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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.번호 | 참고문헌 | 국회도서관 소장유무 |
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1 | (The) effect of the forward head posture on postural balance in long time computer based worker | 소장 |
2 | Effect of Neck Exercise on Sitting Posture in Patients With Chronic Neck Pain ![]() |
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3 | O. Evans and K. Patterson, "Predictors of neck and shoulder pain in non-secretarial computer users,"International Journal of Industrial Ergonomics, vol. 26, no. 3, pp. 357-365, 2000 | 미소장 |
4 | Kapandji, I. A, "The physiology of the Joints," Elsevier Science Health Science div, vol. 3 2008, pp.145-208 | 미소장 |
5 | E.M. Joseph, “Kinesiology, the skeletal system and muscle function,” 2011, pp.245-249 | 미소장 |
6 | P.B. Bruce, “Musculoskeletal disorders and workplace factors,” U.S. department of health and human services, 1997, pp.97-141. | 미소장 |
7 | J.K. Ko, "ET form, PC room form, ... This is four kinds of poor sitting postures", 2005.06.21., joongang, http://news.joins.com/article/1620642 | 미소장 |
8 | S.J. Lee and S.K. Jung, “Posture symmetry based motion capture system for analysis of lower-limbs rehabilitation training,” Journal of Multimedia Information System, vol. 14, no. 12, pp. 1517-1527, 2011. | 미소장 |
9 | M. R. Kim, H. W. Kim and W. D. Cho, “posture helper using gaussian mixture background modeling,” in Proc the Korean Institute of communications and Information Sciences, Pyeongchang, Korea, 2010, pp. 25-26. | 미소장 |
10 | Design of Algorithm for Guidance of Sitting Posture Correction Using Pressure Sensor and Image Processing Interpolation Technique | 소장 |
11 | L. Bao, and S. S. Intille, "Activity Recognition from User-Annotated Acceleration Data,” In Proceceedings of the 2nd International Conference on Pervasive Computing, 2004, pp.1-17. | 미소장 |
12 | Y. Jung, D. Kang and J. Kim, “Upper Body Motion Tracking with Inertial Sensors,” In Robotics and Biomimetics (ROBIO), IEEE International Conference, Dec. 2010, pp. 1746-1751. | 미소장 |
13 | A real-time and self-calibrating algorithm based on triaxial accelerometer signals for the detection of human posture and activity. ![]() |
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14 | Feature Extraction and Classification of Posture for Four-Joint based Human Motion Data Analysis | 소장 |
15 | The influence of different sitting positions on cervical and lumbar posture. ![]() |
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16 | IEEE Computer Society Election ![]() |
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17 | T. P. Kao, C. W. Lin and J. S. Wang, “Development of a portable activity detector for daily activity recognition,”in IEEE international Symposium on Industrial Electornics, Seoul, Korea, Jul, 2009, pp.115-120. | 미소장 |
18 | A triaxial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer. ![]() |
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19 | Principal component analysis ![]() |
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20 | Least squares quantization in PCM ![]() |
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21 | J. MacQueen, “Some methods for classification and analysis of multivariate observations,” In proceedings of the fifth berkely symposium on mathematical statistics and probability, vol. 1, 1967, pp.281-297. | 미소장 |
22 | C. Cortes, V. Vapnik, “Support-vector networks,”Machines Learning, vol. 20, no. 3, pp. 273-297, 1995. | 미소장 |
23 | Classification of Sitting Position by IMU Built in Neckband for Preventing Imbalance Posture | 소장 |
24 | A Study on the 4-Joint Based Motion Capture System for Spinal Disease Prevention | 소장 |
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