Computer Science ›› 2018, Vol. 45 ›› Issue (7): 237-242.doi: 10.11896/j.issn.1002-137X.2018.07.041

• Graphics, Image & Pattem Recognition • Previous Articles     Next Articles

Sitting Posture Detection System Based on Depth Sensor

ZENG Xing, SUN Bei ,LUO Wu-sheng, LIU Tao-cheng ,LU Qin   

  1. College of Mechatronic Engineering and Automation,National University of Defense Technology,Changsha 410073,China
  • Received:2017-05-17 Online:2018-07-30 Published:2018-07-30

Abstract: For the purpose of the detection of bad postures and analysis of people’s sitting habit,a sitting posture detection system based on depth sensor was designed.The system first uses the Astra3D sensor to obtain the depth image of human body’s sitting posture and designs the fast and effective foreground extraction method based on the thre-shold segmentation method.The sitting foreground segmentation images are projected into three Cartesian planes respectively and three projection maps are obtained.The background removal,interpolation scaling and normalization are performed for each projection map to obtain the projection features.After the PCA dimensionality,the projection features and the pyramid HOG feature of the front view form the final sitting posture feature vector.Then,random forest is used toclassify and identify 14 kinds of sitting posture.In the experiment,the sitting posture depth image database of 20 people is used for uniform testing and cross testing.The test results show that the method of sitting posture recognition has good recognition rate and recognition speed,and it is superior to the existing method in the type of sitting posture and recognition.Finally,the method was implemented on the Android platform and the application software of the sitting posture detection system was designed to realize the effective detection of sitting posture and timely reminder for the bad sitting posture.

Key words: Android, Depth sensor, Random forest, Sitting posture

CLC Number: 

  • TP391.4
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