• 대한기계학회논문집A
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• 제40권10호
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• pp.843-849
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• 2016

과소 구동기구 로봇 핸드에서 최소한의 구동기만으로 적응 파지 기능을 구현할 수 있는 것으로 알려져 있다. 이러한 연구결과를 기반으로 새로운 의수의 설계를 제안하였다. 또한 다양한 크기의 원통형, 구형, 사각 기둥 형 물체에 대한 의수의 파지 성능을 평가하는 방법을 제시하였다. 중요 설계 인자들이 파지 성능에 미치는 영향을 실험계획법으로 평가하고 개선된 설계 방안을 제시하였다.

• 한국정밀공학회지
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• 제17권11호
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• pp.36-42
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• 2000

• 로봇학회논문지
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• 제19권1호
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• pp.1-7
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• 2024

This paper presents an under-actuated robotic hand inspired by the ligamentous structure of the human hand for a prosthetic application. The joint mechanisms are based on the concept of a tensegrity structure formed by elastic strings. These rigid bodies and elastic strings in the mechanism emulate the phalanx bones and primary ligaments found in human finger joints. As a result, the proposed hand inherently possesses compliant characteristics, ensuring robust adaptability during grasping and when interacting with physical environments. For the practical implementation of the tensegrity-based joint mechanism, we detail the installation of the strings and the routing of the driving tendon, which are related to extension and flexion, respectively. Additionally, we have designed the palm structure of the proposed hand to facilitate opposition and tripod grips between the fingers and thumb, taking into account the transverse arch of the human palm. In conclusion, we tested a prototype of the proposed hand to evaluate its motion and grasping capabilities.

• 문화기술의 융합
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• 제10권1호
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• pp.19-23
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• 2024

양손 절단 환자들에게 미용적 목적과 함께 기능적 목적을 갖춘 의수가 필요하며 잔존 근육의 근전도를 이용한 인공 의수에 대한 연구가 활발하나 아직도 비싼 비용의 문제가 있다. 본 연구에서는 저비용의 부품과 소프트웨어인 표면 근전도 센서, 머신러닝 소프트웨어 Edge Impulse, Arduino Nano 33 BLE, 그리고 3D 프린팅을 이용하여 인공의수를 제작하고 성능을 평가하였다. 표면 근전도 센서로 획득하고 Edge Impulse에서 디지털 시그널 프로세싱 과정을 거친 신호들을 이용하여 머신러닝으로 손가락 운동의 종류를 판단하는 훈련을 통해 각 손가락의 굽힘 운동신호를 의수 모델의 손가락들에 전달하였다. 디지털 시그널 프로세싱 조건을 노치 필터 60 Hz, 대역필터 10-300 Hz, 그리고 샘플링 주파수 1,000 Hz로 했을 때, 머신 러닝의 정확도가 82.1%로 가장 높았다. 각 손가락 굴곡 운동간에 혼동될 수 있는 가능성은 약지가 가장 높아서 검지의 운동으로 혼동될 가능성이 44.7 %이었다. 저비용 인공의수의 성공적인 개발을 위해서는 더 많은 연구가 필요하다.

• Archives of Plastic Surgery
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• 제46권4호
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• pp.303-310
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• 2019

Background Prosthetic hands with a myoelectric interface have recently received interest within the broader category of hand prostheses, but their high cost is a major barrier to use. Modern three-dimensional (3D) printing technology has enabled more widespread development and cost-effectiveness in the field of prostheses. The objective of the present study was to evaluate the clinical impact of a low-cost 3D-printed myoelectric-interface prosthetic hand on patients' daily life. Methods A prospective review of all upper-arm transradial amputation amputees who used 3D-printed myoelectric interface prostheses (Mark V) between January 2016 and August 2017 was conducted. The functional outcomes of prosthesis usage over a 3-month follow-up period were measured using a validated method (Orthotics Prosthetics User Survey-Upper Extremity Functional Status [OPUS-UEFS]). In addition, the correlation between the length of the amputated radius and changes in OPUS-UEFS scores was analyzed. Results Ten patients were included in the study. After use of the 3D-printed myoelectric single electromyography channel prosthesis for 3 months, the average OPUS-UEFS score significantly increased from 45.50 to 60.10. The Spearman correlation coefficient (r) of the correlation between radius length and OPUS-UEFS at the 3rd month of prosthetic use was 0.815. Conclusions This low-cost 3D-printed myoelectric-interface prosthetic hand with a single reliable myoelectrical signal shows the potential to positively impact amputees' quality of life through daily usage. The emergence of a low-cost 3D-printed myoelectric prosthesis could lead to new market trends, with such a device gaining popularity via reduced production costs and increased market demand.

