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14th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2017

作者： Jonsson, Porsteinn B. Wang, Jeonghyeon Kim, Jinwhan Laboratory Department of Mechanical Engineering KAIST Daejeon34141 Korea Republic of Robotics Program KAIST Daejeon34141 Korea Republic of

ISBN: (纸本)9781509030552

This paper proposes a method of reconstructing a scalar field by adaptively choosing sampling locations and using the measurements obtained from those locations to reconstruct an estimate of the underlying field using Gaussian process regression. Spreading sampling points evenly over the field may not always be effective if the field is not uniformly distributed and the maximum number of measurements is limited. Taking more measurements in regions of large changes in the field than in regions of small changes can give a better estimate than spreading the same number of measurements evenly over the space. The proposed algorithm was tested on a synthetic scalar field and compared to two popular methods of determining sensor placement based on entropy and mutual information from information theory. © 2017 IEEE.

关键词： Gaussian noise (electronic)

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WE-173. Combining excitability and connectivity measures from interictal invasive-EEG estimates the epileptogenic zone, even in the absence of frank epileptiform activity

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Clinical Neurophysiology 2022年 141卷 S81-S82页

作者： Billardello, Roberto Ntolkeras, Georgios Pesce, Matthew Harmon, Joseph Hadjinicolaou, Aristides Madsen, Joseph Taffoni, Fabrizio Pearl, Phillip Papadelis, Christos Grant, Ellen Tamilia, Eleonora Fetal-Neonatal Neuroimaging and Developmental Science Center Boston Children’s Hospital Harvard Medical School Boston USA Research Unit of Advanced Robotics and Human-Centred Technologies Department of Engineering University Bio-Medico Campus of Rome Rome Italy Baystate Children's Hospital Springfield USA Division of Epilepsy and Clinical Neurophysiology Department of Neurology Boston Children’s Hospital Harvard Medical School Boston USA Epilepsy Surgery Program Department of Neurosurgery Boston Children’s Hospital Harvard Medical School Boston USA Jane and John Justin Neurosciences Center Cook Children’s Health Care System Fort Worth USA Department of Bioengineering University of Texas at Arlington Arlington USA

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Quantification of Extent of Muscle-skin Shifting by Traversal sEMG Analysis Using High-density sEMG Sensor

Quantification of Extent of Muscle-skin Shifting by Traversa...

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IEEE International Conference on Cyborg and Bionic Systems (CBS)

作者： Shotaro Okajima Eduardo Iáñez Hiroshi Yamasaki Álvaro Costa García Fady S. Alnajjar Noriaki Hattori Shingo Shimoda Man-Machine Collaboration Research Team Robotics Project Baton Zone Program Science Technology and Innovation Hub RIKEN Nagoya Japan Brain-Machine Interface Systems Lab Miguel Hernández University of Elche Spain Intelligent Behavior Control Unit BTCC CBS RIKEN Nagoya Japan College of IT (CIT) UAE University (UAEU) Global Center for Medical Engineering and Informatics Osaka University

ISBN: (数字)9781728150734

ISBN: (纸本)9781728150741

Surface electromyography(sEMG) measurement has been an essential approach to analyze human behaviors because we can generally consider that sEMG signals represent the muscle activities as the final output of our nerve system. One of the most serious problems for considering sEMG signal as the muscle activity is the shift of the relative position between muscles and skin depending on a posture. The motion of forearm rotation is the prominent example of muscle-skin shifting depending on postural changes. The sEMG signal from a sensor may represent the different muscle activity when the muscle-skin shifting is happened. In this study, we discuss a method to quantify the muscle-skin shift from the sEMG signals in response to the postural changes. We use the high density sEMG sensor that is possible to measure sEMG signal as the potential map. We proposed the computation algorithm to quantify the amount of muscle-skin shifting based on the change of the sEMG signals in response to the postural changes. We conducted the experiments of wrist extension motions under three different forearm postures: forearm pronation, natural posture and forearm supination. Experimental results from three healthy subjects show that we can quantify the extent of muscle-skin shifting as an angle by using proposed algorithm.

