• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.841-847
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• 2007

최근 삶의 수준의 향상과 의학 기술의 발전으로 노인 인구가 증가하고 있다. 하지만 늘어나는 노인 인구에 비례하여 신체적 노화로 거동이 어려운 노인의 수 또한 증가하는 추세이다. 실제로 많은 노인 인구가 거동이 불편해 정상적인 생활을 하지 못하고 있기 때문에 보행 시 적절한 힘을 보조해 줄 수 있는 보행 보조 장치의 개발이 필요하다. 이 같은 보행 보조 장치를 개발함에 있어 보행자의 보행 패턴이 고려된다면 보행자의 걸음걸이에 맞춰 자연스럽게 힘을 보조해 줄 수 있기 때문에 보행자의 보행 단계 분류에 관한 연구가 선행되어야 한다. 그래서 본 논문에서는 하지 근전도 신호를 이용해 보행 단계를 구분하는 방법을 제안하고자 한다. 근전도 신호는 근육이 움직일 때 발생하는 아주 작은 전기적인 신호이다. 근전도 신호는 작은 잡음에도 민감하며, 전극을 부착하는 근육의 위치에 따라서도 값의 차이가 크기 때문에 근전도 신호의 획득 및 처리 방법이 중요하다. 위를 위해 피실험자 별 근육의 위치와 보행 속도를 달리하여 근전도 신호를 획득하고 획득한 신호로부터 여러 특징 값을 추출한다. 그리고 새로운 데이터에 대해 적응성이 강하고 시간에 따라 변하는 근전도 신호의 특성을 잘 반영할 수 있으며 각 집합(class)의 비선형 분리가 가능한 퍼지 최대-최소 신경망(Fuzzy Min-Max Neural Network: FMMNN)을 이용해 보행 단계를 분류해 본다. 실험 결과를 통해 제안한 방법의 타당성을 검증해 보고 보행자, 보행속도, 근전도 측정을 위한 근육의 위치가 보행 패턴 분류에 미치는 영향을 알아본다.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.3
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• pp.181-189
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• 2015

Powered prosthesis is used to assist walking of people with an amputated lower limb and/or weak leg strength. The accurate gait phase classification is indispensable in smooth movement control of the powered prosthesis. In previous gait phase classification using physical sensors, there is limitation that powered prosthesis should be simulated as same as the speed of training process. Therefore, we propose EMG signal based gait phase recognition method to classify stairs ascending and stairs descending into four steps without using physical sensors, respectively. RMS, VAR, MAV, SSC, ZC, WAMP features are extracted from EMG signal data and LDA(Linear Discriminant Analysis) classifier is used. In the training process, the AHRS sensor produces various ranges of walking steps according to the change of knee angles. The experimental results show that the average accuracies of the proposed method are about 85.6% in stairs ascending and 69.5% in stairs descending whereas those of preliminary studies are about 58.5% in stairs ascending and 35.3% in stairs descending. In addition, we can analyze the average recognition ratio of each gait step with respect to the individual muscle.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.6
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• pp.654-662
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• 2010

In this paper, we suggest an advanced algorithm, to recognize pedestrian/non-pedestrian using second-stage cascade method, which applies Adaboost algorithm to make a strong classification from weak classifications. First, we extract two feature vectors: (i) Histogram of Oriented Gradient (HOG) which includes gradient information and differential magnitude; (ii) Curvature-HOG which is based on four different curvature features per pixel. And then, a strong classification needs to be obtained from weak classifications for composite recognition method using both HOG and curvature-HOG. In the proposed method, we use one feature vector and one strong classification for the first stage of recognition. For the recognition-failed image, the other feature and strong classification will be used for the second stage of recognition. Based on our experiment, the proposed algorithm shows higher recognition rate compared to the traditional method.

• Journal of Korean Society of Transportation
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• v.21 no.5
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• pp.41-48
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• 2003

Korea Claim Adjustor Association(KCAA) defines the near pedestrian crossing accidents as those accidents that occurred in the area within 25m from pedestrian crossing on the arterial road and within 15m from pedestrian crossing on other classes of road. Accidents between pedestrian crossing and stop line are classified as the accident near pedestrian crossing. Reviewing of current statute and court precedent, three kinds of traffic accidents which are accidents occurred in the pedestrian crossing. near pedestrian crossing and the area between pedestrian crossing and stop line. should be distinguished by different pedestrian contributory negligence. To find out how different they are. we surveyed transportation society members about the contributory negligence of traffic accidents between pedestrian crossing and stop line and the results are as follows : (1) The current two classification of pedestrian crossing accidents and near pedestrian crossing accidents should be changed to three classification of pedestrian crossing accidents that includes accidents on pedestrian crossing, near pedestrian crossing and between pedestrian crossing and the stop line. (2) For the pedestrian's contributory negligence, the least reasonability to pedestrian is accident on the pedestrian crossing. The next one is the accident between pedestrian crossing and stop line and the last is the accident near pedestrian crossing. (3) Pedestrian contributory negligence for accident by space is recommended as 〈table 8〉, 〈table 9〉, 〈table 10〉. (4) Contributory negligence rate of the accident on the pedestrian crossing during red light should be modified to be less than that of near pedestrian crossing.

