• 한국정밀공학회지
• /
• 제30권11호
• /
• pp.1217-1224
• /
• 2013

In this paper, a hand exoskeleton actuated by air muscles(soft hand exoskeleton) is introduced. Some soft hand exoskeletons have already been developed to overcome the defects of hand exoskeletons based on linkage and pneumatic piston system-they are usually bulky and do not have enough degree of freedom(DOF). However, soft hand exoskeletons still have defects. Their motions are not precise as linkage based hand exoskeletons, because their actuator, such as air muscle is made of soft materials. So we developed a new linkage which is not bulky and has redundant DOF. It is combined with air muscle in a specific way so that it acts as a guide when air muscle is actuated. Some experiments were conducted to evaluate the validity and usability of our hand exoskeleton.

• 전자공학회논문지SC
• /
• 제46권6호
• /
• pp.38-43
• /
• 2009

표면 근전도 신호를 이용하여 손가락의 굽힘 동작을 추론하는 방법을 제안한다. 표면 근전도 신호는 인체 근육의 표면에서 무해하고 손쉽게 취득되나, 전극이 근육 내부에 침투하는 침습식 근전도와는 달리 특정 근육의 활동만을 반영하지 않는다. 따라서 소수의 전극을 사용하는 표면 근전도 신호로 다양한 신체 동작을 구분하기는 쉽지 않다. 본 연구에서는 전완 둘레에 부착된 4채널 근전도 센서를 사용하여 신호를 취득하였고, 구분을 위하여 사용한 동작은 엄지, 검지, 중지, 약지, 소지의 개별 손가락의 굽힘 동작이다. 피검자 한 명은 숙련자였으며, 다른 한 명은 비숙련자였다. 근전도 신호의 특성으로 정보 엔트로피를 추출하였으며 최대우도추정법을 사용하여 실제 동작을 추정하였다. 실험 결과 평균 95% 이상의 성능을 보였으며, 제안하는 방법이 손가락 동작의 구분에 유용함을 확인하였다.

• 한국정보과학회논문지:소프트웨어및응용
• /
• 제36권6호
• /
• pp.471-478
• /
• 2009

최근 다양한 3차원 콘텐츠 및 응용 프로그램의 개발되고, 컴퓨터 사용자 계층이 다양화됨으로 인하여 인터페이스에 대한 연구가 활발히 진행되고 있다. 본 연구는 사용자가 손을 이용하여 윈도우를 효율적으로 제어할 수 있도록 하는 것을 목적으로 한다. 기존 인터페이스에 관한 연구들은 고가의 장비를 사용하거나 복잡한 제스처 인식, 또는 부가적인 도구인 마커 등을 사용하는 문제가 있다. 이러한 단점을 개선하기 위하여 새로운 시각적 비접촉 인터페이스를 제안한다. 손을 사용하여 윈도우를 제어하기 위하여 HSV 색상공간을 이용하여 손 영역을 검출한다. 그리고 거리변환 행렬과 손 외곽선의 곡률을 이용하여 손의 중심과 손가락의 위치를 파악한다. 손의 중심과 손가락 위치를 이용하여 본 논문에서 제안하는 7가지의 제어 동작 중 사용자가 의도하는 동작을 파악하여 인터페이스로써 활용한다. 제안하는 인터페이스는 스테레오 카메라를 사용함으로써 사유자가 3차원 공간에서 거리감을 가지고 제어할 수 있도록 하였다. 그리고 간단한 손동작으로 제어하고자 하는 객체를 시각적으로 접촉할 수 있도록 함으로써 직관적인 제어가 가능하도록 하였다. 마지막으로 제안하는 인터페이스를 활용한 응용을 통해서 효율성을 확인한다.

• 제어로봇시스템학회논문지
• /
• 제16권10호
• /
• pp.919-926
• /
• 2010

This paper proposes a wearable hand rehabilitation device, DULEX, for persons with functional paralysis of upper-limbs after stoke. DULEX has three degrees of freedom for rehabilitation exercises for wrist and fingers except the thumb. The main function of DULEX is to extend the range of motions of finger and wrist being contracture. DULEX is designed by using a parallel mechanism, and its parameters such as length and location of links are determined by kinematic analysis. The motion trajectory of the designed DULEX is aligned to human hand to prevent a slip. To reduce total weight of DULEX, artificial air muscles are used for actuating each joint motion. In feedback control, each joint angle is indirectly estimated from the relations of the input air pressure and the output muscle length. Experimental results show that DULEX is feasible in hand rehabilitation for stroke survivals.

• 한국운동역학회지
• /
• 제17권1호
• /
• pp.69-79
• /
• 2007

The purpose of this study was to examine the characteristics of throwing motion in the elementary school students from the developmental point of view. For the purpose of this, total of nine subjects(each of three students in five, third, and first grades) were participated. They were asked to throw the ball as far as they can and the motions were videotaped with the 30frames/sec and 1/500 shutter speed. The successful motions for each subject were selected for three dimensional analysis. The collected data were analyzed using DV express 1.0 and Kwon3D 3.0 softwares. The results obtained from this study were as follows; 1. Total time for the throwing motion of the first grade was longer than that of the fifth and third grades. 2. The resultant displacement and velocity of COM for the fifth and third grades were greater than that of the frist grade. 3. The first grade tended to flex the trunk forward excessively during the throwing motion. 4. The fifth grade tended to place the upper arm close to the sagital plane and move the forearm and hand freely. 5. Looking at the greater variability of the angular velocity of the hand segment, the fifth grade seemed to have faster and more flexible movement of the wrist. 6. There were somewhat differences in the patterns and magnitudes of ground reaction forces among the different grades.

