• Journal of Institute of Control, Robotics and Systems
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• v.15 no.8
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• pp.831-838
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• 2009

This paper proposes an IR (infrared) LED (Light Emitting Diode)-based tactile fingertip sensor that can independently measure the normal and tangential force between the hand and an object. The proposed IR LED-based tactile sensor has several advantages over other technologies, including a low price, small size, and good sensitivity. The design of the first prototype is described and some experiments are conducted to show output characteristics of the proposed sensor. Furthemore, the effectiveness of the proposed sensor is demonstrated through anti-slip control in a multifunction myoelectric hand, called the KNU Hand, which includes several novel mechanisms for improved grasping capabilities. The experimental results show that slippage was avoided by simple force control using feedback on the normal and tangential force from the proposed sensor. Thus, grasping force control was achieved without any slippage or damage to the object.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.8
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• pp.824-832
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• 2011

An essential consideration to differentiate prosthetic hand from robot hand is its convenience and usefulness rather than high resolution or multi-function of the robot hand. Therefore, this study proposes a myoelectric hand with a 2 DOF auto wrist module which has 6 essential functions of the human hand such as open, grasp, pronation, supination, extension, flexion, which improves the convenience of the daily life. It consists of the 3 main parts, the myoelectric sensor for input signal without additional attachment to operate the prosthetic hand, hand mechanism with high-torqued auto-transmission mechanism and self-locking module which guarantee the safety under the abrupt emergency and minimum power consumption, and dual threshold based controller to make easy for adopting the multi-DOF myoelectric hand. We prove the validity of the proposed system with experimental results.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.6
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• pp.1027-1035
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• 1994

This paper aims to make an artificial arm control strategy. For this, we propose a new feature extraction method and design artificial neural network for the functional classification of myoelectric signal(MES). We first transform the two channel myoelectric signals (MES) for biceps and triceps into frequency domain using fast Fourier transform (FFT). And features were obtained by comparing the magnitudes of ensemble spectrum data and used as inputs to the three-layer neural network for the learning. By changing the number of units in hidden layer of neural network we observed the improvement of classification performance. To observe the effeciency of the proposed scheme we performed experiments for classification of six arm functions to the three subjects. And we obtained on average 94[%] the ratio of classification.

• Physical Therapy Korea
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• v.2 no.1
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• pp.80-87
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• 1995

The purpose of this case study was to introduce a myoelectric hand prosthesis for upper extremity amputee and prosthetic training program. Limb loss can result from disease, injury, or congenital causes. Trauma has been increasingly important role as the cause of amputaion in young, vigorous, and otherwise healthy individuals. The higher the level of amputation the greater the functional loss of the part, and the more the amputee must depend on the prostheis for fuction and cosmesis. Myoelectrical control of prostheses is a recent development and has been steadily gaining in clinical use over the past 20 years. Such a prosthesis uses signals from muscle contraction within the stump to activate a battery driven moter that operates specific component fuctions of the prosthesis. This twenty years old male case was operated a right above-elbow amputation due to tracffic accident and admitted to Yonsei Rehabilitaion hospital for the preprosthetic and prosthetic training. The case was able to successfully complete his myoelectric hand prosthesis training in the February of 1995.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1489-1494
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• 2003

A neuromuscular control system of a myoelectric prosthetic hand (PH) constitutes a nonlinear system with a dead zone whose magnitude is equal to its joint angle when the PH just grasps an object. This is because the neuromuscular control system remains an open-loop system until the PH grasps the object but it constitutes a feedback control system after the PH griped the object in which a torque induced in the fingers of the PH is fed back. To improve the transient performance of the control system, it is desirable to make the feed-forward gain as large as possible, so long as the stability of the system is not impaired. It is also desired that the control system remains stable even when the PH lifts a heavy or rigid object, because this makes the closed loop gain large and leads to the closed system unstable. According to the theory of stability analysis of nonlinear systems, we can only know the sufficient conditions that the system should be stable. Thus the nonlinear theory on stability is insufficient to be used to design the neuromuscular control system for improving its transient responses. This paper shows that the nonlinear system with a dead zone can be approximated to a linear feedback system and that well-known methods of analysis and design on linear control systems can be applicable. It is also shown through various simulation results that errors induced by approximation are practically negligible and thus the design methods are quite accurate.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.12
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• pp.1248-1255
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• 2011

In daily life, another major role of human hand is a communicative function using hand gestures besides grasp function. Therefore, if amputees can express their intention by the prosthetic hand, they can much actively participate in social activities. Thus, this paper propose myoelectric multi-function prosthetic hand which can express 6 useful hand gestures such as Rock, Scissors, Paper, Indexing, Ok and Thumb-up. It was designed as under-actuated structure to minimize volume and weight of the prosthetic hand. Moreover, in order to effectively control various hand gestures by only two EMG sensors, we propose a control strategy that the signal type are expanded as "Strong" and "Light", and hand gestures are hierarchically classified for the intuitive control. Finally, we prove the validity of the developed prosthetic hand with the experiment.

• Journal of Biomedical Engineering Research
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• v.26 no.5
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• pp.257-264
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• 2005

This paper proposes an ionic polymer metal composite (IPMC) based artificial muscle to be applicable to the Myoelectric hand prosthesis. The IPMC consists of a thin polymer membrane with metal electrodes plated chemically on both faces, and it is widely applying to the artificial muscle because it is driven by relatively low input voltage. The control commands for the IPMC-based artificial muscle is given by electromyographic (EMG) signals obtained from human forearm. By an intended contraction of the human flexor carpi ulnaris and extensor carpi ulnaris muscles, we investigated the actuation behavior of the IPMC-based artificial muscle. To obtain higher actuation force of the IPMC, the single layered as thick as $800[{\mu}m]$ or multi-layered IPMC of which each layer can be as thick as $178[{\mu}m]$ are prepared. As a result, the bending force was up to the maximum 12[gf] from 1[gf] by actuating the single layered IPMC with $178[{\mu}m]$, but the bending displacement was reduced to 6[mm] from 30[mm]. The experimental results using an implemented IPMC control system show a possibility and a usability of the bio-mimetic artificial muscle.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.714-717
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• 1988

As a muscular contraction is sustained, the power spectrum of the myoelectric signal is compressed into lower frequencies. The median frequency appears to be the prefered parameters to monitor this compression. This paper describes a technique and a device which provide an estimate of the median frequency using a TMS32020 DSP chip and IBM PC for tracking of this parameter. Results obtained from myoelectric signal are presented and discussed.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.8
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• pp.132-141
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• 2009

In this paper, we describe finger force estimation from surface electromyogram (sEMG) data for intuitive and delicate force control of robotic devices such as exoskeletons and robotic prostheses. Four myoelectric sites on the skin were found to offer favorable sEMG recording conditions. An artificial neural network (ANN) was implemented to map the sEMG to the force, and its structure was optimized to avoid both under- and over-fitting problems. The resulting network was tested using recorded sEMG signals from the selected myoelectric sites of three subjects in real-time. In addition, we discussed performance of force estimation results related to the length of the muscles. This work may prove useful in relaying natural and delicate commands to artificial devices that may be attached to the human body or deployed remotely.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.900-902
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• 1999

본 연구에서는 유전알고리즘을 이용한 인간의 팔운동 근전신호의 기능분리에 관한 방법을 제시하였다. 6가지 기능의 팔운동 시에 근전신호의 특성을 유전알고리즘을 이용하여 기능분리 하였다.