• Journal of Korean Society of Transportation
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• v.25 no.2 s.95
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• pp.83-93
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• 2007

Pedestrians and vehicle users should be treated with equal importance in urban street design. However, current street design suggests that the design criteria for sidewalks is based on the functional hierarchy of the vehicles, therefore it is necessary to develop sidewalk design standards that give more weight to pedestrians rather than vehicles. For this, this study suggests that the level of service of pedestrians should be considered in the process of designing sidewalks. Currently, level of service (LOS) criteria for pedestrians in the Korean Highway Capacity Manual are based on pedestrian volume, but the volume of pedestrians is seldomly estimated in practice. So, the current LOS criteria has limitations in terms of practical use. Also, the study assumes that the pedestrian flow rate is hardly the dominant factor that could affect the LOS of pedestrians at most urban sidewalks. In this context, the study considers a new LOS for sidewalk design based on the comfort of pedestrians while passing pedestrians coming from the opposite direction. Then the study attempts to link the new LOS criteria to the land use patterns using data of pedestrian traffic characteristics acquired from the field. In addition to this, the scope in which the suggested criteria can be applied is suggested.

• The Journal of the Korea Contents Association
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• v.13 no.4
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• pp.35-43
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• 2013

Exercise such as walking is helpful to manage one's own weight and to counter life habit diseases such as obesity. Calorie consumption is usually calculated based on the distance walked. One way to measure the distance is by using steps and stride length. Most pedometers, including some applications in smart devices, are inaccurate, because they use a common value as the average stride length, even though each person has a different stride length. Moreover, the stride length differs depending on the walking pace, which will further increase the error. To address this, in this paper, I classify paces into three categories. Following that, I introduce a customized measurement of stride length, which is calculated based on the stride length corresponding to each pace category after obtaining x, y, z values from a 3-axis accelerometer in the smart device. In addition to this, I developed an application running on the smart device designed for the proposed measurement of stride. I have conducted three experiments for the assessment of the proposed measurement. In conclusion, I confirmed the effectiveness of this system.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.7 no.2
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• pp.19-26
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• 2013

This paper propose a surface EMG signal based gait phase recognition method that selects features and channels adaptively. The proposed method can be used to control powered artificial prosthetic for lower limb amputees and can reduce overhead in real-time pattern recognition by selecting adaptive channels and features in an embedded device. The method can enhance the classification accuracy by adaptively selecting channels and features based on sensitivity and specificity of each subject because EMG signal patterns may vary according to subject's locomotion convention. In the experiments, we found that the muscles with highest recognition rate are different between human subjects. The results also show that the average accuracy of the proposed method is about 91% whereas those of existing methods using all channels and/or features is about 50%. Therefore we assure that sEMG signal based gait phase recognition using small number of adaptive muscles and corresponding features can be applied to control powered artificial prosthetic for lower limb amputees.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.167-171
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• 2007

본 논문은 패턴 분류 문제에 사용되는 퍼지 최대-최소 신경망 방법을 이용하여 특정 집합으로부터 새로운 특정 집합을 추출해내고 추출된 특정 집합으로부터 의미 있는 특정을 선택해 내는 새로운 방법을 제안한다. 퍼지 최대-최소 신경망은 패턴 분류를 위해 주로 사용이 되어 왔지만, 퍼지 최대-최소 신경망을 이용해 특정 집합의 값들을 패턴 공간내의 초상자의 집합으로 변환하고 변환된 초상자들끼리의 인접성을 척도로 단순한 연산을 통한 빠른 특정 집합을 선택하게 된다. 마지막으로 본 논문의 특정 집합 선택 방법을 하지 근전도 신호를 이용한 보행 패턴 분류에 적용해 보고, 그 결과를 기존 여러 특정 집합 선태 방법들과 비교해 봄으로써 제안한 방법의 타당성 및 적용 가능성을 알아본다.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.321-322
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• 2013

• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.802-805
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• 2014

본 연구에서는 스마트 디바이스를 이용하여 보행자의 상태를 감지하여 필요한 사용자의 정보에 대해 얻는 방법을 제안하고, 이를 분석하는 모델을 연구하여 예방 방안을 제공하는 서비스를 제안하였다. 분석 모델을 Sensing, Thinking, Action의 세 단계로 나누어 분류한 세부적인 수행 순서를 정하였다. Sensing 단계에서 센서, 디바이스, 어플리케이션 등을 통해 사용자에 대한 있는 그대로의 정보를 받아들여 디바이스가 인식하게 하고, 이를 분석해 사용자의 상태 및 상황에 대해 Thinking하고, 그에 맞는 Action을 취한다. 본 논문에서는 분석 모델의 정해진 수행 순서에서의 기능들을 설명하고, 그에 맞는 예상 구현 시나리오를 제시하였다.