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Towards a soft robotic skin for autonomous tissue palpation

Towards a soft robotic skin for autonomous tissue palpation

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2017 IEEE International Conference on robotics and Automation, ICRA 2017

作者： Campisano, Federico Ozel, Selim Ramakrishnan, Anand Dwivedi, Anany Gkotsis, Nikolaos Onal, Cagdas D. Valdastri, Pietro STORM Lab Department of Mechanical Engineering Vanderbilt University NashvilleTN United States WPI Soft Robotics Lab Department of Mechanical Engineering and Robotics Engineering Program Worcester Polytechnic Institute WorcesterMA United States Department of Robotics and Autonomous System University of Leeds Leeds United Kingdom

ISBN: (纸本)9781509046331

Manual palpation is commonly used to localize tumors and other features buried deep inside organs during open surgery. This approach is not feasible in minimally invasive or robotic surgery, as the contact with the tissue is mediated by instruments. To address this problem, we propose a soft robotic skin (SRS) that can be deployed from a small incision and create a stiffness map in a single step. Such a skin is composed of a matrix of soft robotic tactile elements (SRTEs), each one able to expand and record the tissue response during expansion. In this paper, we firstly prove the feasibility of palpation using a single SRTE. Then, we present and test a soft-suction based anchoring mechanism able to keep the SRS in the desired position in contact with the tissue, allowing surgeons to palpate different sides of the organ. Finally, we detail a calibration method for the SRTE, and assess the feasibility of identifying lumps buried inside a soft tissue phantom, and then inside a chicken liver during an ex-vivo trial. Experimental results show that the SRTE was able to differentiate simulated lumps (up to 3.25 mm deep) from healthy tissue in both the phantom and the ex-vivo trials. These results, added to the ability of the suction gripper to compensate for the expansion forces of each SRTE, are paving the way for soft robotic autonomous tools that can be used for intraoperative mapping of tissue cancers. © 2017 IEEE.

关键词： Tissue

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Development of a Transformable Self-Balancing Mobile Robot

Development of a Transformable Self-Balancing Mobile Robot

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International Conference on Control, Automation and Systems

作者： Jinkwang Kim Geonhoo Kim Taewoo Han Seungwon Heo Yongjae Kim Hyun Myung Department of the Robotics Program KAIST Daejeon Korea Department of Electronics Engineering Korea University of Technology and Education(Koreatech) Cheonan Korea Department of Civil and Environmental Engineering KAIST Daejeon Korea

ISBN: (纸本)9781538670804;9788993215168

This paper reports the design, construction, and control of a mobile robot that can be transformed from the four-wheel mobile robot into two-wheel self-balancing robot and vice versa. The hardware of the robot utilizes the mechanism of the three-link manipulator. This robot is composed of three components;the body part, the middle link, and the top part. The system architecture comprises a pair of DC motor controllers to move the wheel, two servo motors to move the middle link and the top part, and an Arduino microcontroller board, etc. When the robot is transformed to the two-wheel self-balancing robot, the COM (Center of Mass) equation, and the IMU (Inertial Measurement Unit) sensor are employed for attitude determination. The method of the control is based on a proportional-integral-differential (PID) control. The results show the possibility of performing both functions of the four-wheel mobile robot and two-wheel self-balancing robot.

关键词： Three-link manipulator Two-wheel self-balancing robot Center of mass

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WE-184. From “irritative zone” to “irritative network”: a novel interictal estimate of the epileptogenic zone using invasive EEG

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Clinical Neurophysiology 2022年 141卷 S86-S86页

作者： Pesce, Matthew Billardello, Roberto Pace, Lavinia Madsen, Joseph Pearl, Phillip Papadelis, Christos Grant, Ellen Tamilia, Eleonora Fetal-Neonatal Neuroimaging and Developmental Science Center Boston Children's Hospital Boston USA Research Unit of Advanced Robotics and Human-Centered Technologies Department of Engineering University Bio-Medico Campus of Rome Rome Italy Epilepsy Surgery Program Department of Neurosurgery Boston Children’s Hospital Boston USA Harvard Medical School Boston USA Division of Epilepsy and Clinical Neurophysiology Department of Neurology Boston Children’s Hospital Boston USA Jane and John Justin Neurosciences Center Cook Children’s Health Care System Fort Worth USA Department of Bioengineering University of Texas at Arlington Arlington USA Fetal-Neonatal Neuroimaging and Developmental Science Center Boston Children’s Hospital Boston USA

