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IEEE International Symposium on Robot and Human Interactive Communication

作者： Mahdieh Nejati Brenna D. Argall McCormick School of Engineering Master of Science in Robotics Program Northwestern University Evanston IL USA Department of Electrical Engineering and Computer Science Northwestern University Evanston IL USA

ISBN: (纸本)9781509039302

This work presents an algorithm for automated real-time ramp detection using 3D point cloud data in the context of shared-control powered wheelchairs. Limitations in the interfaces available to those with severe motor impairments can make basic maneuvering tasks with powered wheelchairs difficult. Although a significant amount of work has been done on obstacle detection and avoidance, much less attention has been given to algorithms for the safe and reliable detection of ramps and inclines;even though navigating these structures is an important part of urban life. We provide an algorithmic solution for accurately detecting traversable inclines for applications with powered wheelchairs using the Point Cloud Library (PCL) within the robotics Operating System (ROS) framework. All algorithms are implemented first in simulation and later evaluated on data obtained from indoor and outdoor urban environments. We measure the performance of our algorithm with systematic testing on several different ramp structures, observed from varied viewpoints. Results show that our algorithm is successful in detecting the orientation, slope, and width of traversable ramps with up to 100% accuracy and an average detection accuracy of 88%.

关键词： Wheelchairs Mobile robots Three-dimensional displays Robot sensing systems Navigation Image edge detection three-dimensional displays Manipulator sensing systems Edge detection Wheelchair Mobile robots navigation RAMP Inclined Ramps physical impairment algorithms Town life Obstacle detection Traversable urban environment

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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Design and validation of a high-order weighted-frequency fourier linear combiner-based Kalman filter for parkinsonian tremor estimation

Design and validation of a high-order weighted-frequency fou...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Y. Zhou M. E. Jenkins M. D. Naish A. L. Trejos Biomedical Engineering Program and the Department of Electrical and Computer Engineering (ECE) Western University Movement Disorders Program Clinical Neurological Sciences Western University Canadian Surgical Technologies and Advanced Robotics (CSTAR) Western University CSTAR and ECE Western University

The design of a tremor estimator is an important part of designing mechanical tremor suppression orthoses. A number of tremor estimators have been developed and applied with the assumption that tremor is a mono-frequency signal. However, recent experimental studies have shown that Parkinsonian tremor consists of multiple frequencies, and that the second and third harmonics make a large contribution to the tremor. Thus, the current estimators may have limited performance on estimation of the tremor harmonics. In this paper, a high-order tremor estimation algorithm is proposed and compared with its lower-order counterpart and a widely used estimator, the Weighted-frequency Fourier Linear Combiner (WFLC), using 18 Parkinsonian tremor data sets. The results show that the proposed estimator has better performance than its lower-order counterpart and the WFLC. The percentage estimation accuracy of the proposed estimator is 85±2.9%, an average improvement of 13% over the lower-order counterpart. The proposed algorithm holds promise for use in wearable tremor suppression devices.

关键词： Harmonic analysis Estimation Power harmonic filters Band-pass filters Pathology Frequency estimation Physiology

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BLISS: Using Robot in Learning Intervention to Promote Social Skills for Autism Therapy 2016

BLISS: Using Robot in Learning Intervention to Promote Socia...

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Proceedings of the international Convention on Rehabilitation engineering & Assistive Technology

作者： Settapon Santatiwongchai Boonserm Kaewkamnerdpong Wisanu Jutharee Kajornvut Ounjai Department of Computer Engineering Faculty of Engineering King Mongkut's University of Technology Thonburi Bangkok Thailand Biological Engineering Program Faculty of Engineering King Mongkut's University of Technology Thonburi Bangkok Thailand Institute of Field Robotics King Mongkut's University of Technology Thonburi Bangkok Thailand

Robots have been introduced for autism therapy. However, most robotics studies focused on providing means of practice with dyadic interaction activities. To live in the society, autistic children need social interaction skills. In this study, we developed a mobile robot with non-threatening facial expression and investigated the potential of using this robot in triadic interaction activity for autism therapy to promote social interaction in children with autism. From the experiment that subjects play a picture matching game, it can be indicated that the robot can attract children with autism. Subjects can learn and understand the rule with their parents' participation. They could learn the name of the object in the picture that they do not know before as well. Some parents found that the robot could aid them in training their children. Autism specialists discovered some capabilities that they have never observed from the subjects in their therapeutic sessions. Therefore, this robot could be useful for autism specialists in finding aptitudes of children with autism in order to provide suitable intervention for effective therapy. This robot could be helpful for parents/caregivers in conducting therapeutic activities at home (continuing after therapeutic session with specialists) for even more effective therapy.

