• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.324-329
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• 2009

Hand Motion Modeling and Recognition (HMR) are a fundamental technology in the field of proactive computing for designing a human computer interaction system. In this paper, we present a 3D HMR system including data glove based on 3-axis accelerometer sensor and 3D Hand Modeling. Data glove as a device is capable of transmitting the motion signal to PC through wireless communication. We have implemented a 3D hand model using kinematic chain theory. We finally utilized the rule based algorithm to recognize hand gestures namely, scissor, rock and papers using the 3-D hand model.

• The Journal of Korea Robotics Society
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• v.15 no.3
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• pp.240-247
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• 2020

This paper presents a novel knitted data glove system for pattern classification of hand posture. Several experiments were conducted to confirm the performance of the knitted data glove. To find better sensor materials, the knitted data glove was fabricated with stainless-steel yarn and silver-plated yarn as representative conductive yarns, respectively. The result showed that the signal of the knitted data glove made of silver-plated yarn was more stable than that of stainless-steel yarn according as the measurement distance becomes longer. Also, the pattern classification was conducted for the performance verification of the data glove knitted using the silver-plated yarn. The average classification reached at 100% except for the pointing finger posture, and the overall classification accuracy of the knitted data glove was 98.3%. With these results, we expect that the knitted data glove is applied to various robot fields including the human-machine interface.

• Proceedings of the ESK Conference
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• 1997.10a
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• pp.393-397
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• 1997

본 연구에서는 손동작(Hand Motion)과 수작업(Manual Task) 분석에 VR환경에서 사용되는 각도 측정 장갑(3-D Glove)을 이용하는 방법을 제안하였다. 본 연구에서 개발된 손동작(Hand Motion)과 수작업(Manual Task)의 분석 시스템은 18-sensor $Cyberglove^{TM}$정 시스템으로부터 측정된 angle data를 기초로 손동작이나 수작업에 대한 totalmuscle moment값과 total muscle excursion값을 구하고, digit와 joint의 moment값을 X,Y.Z방향별고 구하는 기능을 가지고 있다. 시스템의 구성은 : (1) $Cyberglove^{TM}$ System과 분석 시스템의 digital data 처리를 기반으로 하는 손동작의 측정 시스템 ; (2) $Cyberglove^{TM}$ System에서 얻어진 자료를 바탕으로 3차원 공간에서 손동작을 표현할 수 있는 Kinematic Hand Model ; (3) Hand Model과 $Cyberglove^{TM}$ Systme을 기반으로 3차원에서 손동작의 역학적 분석을 할 수 있는 3-D Hand Biomechanical Model ; 등으로 되어있다. 본 시스템은 Telerobotics, Medicine, Virtual Reality 등 다양한 분야에 응용이 가능하며, 수작업에 관련되는 Product Design, Manual Control Device, Computer I/O Device의 설계에도 도움이 될 것으로 기대된다.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.10
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• pp.1014-1020
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• 2011

This presents a motion and force estimation system of human fingers by using an Electromyography (EMG) sensor module and a data glove system to be proposed in this paper. Both EMG sensor module and data glove system are developed in such a way to minimize the number of hardware filters in acquiring the signals as well as to reduce their sizes for the wearable. Since the onset of EMG precedes the onset of actual finger movement by dozens to hundreds milliseconds, we show that it is possible to predict the pattern of finger movement before actual movement by using the suggested system. Also, we are to suggest how to estimate the grasping force of hand based on the relationship between RMS taken EMG signal and the applied load. Finally we show the effectiveness of the suggested estimation system through several experiments.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.566-569
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• 2022

In this paper, the algorithm was developed to recognize hand golve gesture and implemented a system to remotely control the robot. The system consists of a camera and a controller that controls robot motion by hand position gesture. The camera recognizes the specific color of the glove and outputs the recognized range and position by including the color area of the glove. We recognize the velocity vector of robot motion and control the robot by the output data of the position and the detected rectangle. Through the several experiments, it was confirmed that the robot motion control was successfully performed.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.12
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• pp.1585-1591
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• 2011

In this paper, we present a new exoskeleton-type data glove that can sense the movement of the human finger and reflect the force to the finger. The data glove is designed on the basis of the skeletal structure of the human hand, and the finger module has 1 degree-of-freedom because it includes three four-bar mechanism joints in series and a wire-coupling mechanism. In addition, the transmission ratio of the finger module is maintained at 1:1.4:1 over the entire movement range, and hence, the module can perform both extension and flexion. In addition, to enable adduction/abduction motion of the human hand, a unique MCP joint is designed by using two universal joints. To validate the feasibility of the data glove, master-slave control experiments based on force-position control between the data glove and the robot hand are conducted.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2009.05a
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• pp.93-97
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• 2009

