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http://dx.doi.org/10.21288/resko.2017.11.3.261

Research on Classification of Sitting Posture with a IMU  

Kim, Yeon-Wook (인하대학교 전자공학과)
Cho, Woo-Hyeong (인하대학교 전자공학과)
Jeon, Yu-Yong (인하대학교 전자공학과)
Lee, Sangmin (인하대학교 전자공학과)
Publication Information
Journal of rehabilitation welfare engineering & assistive technology / v.11, no.3, 2017 , pp. 261-270 More about this Journal
Abstract
Bad sitting postures are known to cause for a variety of diseases or physical deformation. However, it is not easy to fit right sitting posture for long periods of time. Therefore, methods of distinguishing and inducing good sitting posture have been constantly proposed. Proposed methods were image processing, using pressure sensor attached to the chair, and using the IMU (Internal Measurement Unit). The method of using IMU has advantages of simple hardware configuration and free of various constraints in measurement. In this paper, we researched on distinguishing sitting postures with a small amount of data using just one IMU. Feature extraction method was used to find data which contribution is the least for classification. Machine learning algorithms were used to find the best position to classify and we found best machine learning algorithm. Used feature extraction method was PCA(Principal Component Analysis). Used Machine learning models were five : SVM(Support Vector Machine), KNN(K Nearest Neighbor), K-means (K-means Algorithm) GMM (Gaussian Mixture Model), and HMM (Hidden Marcov Model). As a result of research, back neck is suitable position for classification because classification rate of it was highest in every model. It was confirmed that Yaw data which is one of the IMU data has the smallest contribution to classification rate using PCA and there was no changes in classification rate after removal it. SVM, KNN are suitable for classification because their classification rate are higher than the others.
Keywords
Sitting posture; Classification; Internal Measurement Unit : IMU; Machine learning; Principle Component Analysis : PCA;
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