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Privacy-sensitive robotics 12

Privacy-sensitive robotics

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12th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2017

作者： Rueben, Matthew Smart, William D. Grimm, Cindy M. Cakmak, Maya Robotics Program Oregon State University CorvallisOR97331 United States Computer Science and Engineering University of Washington SeattleWA98195 United States

ISBN: (纸本)9781450348850

As robots start to enter our everyday lives, they will bring with them the risk of privacy invasions. Unlike videoconferencing, we might not have control of where the sensors on our robots look, and where the robots go. They might operating in or homes and offices while we are not there, opening the door to an invasion of our privacy that no previous technology has been capable of. How do people think about privacy in terms of robots? Are they worried about their privacy being violated and, if so, in what ways? How does the law view the issues around privacy and robotics? What are the pressing questions that we need to address now, before it's too late. This workshop will bring together researchers from a wide variety of communities to look at these questions, identify others, and help define the new area of Privacy-Sensitive robotics. The workshop will result in a white paper that defines the pressing issues in Privacy-Sensitive robotics, from a variety of perspectives, and will suggest a research agenda to address these problems. © 2017 Authors.

关键词： Human robot interaction

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Online Recurrent Extreme Learning Machine and its Application to Time-series Prediction

Online Recurrent Extreme Learning Machine and its Applicatio...

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International Joint Conference on Neural Networks

作者： Jin-Man Park Jong-Hwan Kim Robotics Program KAIST School of Electrical Engineering KAIST

ISBN: (纸本)9781509061839

Online sequential extreme learning machine (OS-ELM) is an online learning algorithm training single-hidden layer feedforward neural networks (SLFNs), which can learn data one-by-one or chunk-by-chunk with fixed or varying data size. Due to its characteristics of online sequential learning, OS-ELM is popularly used to solve time-series prediction problem, such as stock forecast, weather forecast, passenger count forecast, etc. OS-ELM, however, has two fatal drawbacks: Its input weights cannot be adjusted and it cannot be applied to learn recurrent neural network (RNN). Therefore we propose a modified version of OS-ELM, called online recurrent extreme learning machine (OR-ELM), which is able to adjust input weights and can be applied to learn RNN, by applying ELM-auto-encoder and a normalization method called layer normalization (LN). Proposed method is used to solve a time-series prediction problem on New-York City passenger count dataset, and the results show that R-ELM outperforms OS-ELM and other online-sequential learning algorithms such as hierarchical temporal memory (HTM) and online long short-term memory (online LSTM).

关键词： Online sequential extreme learning machine (OS-ELM) Online recurrent extreme learning machine (OR-ELM) Online learning Time-series prediction Auto-encoding e-learning recurrent neural nets Learning machines normalization method Chunking data size learning algorithms

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CWave: High-performance single-source any-angle path planning on a grid

CWave: High-performance single-source any-angle path plannin...

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2017 IEEE International Conference on robotics and Automation, ICRA 2017

作者： Sinyukov, Dmitry A. Padir, Taskin Robotics Engineering Program Worcester Polytechnic Institute WorcesterMA01609 United States Department of Electrical and Computer Engineering Northeastern University BostonMA02115 United States

ISBN: (纸本)9781509046331

Path planning on a 2D-grid is a well-studied problem in robotics. It usually involves searching for a shortest path between two vertices on a grid. Single-source path planning is a modified problem which asks to find distances from a given point to all other points on the map. A high-performance algorithm for single-source any-angle path planning on a grid that we named CWave is proposed in this work. 'Any-angle' attribute of a path planning algorithm implies that such algorithm can find paths which may include any angle segments, as opposed to standard A © 2017 IEEE.

关键词： Motion planning

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Scalar field reconstruction based on the Gaussian process and adaptive sampling

Scalar field reconstruction based on the Gaussian process an...

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International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

作者： Porsteinn B. Jónsson Jeonghyeon Wang Jinwhan Kim Mobile Robotics and Intelligence (MORIN) Laboratory Departrnent of Mechanical Engineering KAIST Daeieon Korea Robotics Program KAIST Daejeon Korea

关键词： Entropy Mutual information Gaussian processes Training Covariance matrices Training data Simulated annealing

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