현재 손가락 및 제스춰 인식 같은 영상처리를 이용하여 로봇의 움직임을 제어하는 방법이 많이 연구되고 있지만, 인식 속도가 느려서 빨리 제어하지 못하는 단점이 있다. 하지만 데이터 글러브와 같은 입력 장치를 이용하면 쉽고 빠르게 제어가 가능하다. 이에 본 논문에서는 로봇 키트를 이용하여 자동차 로봇을 구현하고, 이를 데이터 글러브(Data Glove)와 연동시켜 움직임을 제어하는 방법을 제안한다.

• The KIPS Transactions:PartB
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• v.16B no.5
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• pp.341-346
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• 2009

The motion data glove is a representative human-computer interaction tool that inputs human hand gestures to computers by measuring their motions. The motion data glove is essential equipment used for new computer technologiesincluding home automation, virtual reality, biometrics, motion capture. For its popular usage, this paper attempts to develop an inexpensive visual.type motion data glove that can be used without any special equipment. The proposed approach has the special feature; it can be developed as a low-cost one becauseof not using high-cost motion-sensing fibers that were used in the conventional approaches. That makes its easy production and popular use possible. This approach adopts a visual method that is obtained by improving conventional optic motion capture technology, instead of mechanical method using motion-sensing fibers. Compared to conventional visual methods, the proposed method has the following advantages and originalities Firstly, conventional visual methods use many cameras and equipments to reconstruct 3D pose with eliminating occlusions But the proposed method adopts a mono vision approachthat makes simple and low cost equipments possible. Secondly, conventional mono vision methods have difficulty in reconstructing 3D pose of occluded parts in images because they have weak points about occlusions. But the proposed approach can reconstruct occluded parts in images by using originally designed thin-bar-shaped optic indicators. Thirdly, many cases of conventional methods use nonlinear numerical computation image analysis algorithm, so they have inconvenience about their initialization and computation times. But the proposed method improves these inconveniences by using a closed-form image analysis algorithm that is obtained from original formulation. Fourthly, many cases of conventional closed-form algorithms use approximations in their formulations processes, so they have disadvantages of low accuracy and confined applications due to singularities. But the proposed method improves these disadvantages by original formulation techniques where a closed-form algorithm is derived by using exponential-form twist coordinates, instead of using approximations or local parameterizations such as Euler angels.

• Journal of Sensor Science and Technology
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• v.26 no.2
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• pp.128-134
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• 2017

Until now, neuropsychological tests can diagnose the brain dysfunction, however, cannot distinguish the objective data of experiment enough to distinguish the relationships between brain dysfunction and cerebropathia. In this paper, an automatic cognitive test equipment system with 6-axis motion sensors was proposed for the automation of neuropsychological tests. Fist-Edge-Palm(FEP) test and Go-no go test were used to evaluate motor programming of frontal lobe. The motion data from the specially designed motion glove are transmitted wirelessly to a computer to detect the gestures automatically. The healthy 20 and 11 persons are investigated for the FEP and Go-No go test, respectively. The recognition rates of gestures of FEP and Go-No go test are min. 91.38% and 89.09%. In conclusion, the automations of cognitive tests are successful to diagnose the brain diagnostics quantitatively.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.124-129
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• 1992

The goal of the proposed Intelligent Assisting System - IAS is to assist human operators in an intelligent way, while leaving decision and goal planning instances for the human. To realize the IAS the very important issue of manipulation skill identification and analysis has to be solved, which then is stored in a Skill Data Base. Using this data base the IAS is able to perform complex manipulations on the motion control level and to assist the human operator flexibly. We propose a model for manipulation skill based on the dynamics of the grip transformation matrix, which describes the dynamic transformation between object space and finger joint space. Interaction with a virtual world simulator allows the calculation and feedback of appropriate forces through controlled actuators of the sensor glove with 10 degrees-of-freedom. To solve the sensor glove calibration problem, we learn the nonlinear calibration mapping by an artificial neural network(ANN). In this paper we also describe the experimental system setup of the skill acquisition and transfer system as a first approach to the IAS. Some simple manipulation examples and simulation results show the feasibility of the proposed manipulation skill model.