The purpose of this study is to analyze students' learning characteristics regarding spatial ability, orienteering ability and earth science content learning ability and their relationship through development and application of earth science activities using orienteering. The programme aims to improve students' spatial ability using orienteering activity which requires spatial ability. Topics in the programme included map, compass, contour, movement of celestial, and constellation application. Students were to orienteer in the field using the method they learned in class. This programme was applied to five 7th graders. The results are, first, students who have positive attitude toward science and do well at school tended to perceive their orienteering ability high. Second, all parts of spatial ability, spatial visualization, spatial orientation, spatial relation were used during orienteering, especially spatial visualization and spatial orientation. The relationship between spatial ability, orienteering ability, and earth science content learning abilities was not clear. However, orienteering ability and earth science content learning ability were in similar tendency.

이 연구는 최근 연구가 활발히 진행되는 극지 과학에 대한 인식과 관심을 널리 확산하고, 극지 소양의 개념과 방향성을 설정하고자, 학교 공통교육과정에 해당하는 교과서에서 극지 내용을 다룬 현황을 분석했다. 우리나라를 포함해 극지 진출이 활발한 5개국-미국, 프랑스, 일본, 독일, 영국의 과학과와 사회(지리)과 교과서 110권으로부터 추출한 402건의 사례를 함께 분석했고, 내용적, 형식적, 수업 단계적 측면에서 양적·질적 분석을 시도했다. 분석 결과를 바탕으로 지질과학 교육으로서 극지 연구의 가치와 더불어 지구 환경 변화의 지표로서 극지 연구의 인식 확산이 필요함을 논의했다. 교과서는 극지 빙하, 극지 화산론, 고체지구물리학, 극지 기반 시설, 그리고 지질 유산의 보전과 지질 자원 등을 주요한 극지 주제와 소재로 활용했다. 이를 통해 극지는 지구의 과거와 현재, 미래 환경에 중요한 단서를 지닌 연구 분야이며 좋은 지질학 교육 소재임을 알 수 있지만, 이를 체계적으로 강조할 교육학적 접근이 보완될 필요가 있다. 이를 위한 연구의 시사점으로 극지 과학 연구자와 교육자의 협력 체계 구축, 극지 소양을 위한 핵심 개념 추출과 그에 따른 내용 재구성, 교과와 연관된 새로운 극지 소재 발굴, 교과서 제시 형식의 다양화와 시각 자료 수준의 심화, 올바른 인지적 이해에 기반한 정의적 심상 고취 등을 제시했다. 교과서 속 극지 내용의 정비를 통해, 학생들의 극지 소양과 극지 과학 문화가 확산되고, 장차 지속 가능한 극지 연구의 원동력이 될 것으로 기대된다.

본 연구에서는 국내외 극지 관련 연구소 및 기관에서 운영하고 있는 극지 교육 프로그램의 교수 학습 특징을 분석하여 국내 극지 교육 프로그램에 대한 개선 방향을 제공하고자 한다. 이를 위해 극지연구소에서 제공하는 데이터베이스를 활용하여 33개국 45곳의 극지 관련 연구소 및 기관 중 최종적으로 9개국 16곳의 연구소 및 기관에서 현재 운영하고 있는 120개의 프로그램을 선정했다. 교육 대상, 교수자, 교육 기간, 교육 장소, 운영 방식, 교수 학습 내용, 교수 학습 방법을 분석 기준으로 하여 프로그램들을 분석했다. 연구 결과 교수자, 운영 방식, 교육 장소, 교수 학습 방법에서 국내외 프로그램의 유의미한 차이가 발견되었다. 이를 토대로 실제 프로그램 사례를 통해 극지 교육의 저변 확대, 극지 교육 프로그램 교육 방법의 다양화, 극지 교육의 체계화 측면에서 국내 극지 교육 개선 방향에 대해 논의했다. 본 연구는 극지에 대한 과학, 사회·경제·문화적 가치와 중요성을 토대로 우리나라의 극지 교육과 문화를 더욱 확산, 지속할 수 있는 구체적인 방안을 제시한다.

본 연구에서는 인공지능기법을 이용하여 진동만의 용존산소량 예측을 하였다. 관측자료에 존재하는 결측 구간을 보간하기 위해 양방향재귀신경망(BRITS, Bidirectional Recurrent Imputation for Time Series) 딥러닝 알고리즘을 이용하였고, 대표적 시계열 예측 선형모델인 ARIMA(Auto-Regressive Integrated Moving Average)과 비선형모델 중 가장 많이 이용되고 있는 LSTM(Long Short-Term Memory) 모델을 이용 하여 진동만의 용존산소량을 예측하고 그 성능을 평가했다. 결측 구간 보정 실험은 표층에서 높은 정확도로 보정이 가능했으나, 저층에서는 그 정확도가 낮았으며, 중층에서는 실험조건에 따라 정확도가 불안정하게 나타났다. 실험조건에 따라 정확도가 불안정하게 나타났다. 결과로부터 LSTM 모델이 중층과 저층에서 ARIMA 모델보다 우세한 정확도를 보였으나, 표층에서는 ARIMA모델의 정확도가 약간 높은 것으로 나타났다.