关键词： Mobile Robot Autism Non-Humanoid Robot Social Interaction

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User-adaptive fall detection for patients using wristband

User-adaptive fall detection for patients using wristband

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2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Young-Hoon Nho Jong Gwan Lim Dae-Eon Kim Dong-Soo Kwon Robotics Program and Human-Robot Interaction research center Korea Advanced Institute of Science and Technology Daejeon Republic of Korea Department of Information Engineering Computer Science and University of Trento Italy KAIST Human-Robot Interaction Research Center Daejeon Republic of Korea Department of Mechanical Engineering Human-Robot Interaction research center Daejeon Republic of Korea

Fall detection systems have been proposed to prevent additional injuries following fall accidents. This paper introduces an easily learnable fall detection system based on the data of an individual patient in a hospital room. The improvement of low performance using a single accelerometer at wrists and the inconvenience of sensor attached to a waist in the conventional approach was concentrated on by integrating heart rate signals to the conventional acceleration approach and changing the sensor location from a waist to wrists. As for the optimal heart rate feature selection, we proposed a four-feature vector combination (root mean square of successive differences, standard deviation of successive differences, normal to normal 50, normal to normal 20) with correlation and mutual information analysis in addition to mean absolute deviation selected as an accelerometer feature. To easily acquire and train the patients' fall data, our system was based on unsupervised learning approaches using Gaussian mixture models for optimal classifiers with the optimal cluster number decided by cluster validation index of square error sum. A 10-fold cross validation was applied for a final performance evaluation where each threshold for separating fall state from non-fall state was automatically decided in several comparison groups, which were created on the basis of fusion timing and used sensors. As a result, despite sensors attached to the wrist, the wearable inconvenience of the conventional is overcome using the feature-level fused approach between heart rates and accelerations with the accuracy up to 98.39 %, which is closest to 99.34 % of the case using a single accelerometer located at the waist.

关键词： Accelerometers Heart rate Wrist Training data Hospitals Robot sensing systems Unsupervised learning

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Multi-crease self-folding by global heating

Multi-crease self-folding by global heating

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作者： Miyashita, Shuhei Onal, Cagdas D. Rus, Daniela Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology 32 Vassar Street 32-379 CambridgeMA02139 United States Worcester Polytechnic Institute Department of Mechanical Engineering Robotics Engineering Program 100 Institute Rd. HL 105 WorcesterMA01609 United States

This study demonstrates a new approach to autonomous folding for the body of a 3D robot from a 2D sheet, using heat. We approach this challenge by folding a 0.27-mm sheetlike material into a structure. We utilize the thermal deformation of a contractive sheet sandwiched by rigid structural layers. During this baking process, the heat applied on the entire sheet induces contraction of the contracting layer and thus forms an instructed bend in the sheet. To attain the targeted folding angles, the V-fold spans method is used. The targeted angle θout can be kinematically encoded into crease geometry. The realization of this angle in the folded structure can be approximately controlled by a contraction angle θin. The process is non-reversible, is reliable, and is relatively fast. Our method can be applied simultaneously to all the folds in multi-crease origami structures. We demonstrate the use of this method to create a lightweight mobile robot. © 2015 Massachusetts Institute of Technology.

