• 한국안전학회지
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• 제17권4호
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• pp.178-183
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• 2002

The purpose of this study was to quantitatively investigate the effects of external load upper extremity posture on perceived discomfort. An experiment was conducted for measuring discomfort scores depending upon external loads and upper extremity postures, in which the free modules and numeric estimate method of the magnitude estimation was adopted as a tool for obtaining discomfort ratings. The upper extremity postures were controlled by wrist flexion/extension, elbow foexion, shoulder flexion, and shoulder adduction/abduction. The results showed that all experimental variables except shoulder adduction/abduction were significant at ${\alpha}$=0.01 or 0.05. The effect of external load was very much larger than that of upper extremity postures. Therefore, it is recommended that a new posture classification scheme taking effect of external load to into consideration be developed for quantifying postural load.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 하계학술대회 논문집 D
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• pp.2789-2791
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• 2003

Today, many people are sitting on chair in their life. If a sitting posture is not correct, there is some trouble with the waist. And if it goes on long time, sometimes it cause the hurts of waist or the deformded spinal column. A crouched posture is an obstacle to breath and it give rise to drowsiness because the lack of oxygen. Sitting posture is a habit so people can't feel it oneself and look over some kind of risks. It analyzes a condition of muscles by measuring EMG of spinal both side of spin al-bones. It can have a right sitting posture by the analyzing that increasing of muscles tention in one or the other side of muscles when the posture inclines on one side or ahead.

• 재활복지공학회논문지
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• 제11권3호
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• pp.261-270
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• 2017

바르지 못한 앉은 자세는 다양한 질병과 신체 변형을 유발한다. 하지만 오랜 시간동안 바른 앉은 자세를 유지하는 것은 쉬운 일이 아니다. 이러한 이유 때문에 그동안 자동으로 바른 앉은 자세를 유도하기 위한 다양한 시스템이 제안되어왔다. 이전에 제안되었던 앉은 자세 판별 및 바른 앉은 자세 유도 시스템은 영상 처리를 이용한 방법, 의자에 압력센서를 달아 측정하는 방법, IMU(Internal Measurement Unit)를 이용한 방법이 있었다. 이 중 IMU를 이용한 측정 방법은 하드웨어 구성이 간단하고, 공간, 광량 등의 환경적 제한이 적어 측정에 있어서 용이한 이점이 있었다. 본 논문에서는 하나의 IMU를 이용하여 적은 데이터로 효율적으로 앉은 자세를 분류하는 방법을 연구하였다. 특징추출 기법을 이용하여 데이터 분류에 기여도가 낮은 데이터를 제거하였으며, 머신러닝 기법을 이용하여 앉은 자세 분류에 적합한 센서 위치를 찾고, 여러 개의 머신러닝 모델 중 가장 분류 정확도가 높은 머신러닝 모델을 선정하였다. 특징추출 기법은 PCA(Principal Component Analysis)를 사용하였고, 머신러닝 모델은 SVM(Support Vector Machine), KNN(K Nearest Neighbor), K-means (K-means Algorithm) GMM (Gaussian Mixture Model), and HMM (Hidden Marcov Model)모델을 사용하였다. 연구결과 데이터 분류율이 높게나온 뒷목이 적합한 센서 위치가 되었으며, 센서 데이터 중 Yaw데이터는 분류 기여도가 가장 낮은 데이터임을 PCA 특징추출 기법을 이용하여 확인하고, 제거하여도 분류율에 영향이 매우 작음을 확인하였다. 적합 머신러닝 모델은 SVM, KNN 모델로 다른 모델에 비하여 분류율이 높게 나오는 것을 확인할 수 있었다.

• 한국안전학회지
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• 제15권4호
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• pp.139-149
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• 2000

This study aims to review and assess the existing postural classification schemes used for evaluating postural loads in industry. The schemes can be classified into three categories: self-report, observational and instrument-based techniques depending upon how to record working postures. Of the three techniques, this study was mainly focused on the observational methods. The observational technique is most widely used in the industrial sites because it does not interfere with work, and is easy and simple to use and cost-effective without requiring the use of expensive equipment for estimating the angular deviation of a body segment from the neutral position. In spite of the usefulness and applicability, the techniques have some problems: 1) The existing observational techniques lack the consistency in the class limits of the motion categories in each body segment; 2) Most of them do not provide the post-analysis criteria needed to judge whether or not any posture is acceptable in view point of the postural load; and 3) They can not precisely evaluate the postural load for a given posture because the external loads and dynamic factors including acceleration, moment and force were not taken into consideration.

• 한국안전학회지
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• 제19권4호
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• pp.123-128
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• 2004

This study investigated effect of arm posture, repetition of wrist motion and external load on perceived discomfort. The arm postures were controlled by shoulder flexion, elbow flexion, and ist motions such as flexion, extension, radial deviation and ulnar deviation. An experiment was conducted to measure discomfort scores for experimental treatments using the magnitude estimation, in which the L16 orthogonal array was adopted for reducing the size of experiment. The results showed that while the effect of the shoulder flexion, repetition of wrist motion and external load was statistically significant at $\alpha=0.05$or 0.10, that of the elbow and wrist motions was not. Discomfor ratings increased linearly as levels of wrist repetition and external load increased. This implies that the existing posture classification schemes such as OWAS, RULA, which do not properly consider effect of motion repetition and external load, may underestimate postural load. Based on the regression equation for wrist repetition and external load, isocomfort region indicating the region within which discomfort scores were expected to be the same was proposed. It is recommended that when assessing risk of postures or developing new posture classification schemes, motion repetition and external load as well as posture itself be fully taken into consideration for precisely evaluating postural stress.

