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마상용(Sangyong Ma),홍상표(Sangpyo Hong),심현민(Hyeon-min Shim),권장우(Jang-Woo Kwon),이상민(Sangmin Lee) 대한전기학회 2016 전기학회논문지 Vol.65 No.9
According to recent studies, poor sitting posture of the spine has been shown to lead to a variety of spinal disorders. For this reason, it is important to measure the sitting posture. We proposed a strategy for classification of sitting posture using machine learning. We retrieved acceleration data from single tri-axial accelerometer attached on the back of the subject’s neck in 5-types of sitting posture. 6 subjects without any spinal disorder were participated in this experiment. Acceleration data were transformed to the feature vectors of principle component analysis. Support vector machine (SVM) and K-means clustering were used to classify sitting posture with the transformed feature vectors. To evaluate performance, we calculated the correct rate for each classification strategy. Although the correct rate of SVM in sitting back arch was lower than that of K-means clustering by 2.0%, SVM’s correct rate was higher by 1.3%, 5.2%, 16.6%, 7.1% in a normal posture, sitting front arch, sitting cross-legged, sitting leaning right, respectively. In conclusion, the overall correction rates were 94.5% and 88.84% in SVM and K-means clustering respectively, which means that SVM have more advantage than K-means method for classification of sitting posture.
불균형 자세 예방용 IMU 내장 넥밴드를 이용한 앉은 자세 분류
마상용(S. Y. Ma),심현민(H. M. Shim),이상민(S. M. Lee) 한국재활복지공학회 2015 재활복지공학회논문지 Vol.9 No.4
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. In this paper, we propose a classification algorithm for postures of sitting person by using IMU(inertial measurement unit). This algorithm uses PCA(principle component analysis) for decreasing the number of feature vectors to three and SVM(support vector machine) with RBF(radial basis function) kernel for classifying posture types. In order to collect the data, we designed neckband-shaped earphones with IMU, and applied it to three subjects who are healthy adults. Subjects were experimented three sitting postures, which are neutral posture, smartphoning, and writing. As the result, our PCA-SVM algorithm showed 95% confidence while the dimension of the feature vectors was reduced to 25%.