Research on Office Chair Based on Modern Office Posture
The office sitting position of the staff is closely related to the design of the office chair. We use the dynamic capture system of Microsoft Kinect sensor to study office sitting posture, in order to capture sitting posture and form three-dimensional coordinate data and RGB images, and then with the help of GBR and FCM methods to cluster Kinect data in MTATLAB data analysis software, getting the average sitting posture and sitting position type and transformation rules. The results show that there are four types of office posture. The data shows that these four types of postures can be divided into skill posture, adaptive posture and initiative attitude, and then analyzed corresponding task scene, office tasks and office equipment. Category 1 posture scenario is using computer, which belongs to the skill posture. Category 2 posture scene is mobile working, which is initiative attitude; Category 3 posture scene is to talking on the phone, belonging to the adaptive posture; Category 4 pose is a rest scene, belonging to the adaptive posture. The time of Category 1 of changing sitting position is 11.6 min; The time of Category 2 of changing sitting position is 13.7 min; the time of Category 3 of changing sitting position is 2.8 min; the time of Category 4 of changing sitting position is 17.2 min.
KeywordsBehavior clustering Sitting posture Kinect sensor Office chair
The authors are grateful for the financial support provided by “The general project of Humanities and social sciences of Ministry of Education” (17YJA760022).
- 1.Vink, P., Hallbeck, S.: Comfort and discomfort studies demonstrate the need for a new model. Ergonomics 44(8), 781–794 (2012)Google Scholar
- 7.Zhu, M., Martinez, A.M., Tan, H.Z.: Template-based recognition of static sitting postures. In: 2003 Conference on Computer Vision and Pattern Recognition Workshop, CVPRW 2003, vol. 5, pp. 50. IEEE (2003)Google Scholar
- 9.Grafsgaard, J.F., Boyer, K.E., Wiebe, E.N., et al.: Analyzing posture and affect in task-oriented tutoring. In: FLAIRS Conference (2012)Google Scholar
- 10.Meng, M., Yang, F.B., She, Q.S., et al.: Human motion detection based on the depth image of Kinect. Chin. J. Sci. Instrum. 36(2), 386–393 (2015)Google Scholar
- 11.Wang, Y., Zhang, Z.Q.: Gesture recognition based on kinect depth information. J. Beijing Inf. Sci. Technol. Univ. 28(1), 22–26 (2013). (Natural science edition)Google Scholar
- 12.Cao, G.G., Cao, L.: Human motion recognition based on skeletal information of Kinect sensor. Comput. Simul. 31(12), 329–333 (2014)Google Scholar
- 13.Li, H.B., Ding, L.J., Ran, G.Y.: Analysis of human identification based on Kinect depth image. Digital Telecommun. 39(4), 21–26 (2012)Google Scholar
- 15.Xie, J., Wang, L.: Empowering leadership, trust in supervisor and knowledge workers task behavior: evidence from a survey. South China J. Econ. 32(1), 77–88 (2014)Google Scholar
- 16.Xu, J.F., Zhang, H.N., Cui, T.J.: Office chair design and creation based on sitting behavior. Packag. Eng. 8, 52–56 (2013)Google Scholar
- 17.Huang, L., Yang, Y., Peng, B.: Three-dimensional parametric modeling of human body for analysis of seat comfort. J. Eng. Graph. 32(1), 10–15 (2011)Google Scholar