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.

This paper proposes a pattern recognition and classification algorithm based on a circular structure that can reflect the characteristics of the sEMG (surface electromyogram) signal measured in the arm without putting the placement limitation of electrodes. In order to recognize the same pattern at all times despite the electrode locations, the data acquisition of the circular structure is proposed so that all sEMG channels can be connected to one another. For the performance verification of the sEMG pattern recognition and classification using the developed algorithm, several experiments are conducted. First, although there are no differences in the sEMG signals themselves, the similar patterns are much better identified in the case of the circular structure algorithm than that of conventional linear ones. Second, a comparative analysis is shown with the supervised learning schemes such as MLP, CNN, and LSTM. In the results, the classification recognition accuracy of the circular structure is above 98% in all postures. It is much higher than the results obtained when the linear structure is used. The recognition difference between the circular and linear structures was the biggest with about 4% when the MLP network was used.

This paper presents the kinematic modeling of the human forearm rotation constructed with a spatial four-bar linkage. Especially, a circumduction of the distal ulna is modeled for a minimal displacement of the position of the hand during the forearm rotation from the supination to the pronation. To establish its model, four joint types of the four-bar linkage are, firstly, assigned with the reasonable grounds, and then the spatial linkage having the URUU (Universal-Revolute-Universal-Universal) joint type is proposed. Kinematic analysis is conducted to show the behavior of the distal radio-ulna as well as to evaluate the angular displacements of all the joints. From the simulation result, it is, finally, revealed that the URUU spatial linkage can be substituted for the URUR (Universal-Revolute-Universal-Revolute) spatial linkage by a kinematic constraint.

This paper suggests novel types of joint mechanisms composed of elastic strings and rigid bodies. All of the human hinge joints have the articular capsule and a pair of collateral ligaments. These fibrous tissues make the joint compliant and stable. The proposed mechanism closely imitates the human hinge joint structure by using the concept of tensegrity. The resultant mechanism has several characteristics shown commonly from both the tensegrity structure and the human joint such as compliance, stability, lightweight, and non-contact between rigid bodies. In addition, the role and feature of the human hinge joints vary according to the origins of a pair of collateral ligaments. Likewise, the locations of two strings corresponding to a pair of collateral ligaments produce different function and motion of the proposed mechanism. It would be one of the advantages obtained from the proposed mechanism. How to make a joint mechanism with different features is also suggested in this paper.

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.

The paper proposes a stiffness measurement device composed of a measurement part including six indenters and a fixing part including four fixtures. The device is able to make simultaneously measurements of the stiffness of human arm. The six indenters make use of both position and force control schemes sequentially whenever needed. In addition, the loadcells and the digital encoders are attached to the indenters and electric motors, respectively, so that the data can be provided in real time. On the end of the indenter, two-axis potentiometer is attached in order to measure the angle difference between the applied force axis and the axis normal to the skin of human arm, and to convert the force measured on the loadcell into the actual applied force to skin. For this purpose, the mapping between the voltage output and the angle of potentiometer was obtained by fitting it for each axis. Ultimately, the measurement device was able to measure the stiffnesses of six regions of human arm.

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%).

This paper presents an anthropomorphic finger prosthesis for amputees whose proximal phalanx is mutilated. The finger prosthesis to be proposed is able to make the amputees to perform the natural motion such as flexion/extension as well as self-adaptive grasping motion as if normal human finger does. The mechanism of finger prosthesis with three degrees-of-freedom (DOFs) consists of two five-bar and one four-bar linkages. Two passive components composed of torsional spring and mechanical stopper and only one active joint are employed in order to realize an underactuation. Each passive component is installed into the five-bar linkage. In order to activate the finger prosthesis, it is required for the user to flex and extend the remaining proximal phalanx on the metacarpophalangeal (MCP) joint, not an electric motor. Thus the finger prosthesis conducts not only the natural motion according to his/her intention but also the grasping motion through the deformation of springs by the object for human finger-like behavior. In order to reveal the operation principle of the proposed mechanism, kinematic analysis is performed for the linkage design. Finally both simulations and experiments are conducted in order to reveal the design feasibility of the proposed finger mechanism.