• Archives of Reconstructive Microsurgery
• /
• 제15권2호
• /
• pp.117-122
• /
• 2006

The function of the thumb is critical to overall hand function. The thumb enables motions such as pinch, grip, fine manipulation and allows to circumduction and opposition. It's loss is a serious problem not only from cosmetic point of view but also functional. Therefore, we should make every effort on thumb reconstruction. Many methods of thumb reconstruction from simple osteoplasty to complex microsurgical reconstruction have been reported. We should understand merits and demerits of each method and choose proper method on case by case. When both hands are injured and there is no option but to amputate one hand and the thumb of another hand is lost, spare part flap from the thumb of the amputated hand to another hand can solve cosmetic problem, functional problem and donor morbidity. We report a case of amputated thumb which has been reconstructed with amputated ring finger of the contralateral hand using spare part flap concept.

• 한국통신학회논문지
• /
• 제30권12C호
• /
• pp.1175-1180
• /
• 2005

본 논문에서는 비전 기반의 손동작 검출 및 추적 시스템을 제안하고자 한다. 기존의 손동작 인식 시스템은 정적인 관측 환경에서 배경을 제거함으로 손을 검출하는 단순한 방법을 사용함으로써, 카메라의 움직임, 조명의 변화 등에 의해 견실하지 못하였다. 그러므로 본 논문에서는 기하학적 구조에 의하여 손의 외형을 인식하여 검출할 수 있는 통계적 방법을 제안하였다. 또한 카메라의 각도에 의한 손이 겹쳐 보이는 문제를 줄이기 위하여 다중 카메라를 사용하였으며 비동기식 다중 관측으로 시스템의 범용성을 향상시키었다. 실험 결과 제안된 방법이 기존의 외관을 이용한 방법보다 $3.91\%$ 개선된 $99.28\%$의 인식률을 나타내어 제안한 방법의 효율성을 입증하였다.

• 대한의용생체공학회:의공학회지
• /
• 제42권4호
• /
• pp.186-192
• /
• 2021

In this paper, we evaluated prediction accuracy of Euler angle spectrograph classification method using a convolutional neural networks (CNN) for hand gesture recognition in augmented reality (AR) cognitive rehabilitation system based on Leap Motion Controller (LMC). Hand gesture recognition methods using a conventional support vector machine (SVM) show 91.3% accuracy in multiple motions. In this paper, five hand gestures ("Promise", "Bunny", "Close", "Victory", and "Thumb") are selected and measured 100 times for testing the utility of spectral classification techniques. Validation results for the five hand gestures were able to be correctly predicted 100% of the time, indicating superior recognition accuracy than those of conventional SVM methods. The hand motion recognition using CNN meant to be applied more useful to AR cognitive rehabilitation training systems based on LMC than sign language recognition using SVM.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제14권2호
• /
• pp.757-770
• /
• 2020

We propose a hand gesture recognition method that is compatible with a head-up display (HUD) including small processing resource. For fast link adaptation with HUD, it is necessary to rapidly process gesture recognition and send the minimum amount of driver hand gesture data from the wearable device. Therefore, we use a method that recognizes each hand gesture with an inertial measurement unit (IMU) sensor based on revised correlation matching. The method of gesture recognition is executed by calculating the correlation between every axis of the acquired data set. By classifying pre-defined gesture values and actions, the proposed method enables rapid recognition. Furthermore, we evaluate the performance of the algorithm, which can be implanted within wearable bands, requiring a minimal process load. The experimental results evaluated the feasibility and effectiveness of our decomposed correlation matching method. Furthermore, we tested the proposed algorithm to confirm the effectiveness of the system using pre-defined gestures of specific motions with a wearable platform device. The experimental results validated the feasibility and effectiveness of the proposed hand gesture recognition system. Despite being based on a very simple concept, the proposed algorithm showed good performance in recognition accuracy.

• 제어로봇시스템학회논문지
• /
• 제12권9호
• /
• pp.935-944
• /
• 2006

EMG pattern recognition is essential for the control of a multifunction myoelectric hand. The main goal of this study is to develop an efficient feature projection method for EMC pattern recognition. To this end, we propose a linear supervised feature projection that utilizes linear discriminant analysis (LDA). We first perform wavelet packet transform (WPT) to extract the feature vector from four channel EMC signals. For dimensionality reduction and clustering of the WPT features, the LDA incorporates class information into the learning procedure, and finds a linear matrix to maximize the class separability for the projected features. Finally, the multilayer perceptron classifies the LDA-reduced features into nine hand motions. To evaluate the performance of LDA for the WPT features, we compare LDA with three other feature projection methods. From a visualization and quantitative comparison, we show that LDA has better performance for the class separability, and the LDA-projected features improve the classification accuracy with a short processing time. We implemented a real-time pattern recognition system for a multifunction myoelectric hand. In experiment, we show that the proposed method achieves 97.2% recognition accuracy, and that all processes, including the generation of control commands for myoelectric hand, are completed within 97 msec. These results confirm that our method is applicable to real-time EMG pattern recognition far myoelectric hand control.