• 한국전문물리치료학회지
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• 제2권1호
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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.

• 로봇학회논문지
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• 제14권3호
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• pp.228-235
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• 2019

This paper presents a multiple DoFs (degrees-of-freedom) prosthetic forearm and sEMG (surface electromyogram) pattern recognition and motion intent classification of forearm amputee. The developed prosthetic forearm has 9 DoFs hand and single-DoF wrist, and the socket is designed considering wearability. In addition, the pattern recognition based on sEMG is proposed for prosthetic control. Several experiments were conducted to substantiate the performance of the prosthetic forearm. First, the developed prosthetic forearm could perform various motions required for activity of daily living of forearm amputee. It was able to control according to shape and size of the object. Additionally, the amputee was able to perform 'tying up shoe' using the prosthetic forearm. Secondly, pattern recognition and classification experiments using the sEMG signals were performed to find out whether it could classify the motions according to the user's intents. For this purpose, sEMG signals were applied to the multilayer perceptron (MLP) for training and testing. As a result, overall classification accuracy arrived at 99.6% for all participants, and all the postures showed more than 97% accuracy.

• 대한기계학회논문집A
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• 제41권5호
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• pp.415-420
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• 2017

과소 구동 전동의수는 가벼우며 비교적 경제적이라는 장점이 있다. 본 연구에서는 적응파지가 가능한 과소 구동 전동의수를 대상으로 경제적인 파지력 제어 시스템을 제안하였다. 근전도 신호로 구동되는 메인 케이블의 장력으로 파지력이 결정되므로 장력에 따라 사용자의 피부에 부착된 진동모터를 구동하는 촉감기반 피드백 시스템을 구성하였다. 진동 신호에 대한 사용자의 감각적 판단을 기반으로 파지력을 추정하고 제어하기 위하여 파지력과 진동 신호 간의 적절한 변환 관계를 수립하고 시제품 성능시험을 하였다. 최소한의 훈련으로 사용자들은 비교적 정확하게 파지력을 제어할 수 있었다.

• 로봇학회논문지
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• 제13권4호
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• pp.265-271
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• 2018

The recent prosthetic technologies pursue to control multi-DOFs (degrees-of-freedom) hand and wrist. However, challenges such as high cost, wear-ability, and motion intent recognition for feedback control still remain for the use in daily living activities. The paper proposes a multi-channel knit band sensor to worn easily for surface EMG-based prosthetic control. The knitted electrodes were fabricated with conductive yarn, and the band except the electrodes are knitted using non-conductive yarn which has moisture wicking property. Two types of the knit bands are fabricated such as sixteen-electrodes for eight-channels and thirty-two electrodes for sixteen-channels. In order to substantiate the performance of the biopotential signal acquisition, several experiments are conducted. Signal to noise ratio (SNR) value of the knit band sensor was 18.48 dB. According to various forearm motions including hand and wrist, sixteen-channels EMG signals could be clearly distinguishable. In addition, the pattern recognition performance to control myoelectric prosthesis was verified in that overall classification accuracy of the RMS (root mean squares) filtered EMG signals (97.84%) was higher than that of the raw EMG signals (87.06%).

• 전기전자학회논문지
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• 제24권4호
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• pp.1039-1049
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• 2020

신경의수를 제어하기 위해서는 사용자의 의도를 인식하는 신경신호 디코딩이 중요하다. 본 논문에서는 다채널 말초 신경신호의 실시간 디코딩 방법을 제안한다. 말초 신경신호는 정중신경과 요골신경에서 측정되었으며 운동잡음은 국소 근사 다항식에 의해 제거되었다. 다음으로 활동전위는 k-평균 알고리즘으로 분류되었다. 특징벡터는 활동전위의 발화율로부터 추출되었으며 자기 조직화 특징지도를 통해 차원이 축소되었다. 마지막으로 다층 퍼셉트론으로 손동작을 분류하였다. 원숭이 실험에서 모든 신호처리가 실시간 제한조건 이내에 완료되었으며 높은 성공률로 손동작을 인식할 수 있었다.