• 재활복지공학회논문지
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• 제9권4호
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• pp.285-291
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• 2015

본 논문에서는 IMU(inertial measurement unit)의 데이터를 이용하여 사람의 앉은 자세를 분류하는 알고리즘을 제안한다. 제안하는 알고리즘은 IMU의 데이터를 주성분 분석법(principle component analysis: PCA)을 이용하여 특징 벡터를 3개로 축소시켰고, RBF(radial basis function) 커널을 적용한 서포트 벡터 머신(support vector machine: SVM)을 이용하여 자세를 분류하였다. 데이터의 측정을 위하여 건강한 성인 3명을 대상으로 실험을 실시하였고, 데이터의 수집을 위하여 넥밴드 형태의 이어폰에 IMU를 내장한 장치를 개발하여 착용하였다. 피험자는 각각 neutral position, smartphoning, writing의 세 가지 앉은 자세에 대하여 실험을 진행하였다. 실험 결과 제안하는 PCA-SVM 알고리즘은 특징 벡터의 차원을 25%로 축소시키면서도 95%의 신뢰를 보였다.

• 로봇학회논문지
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• 제15권3호
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• pp.240-247
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• 2020

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.

• 한국의류학회지
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• 제28권11호
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• pp.1426-1437
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• 2004

The study has an objective of providing the basic data for the bodice basic pattern that is highly appropriate after classifying the torso shapes of women in 60s. In order to classify the torso shape, 200 women in 60s that reside in Seoul were investigated for 52 tests. The factor analysis produced total of 6 factors. Factor 1 tended to be posture of upper part of torso and shape of shoulder. Factor 2 was an element of silhouette and Factor 3 was vertical size of lower part of torso and side silhouette. Factor 4 showed to be width and thickness of torso, Factor 5 was shape of neck, and Factor 6 appeared to be sagging of belly and buttocks. Therefore, it can be known that posture, silhouette, shape of neck and shoulder, sagging of belly and buttocks, and etc. are important factors for classification of the torso shape of women in 60s. Through a cluster analysis, each torso shape was classified into 4 types and each type showed information on size, shape, and posture clearly. Type 1 showed percentage of 24.2%, and values of height and weight showed to be average. Also, the body shape hardly had any curve with high shoulder at the Posture of upper body, and they had saggy stomach and buttocks. 43.5% of them were involved in Type 2 and they were short and overweighted. They were comparatively large in width compared to the height with no curves. Type 2 had the largest percentage and this can be said to be the special shape of women in 60s. People of Type 3 were short and overweighted just like Type 2 and all the sizes were similar to those of Type 2 or bigger. The posture is right posture and 21.7％ fall into this type and there is no body curve. This type is the shortest and most overweighted type, and it is a torso shape with right posture just like Type 4. Type 4 is a torso shape with tallest height and least weight. The percentage was the smallest(10.6％) and the width was smaller than any other type but the height was the tallest. The body curve is very clear and they have thin body but big buttocks so it can be said that the people of this type have the best silhouette. Type 2 that had the highest percentile is short and overweighted so it can be said that Type 2 is the representative torso shape of women in 60s.

• 한국전문물리치료학회지
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• 제19권3호
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• pp.115-123
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• 2012

It is important to assess foot posture when investigating the relationship between lower extremity dysfunctions and foot types. Although several measurements of static foot posture have been used, there is no consensus regarding clinical measurements for foot posture. The aim of this study is to explore the differences among navicular drift (NDt), foot posture index (FPI), arch index (AI), dorsal arch height ratio (DAHR), normal navicular height truncated (NNHt) and to discover the most effective measurement. After foot types were classified by navicular drop test (NDp), clinical measurements of NDt, FPI, AI, DAHR, and NNHt were performed on 64 subjects' feet. ANOVA analysis was used for the variance of the difference between the NDp and the five kinds of clinical measurements, and the level of significance was set at ${\alpha}$=.05. The results showed that all five clinical measurements demonstrated significant differences with navicular drop. In post-hoc, FPI and NNHt showed significant differences in all foot types. The five clinical measurements are suitable the classification of foot types through the NDp. Therefore, it could be possible to assess correct and objective foot posture by using FPI and NNHt.

• 대한의용생체공학회:의공학회지
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• 제37권5호
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• pp.161-167
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• 2016

Recent technological advances in sensor fabrication and bio-signal processing enabled non-constraint and non-intrusive measurement of human bio-signals. Especially, non-constraint measurement of ECG makes it available to estimate various human health parameters such as heart rate. Additionally, non-constraint ECG measurement of wheelchair user provides real-time health parameter information for emergency response. For accurate emergency response with low false alarm rate, it is necessary to discriminate quality levels of ECG measured using non-constraint approach. Health parameters acquired from low quality ECG results in inaccurate information. Thus, in this study, a machine learning based approach for three-class classification of ECG quality level is suggested. Three sensors are embedded in the back seat, chest belt, and handle of automatic wheelchair. For the two sensors embedded in back seat and chest belt, capacitively coupled electrodes were used. The accuracy of quality level classification was estimated using Monte Carlo cross validation. The proposed approach demonstrated accuracy of 94.01%, 95.57%, and 96.94% for each channel of three sensors. Furthermore, the implemented algorithm enables classification of user posture by detection of contacted electrodes. The accuracy for posture estimation was 94.57%. The proposed algorithm will contribute to non-constraint and robust estimation of health parameter of wheelchair users.