material properties to be used in the conventional methods of deformation simulation. In this paper, we present a direct approach to take the geometric actuation as input and compute the deformed shape of soft robots by numerical optimization using a geometry-based algorithm. By a simple calibration, the properties of multiple materials can be modeled geometrically in the framework. Numerical and experimental tests have been conducted to demonstrate the performance of our approach on both cable-driven and pneumatic actuators in soft robotics

Spring-IMU Fusion Based Proprioception for Feedback Control of Soft Manipulators

This paper presents a novel framework to realize proprioception and closed-loop control for soft manipulators. Deformations with large elongation and large bending can be precisely predicted using geometry-based sensor signals obtained from the inductive springs and the inertial measurement units (IMUs) with the help of machine learning techniques. Multiple geometric signals are fused into robust pose estimations, and a data-efficient training process is achieved after applying the strategy of sim-to-real transfer. As a result, we can achieve proprioception that is robust to the variation of external loading and has an average error of 0.7% across the workspace on a pneumatic-driven soft manipulator. The realized proprioception on soft manipulator is then contributed to building a sensor-space based algorithm for closed-loop control. A gradient descent solver is developed to drive the end-effector to achieve the required poses by iteratively computing a sequence of reference sensor signals. A conventional controller is employed in the inner loop of our algorithm to update actuators (i.e., the pressures in chambers) for approaching a reference signal in the sensor-space. The systematic function of closed-loop control has been demonstrated in tasks like path following and pick-and-place under different external loads

Transcribing Latin Manuscripts in Respect to Linguistics

Current text detection software, although can transcribe modern languages with high accuracy, has flaws detecting texts and transcribing original Latin manuscripts sufficiently. This paper proposes a general approach for transcribing Latin manuscripts in respect to linguistics and develops a system to transcribe Latin manuscripts containing intricate abbreviations, which combines basic object detection algorithms with linguistics. We used methods from image processing and made changes based on the characteristics of Latin.This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

OpenPneu: Compact platform for pneumatic actuation with multi-channels

This paper presents a compact system, OpenPneu, to support the pneumatic actuation for multi-chambers on soft robots. Micro-pumps are employed in the system to generate airflow and therefore no extra input as compressed air is needed. Our system conducts modular design to provide good scalability, which has been demonstrated on a prototype with ten air channels. Each air channel of OpenPneu is equipped with both the inflation and the deflation functions to provide a full range pressure supply from positive to negative with a maximal flow rate at 1.7 L/min. High precision closed-loop control of pressures has been built into our system to achieve stable and efficient dynamic performance in actuation. An open-source control interface and API in Python are provided. We also demonstrate the functionality of OpenPneu on three soft robotic systems with up to 10 chambers

Efficient Jacobian-Based Inverse Kinematics With Sim-to-Real Transfer of Soft Robots by Learning

This paper presents an efficient learning-based method to solve the inverse kinematic (IK) problem on soft robots with highly non-linear deformation. The major challenge of efficiently computing IK for such robots is due to the lack of analytical formulation for either forward or inverse kinematics. To address this challenge, we employ neural networks to learn both the mapping function of forward kinematics and also the Jacobian of this function. As a result, Jacobian-based iteration can be applied to solve the IK problem. A sim-to-real training transfer strategy is conducted to make this approach more practical. We first generate a large number of samples in a simulation environment for learning both the kinematic and the Jacobian networks of a soft robot design. Thereafter, a sim-to-real layer of differentiable neurons is employed to map the results of simulation to the physical hardware, where this sim-to-real layer can be learned from a very limited number of training samples generated on the hardware. The effectiveness of our approach has been verified on pneumatic-driven soft robots for path following and interactive positioning

Unifying Vision, Text, and Layout for Universal Document Processing

We propose Universal Document Processing (UDOP), a foundation Document AI model which unifies text, image, and layout modalities together with varied task formats, including document understanding and generation. UDOP leverages the spatial correlation between textual content and document image to model image, text, and layout modalities with one uniform representation. With a novel Vision-Text-Layout Transformer, UDOP unifies pretraining and multi-domain downstream tasks into a prompt-based sequence generation scheme. UDOP is pretrained on both large-scale unlabeled document corpora using innovative self-supervised objectives and diverse labeled data. UDOP also learns to generate document images from text and layout modalities via masked image reconstruction. To the best of our knowledge, this is the first time in the field of document AI that one model simultaneously achieves high-quality neural document editing and content customization. Our method sets the state-of-the-art on 8 Document AI tasks, e.g., document understanding and QA, across diverse data domains like finance reports, academic papers, and websites. UDOP ranks first on the leaderboard of the Document Understanding Benchmark.Comment: CVPR 202

New Perspectives on Roles of Alpha-Synuclein in Parkinson’s Disease

Parkinson’s disease (PD) is one of the synucleinopathies spectrum of disorders typified by the presence of intraneuronal protein inclusions. It is primarily composed of misfolded and aggregated forms of alpha-synuclein (α-syn), the toxicity of which has been attributed to the transition from an α-helical conformation to a β-sheetrich structure that polymerizes to form toxic oligomers. This could spread and initiate the formation of “LB-like aggregates,” by transcellular mechanisms with seeding and subsequent permissive templating. This hypothesis postulates that α-syn is a prion-like pathological agent and responsible for the progression of Parkinson’s pathology. Moreover, the involvement of the inflammatory response in PD pathogenesis has been reported on the excessive microglial activation and production of pro-inflammatory cytokines. At last, we describe several treatment approaches that target the pathogenic α-syn protein, especially the oligomers, which are currently being tested in advanced animal experiments or are already in clinical trials. However, there are current challenges with therapies that target α-syn, for example, difficulties in identifying varying α-syn conformations within different individuals as well as both the cost and need of long-duration large trials