• 제어로봇시스템학회논문지
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• 제15권8호
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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.

• 제어로봇시스템학회논문지
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• 제16권4호
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• pp.374-380
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• 2010

This paper proposes a wearable interface for a tendon-driven robotic hand prosthesis. The proposed interface is composed of a dataglove to measure finger and wrist joint angle, and a micro-control board with a wireless RF module. The interface is used for posture control of the robotic hand prosthesis. The measured joint angles by the dataglove are transferred to the main controller via the wireless module. The controller works for directly controlling the joint angle of the hand or for recognizing hand postures using a pattern recognition method such as LDA and k-NN. The recognized hand postures in this study are the paper, the rock, the scissors, the precision grasp, and the tip grasp. In experiments, we show the performances of the wearable interface including the pattern recognition method.

• 한국지능시스템학회:학술대회논문집
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• 한국지능시스템학회 2008년도 춘계학술대회 학술발표회 논문집
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• pp.400-402
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• 2008

인간과 컴퓨터간의 정보이동에 중요한 역할을 해온 인간-컴퓨터 상호작용(HCI)기술은 핵심적인 정보기술 분야에 속한다. 최근 키보드와 마우스와 같은 입력장치의 사용없이 인간의 몸짓이나 손짓등과 같은 Gesture를 입력으로 사용하여 로봇이나 제어 장치들을 제어하는 연구가 다각도로 진행되고 있으며 그 중요성 또한 날로 증가하고 있다. 본 논문에서는 가속도 센서에서 계측된 정보를 PCA알고리즘에 적용하여 사용자의 제스처를 인식하는 기법을 제안하고자 한다.

• 제어로봇시스템학회논문지
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• 제18권2호
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• pp.69-75
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• 2012

This paper proposes a biomimetic finger module to be used in a lightweight hand prosthesis. The finger module consists of finger skeleton and an actuator module driven by SMA (Shape Memory Alloy). The prototype finger module can perform flexion and extension motions; finger flexion is driven by a contraction force of SMA, but it is extended by an elastic force of an extension spring inserted into the finger skeleton. The finger motions are controlled by feedback of electric resistance of SMA because the finger module has no sensors to measure length and angle. Total weight of a prototype finger module is 30g. In experiments the finger motions and finger grip force are tested and compared with simulation results when a constant contraction force of SMA is given. The experimental results show that the proposed SMA-driven finger module is feasible to the lightweight hand prosthesis.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2012년도 추계학술발표대회
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• pp.1218-1220
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• 2012

사람이 근육을 움직여 활동을 하면 근골격근에서 $50{\mu}V{\sim}5mV$의 미세한 전압이 측정된다. 이 신호를 증폭하고 적절한 주파수를 여과시키면 근육의 수축 이완의 정도를 알아내어 움직임이나 동작을 유추해 낼 수 있다. 본 논문에서는 의수 또는 Power-Assist 로봇 등을 사람의 손가락 움직임과 동일하고 더 정밀하게 제어하기 위해 상완 상단부분에서 손가락의 근전도를 측정하는 방식을 연구한다.

• 제어로봇시스템학회논문지
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• 제21권5호
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• pp.442-446
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• 2015

As electricity consumption is increasing rapidly these days, an urgent. need exists to minimize consumption through smart and intelligent ways in order to prevent a future energy crisis. For this purpose, development of an intelligent peak power management system should be required. As the number of appliances and consumer electrical devices increase, power consumption in unit business tends to grow. Generally, electricity consumption can be minimized using a peak power management system capable of. effectively controlling the load power by continuously monitoring the power. In this work, a peak power management system which consists of arduino microprocessor equipped with ethernet and Zigbee shield is presented. To verify the feasibility of the proposed scheme, laboratory-scale experiments are carried out.

• 전자공학회논문지B
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• 제32B권7호
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• pp.1010-1024
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• 1995

An intelligent trajectory control method that controls a direction and a average velocity for a prosthetic arm by force and direction estimations using EMG signals is proposed. 3 stage linear filters are used as a real time joint trajectory planner to minimize the impact to human body induced by arm motions and to reduce muscle fatigues. We use combination of MLP and fuzzy filter for a limb direction estimation and a time model of force for determining a cartesian trajectory control parameter. EMG signals are acquired by using a amputation simulator and 2 dimensional joystick motion. Simulation results of the proposed method show that the arm is effectively followed the desired trajectory by estimated foreces and directions. This method reduces the number of electrodes and attatched sites compared with the method using Hogan's impedance control.

• 대한의용생체공학회:의공학회지
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• 제15권4호
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• pp.455-464
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• 1994

The intelligent trajectory control method that controls moving direction and average velocity for a prosthetic arm is proposed by pattern recognition and force estimations using EMG signals. Also, we propose the real time trajectory planning method which generates continuous accelleration paths using 3 stage linear filters to minimize the impact to human body induced by arm motions and to reduce the muscle fatigue. We use combination of MLP and fuzzy filter for pattern recognition to estimate the direction of a muscle and Hogan's method for the force estimation. EMG signals are acquired by using a amputation simulator and 2 dimensional joystick motion. The simulation results of proposed prosthetic arm control system using the EMG signals show that the arm is effectively followed the desired trajectory depended on estimated force and direction of muscle movements.

• 대한의용생체공학회:의공학회지
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• 제17권3호
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• pp.379-386
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• 1996

This paper describes an approach for classifying myoelectric patterns using a multilayer perceptrons (MLP's) and hidden Markov models (HMM's) hybrid classifier. The dynamic aspects of EMG are important for tasks such as continuous prosthetic control or vari- ous time length EMG signal recognition, which have not been successfully mastered by the most neural approaches. It is known that the hidden Markov model (HMM) is suitable for modeling temporal patterns. In contrasts the multilayer feedforward networks are suitable for static patterns. Ank a lot of investigators have shown that the HMM's to be an excellent tool for handling the dynamical problems. Considering these facts, we suggest the combination of MLP and HMM algorithms that might lead to further improved EMG recognition systems.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2006년도 하계종합학술대회
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• pp.245-246
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• 2006

In this paper, we proposed using MFCC coefficients(Mel-Scaled Cepstral Coefficients) and a simple but efficient classifying method. Many other features: IAV, zero crossing, LPCC, $\ldot$ and their derivatives are also tested and compared with MFCC coefficients in order to find the best combination. GMM and HMM (Discrete and Continuous Hidden Markov Model), are studied as well in the hope that the use of continuous distribution and the temporal evolution of this set of features will improve the quality of emotion recognition.