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Jung Kim

Bio: Jung Kim is an academic researcher from KAIST. The author has contributed to research in topics: Materials science & Haptic technology. The author has an hindex of 31, co-authored 249 publications receiving 5202 citations. Previous affiliations of Jung Kim include Incheon National University & Massachusetts Institute of Technology.


Papers
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Journal ArticleDOI
16 Nov 2000-Nature
TL;DR: The results suggest that long-term control of complex prosthetic robot arm movements can be achieved by simple real-time transformations of neuronal population signals derived from multiple cortical areas in primates.
Abstract: Signals derived from the rat motor cortex can be used for controlling one-dimensional movements of a robot arm1. It remains unknown, however, whether real-time processing of cortical signals can be employed to reproduce, in a robotic device, the kind of complex arm movements used by primates to reach objects in space. Here we recorded the simultaneous activity of large populations of neurons, distributed in the premotor, primary motor and posterior parietal cortical areas, as non-human primates performed two distinct motor tasks. Accurate real-time predictions of one- and three-dimensional arm movement trajectories were obtained by applying both linear and nonlinear algorithms to cortical neuronal ensemble activity recorded from each animal. In addition, cortically derived signals were successfully used for real-time control of robotic devices, both locally and through the Internet. These results suggest that long-term control of complex prosthetic robot arm movements can be achieved by simple real-time transformations of neuronal population signals derived from multiple cortical areas in primates.

1,435 citations

Journal ArticleDOI
Pilwon Heo1, Gwang Min Gu1, Soojin Lee2, Kyehan Rhee2, Jung Kim1 
TL;DR: A comprehensive review of hand exoskeleton technologies for rehabilitation and assistive engineering, from basic hand biomechanics to actuator technologies, is presented in this paper, where the main requirements of these hand ex-oskeleton devices are also identified and the mechanical designs of existing devices are classified.
Abstract: In this paper, we present a comprehensive review of hand exoskeleton technologies for rehabilitation and assistive engineering, from basic hand biomechanics to actuator technologies. Because of rapid advances in mechanical designs and control algorithms for electro-mechanical systems, exoskeleton devices have been developed significantly, but are still limited to use in larger body areas such as upper and lower limbs. However, because of their requirements for smaller size and rich tactile sensing capabilities, hand exoskeletons still face many challenges in many technical areas, including hand biomechanics, neurophysiology, rehabilitation, actuators and sensors, physical human-robot interactions and ergonomics. This paper reviews the state-of-the-art of active hand exoskeletons for applications in the areas of rehabilitation and assistive robotics. The main requirements of these hand exoskeleton devices are also identified and the mechanical designs of existing devices are classified. The challenges facing an active hand exoskeleton robot are also discussed.

432 citations

Journal ArticleDOI
TL;DR: This work discusses important aspects of haptics in MISST, such as haptic rendering and haptic recording and playback, and discusses the importance of net forces resulting from tool-tissue interactions in surgery.
Abstract: Haptics is a valuable tool in minimally invasive surgical simulation and training. We discuss important aspects of haptics in MISST, such as haptic rendering and haptic recording and playback. Minimally invasive surgery has revolutionized many surgical procedures over the last few decades. MIS is performed using a small video camera, a video display, and a few customized surgical tools. In procedures such as gall bladder removal (laparoscopic cholesystectomy), surgeons insert a camera and long slender tools into the abdomen through small skin incisions to explore the internal cavity and manipulate organs from outside the body as they view their actions on a video display. Because the development of minimally invasive techniques has reduced the sense of touch compared to open surgery, surgeons must rely more on the feeling of net forces resulting from tool-tissue interactions and need more training to successfully operate on patients.

373 citations

Journal ArticleDOI
Hyonyoung Han1, Jung Kim1
TL;DR: A least mean square based active noise cancellation method is applied to the accelerometer data and shows that the proposed method recovers pulse from PPGs efficiently.

149 citations

Journal ArticleDOI
11 Jul 2018-ACS Nano
TL;DR: This work presents an all-solution processable pressure insensitive strain sensor that utilizes the difference in structural change upon the application of pressure and tensile strain to differentiate between shear stress and normal pressure.
Abstract: Tactile sensors that can mechanically decouple, and therefore differentiate, various tactile inputs are highly important to properly mimic the sensing capabilities of human skin. Herein, we present an all-solution processable pressure insensitive strain sensor that utilizes the difference in structural change upon the application of pressure and tensile strain. Under the application of strain, microcracks occur within the multiwalled carbon nanotube (MWCNT) network, inducing a large change in resistance with gauge factor of ∼56 at 70% strain. On the other hand, under the application of pressure to as high as 140 kPa, negligible change in resistance is observed, which can be attributed to the pressure working primarily to close the pores, and hence minimally changing the MWCNT network conformation. Our sensor can easily be coated onto irregularly shaped three-dimensional objects (e.g., robotic hand) via spray coating, or be attached to human joints, to detect bending motion. Furthermore, our sensor can dif...

142 citations


Cited by
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Journal ArticleDOI
TL;DR: With adequate recognition and effective engagement of all issues, BCI systems could eventually provide an important new communication and control option for those with motor disabilities and might also give those without disabilities a supplementary control channel or a control channel useful in special circumstances.

6,803 citations

Journal ArticleDOI
02 Apr 2004-Science
TL;DR: A method for learning nonlinear systems, echo state networks (ESNs), which employ artificial recurrent neural networks in a way that has recently been proposed independently as a learning mechanism in biological brains is presented.
Abstract: We present a method for learning nonlinear systems, echo state networks (ESNs). ESNs employ artificial recurrent neural networks in a way that has recently been proposed independently as a learning mechanism in biological brains. The learning method is computationally efficient and easy to use. On a benchmark task of predicting a chaotic time series, accuracy is improved by a factor of 2400 over previous techniques. The potential for engineering applications is illustrated by equalizing a communication channel, where the signal error rate is improved by two orders of magnitude.

3,122 citations

Journal ArticleDOI
TL;DR: The brain's electrical signals enable people without muscle control to physically interact with the world through the use of their brains' electrical signals.
Abstract: The brain's electrical signals enable people without muscle control to physically interact with the world.

2,361 citations

Journal ArticleDOI
TL;DR: It is demonstrated that primates can learn to reach and grasp virtual objects by controlling a robot arm through a closed-loop brain–machine interface (BMIc) that uses multiple mathematical models to extract several motor parameters from the electrical activity of frontoparietal neuronal ensembles.
Abstract: Reaching and grasping in primates depend on the coordination of neural activity in large frontoparietal ensembles. Here we demonstrate that primates can learn to reach and grasp virtual objects by controlling a robot arm through a closed-loop brain–machine interface (BMIc) that uses multiple mathematical models to extract several motor parameters (i.e., hand position, velocity, gripping force, and the EMGs of multiple arm muscles) from the electrical activity of frontoparietal neuronal ensembles. As single neurons typically contribute to the encoding of several motor parameters, we observed that high BMIc accuracy required recording from large neuronal ensembles. Continuous BMIc operation by monkeys led to significant improvements in both model predictions and behavioral performance. Using visual feedback, monkeys succeeded in producing robot reach-and-grasp movements even when their arms did not move. Learning to operate the BMIc was paralleled by functional reorganization in multiple cortical areas, suggesting that the dynamic properties of the BMIc were incorporated into motor and sensory cortical representations.

1,740 citations