According to a recent study 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. In this study, we proposed a system that sitting posture classify using 3-axis accelerometer. 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 1.9%, SVM's correct rate was higher by 0.8%, 4.7%, 15.5%, 6.9% in a normal posture, sitting front arch, sitting cross-legged, sitting leaning right, respectively. In conclusion, the overall correction rates were 95.33% and 89.35% in SVM and K-means clustering respectively, which means that SVM have more advantage than K-means method for classification of sitting posture.
['Sangyong Ma', 'Woo-Hyeong Cho', 'Cheng-Hao Quan', 'Sangmin Lee']
A sitting posture recognition system based on 3 axis accelerometer