关键词： Self assembly

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Objects tracking of the mobile robot using the hybrid visual servoing

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Journal of Institute of Control, robotics and Systems 2015年第8期21卷 781-787页

作者： Park, Kang-I.L. Woo, Chang-Jun Lee, Jangmyung Department of Interdisciplinary Program in Robotics Pusan National University Korea Republic of Department of Electronic and Electric and Computer Engineering Pusan National University Korea Republic of Department of Electronic Engineering Pusan National University Korea Republic of

This paper proposes a hybrid visual servoing algorithm for the object tracking by a mobile robot with the stereo camera. The mobile robot with the stereo camera performs an object recognition and object tracking using the SIFT and CAMSHIFT algorithms for the hybrid visual servoing. The CAMSHIFT algorithm using stereo camera images has been used to obtain the three-dimensional position and orientation of the mobile robot. With the hybrid visual servoing, a stable balance control has been realized by a control system which calculates a desired angle of the center of gravity whose location depends on variations of link rotation angles of the manipulator. A PID controller algorithm has adopted in this research for the control of the manipulator since the algorithm is simple to design and it does not require unnecessary complex dynamics. To demonstrate the control performance of the hybrid visual servoing, real experiments are performed using the mobile manipulator system developed for this research. © ICROS 2015.

关键词： Mobile robots

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A six-degree-of-freedom robotic system for lower extremity rehabilitation

A six-degree-of-freedom robotic system for lower extremity r...

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IEEE International Conference on Rehabilitation robotics (ICORR)

作者： Aaron Yurkewich S. Farokh Atashzar Ahmed Ayad Rajni V. Patel Canadian Surgical Technologies and Advanced Robotics (CSTAR) London ON Canada Department of Electrical & Computer Engineering The University of Western Ontario Mcchatronic Systems Engineering Program The University of Western Ontario

ISBN: (纸本)9781479918096

This paper describes the design, control and implementation of a sensorized robotic platform for versatile rehabilitation of stroke patients living with lower extremity neuromuscular deficit. The proposed device is composed of a six-degree-of-freedom actuation mechanism with a large workspace for lower extremity rehabilitation regimens. With a small footprint, lightweight, and low-cost design and a wireless interface this device is portable and well-suited for at-home and in-clinic use. A custom six-degree-of-freedom force/torque sensor was developed to measure real-time patient forces, and an admittance controller was implemented to provide assistive motion therapy. The results obtained show the suitability of this device for human-robot interaction for the implementation of lower extremity rehabilitation therapy.

关键词： Robot sensing systems Force Robot kinematics Medical treatment Extremities Torque

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25th Annual Computational Neuroscience Meeting CNS-2016, Seogwipo City, South Korea, July 2-7, 2016 Abstracts

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BMC NEUROSCIENCE 2016年第1期17卷 1-112页

作者： [Anonymous] Computational Neurobiology Laboratory The Salk Institute for Biological Studies San Diego USA UNIC CNRS Gif sur Yvette France The European Institute for Theoretical Neuroscience (EITN) Paris France ATR Computational Neuroscience Laboratories Kyoto Japan Krembil Research Institute University Health Network Toronto Canada Department of Physiology University of Toronto Toronto Canada Department of Medicine (Neurology) University of Toronto Toronto Canada Department of Physics University of New Hampshire Durham USA Department of Neurophysiology Nencki Institute of Experimental Biology Warsaw Poland Department of Theory Wigner Research Centre for Physics of the Hungarian Academy of Sciences Budapest Hungary Department of Mathematical Sciences KAIST Daejoen Republic of Korea Department of Mathematics University of Houston Houston USA Department of Biochemistry & Cell Biology and Institute of Biosciences and Bioengineering Rice University Houston USA Department of Biology and Biochemistry University of Houston Houston USA Grupo de Neurocomputación Biológica Dpto. de Ingeniería Informática Escuela Politécnica Superior Universidad Autónoma de Madrid Madrid Spain Department of Biological Sciences University of Southern California Los Angeles USA Center for Neuroscience Korea Institute of Science and Technology Seoul South Korea Department of Neurology Albert Einstein College of Medicine Bronx USA Center for Neuroscience KIST Seoul South Korea Department of Neuroscience University of Science and Technology Daejon South Korea Systems Neuroscience Group QIMR Berghofer Medical Research Institute Herston Australia Department of Psychology Yonsei University Seoul South Korea Department of Psychiatry Kyung Hee University Hospital at Gangdong Seoul South Korea Department of Psychiatry Veterans Administration Boston Healthcare System and Harvard Medical School Brockton USA Department of Electrical and Electronic Engineering The University of Melbourne Parkvil

A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni

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