KnE Engineering

ISSN: 2518-6841

The latest conference proceedings on all fields of engineering.

Smart Chair for Monitoring of Sitting Behavior

Published date: Feb 09 2017

Journal Title: KnE Engineering

Issue title: The International Conference on Design and Technology

Pages: 274-280

DOI: 10.18502/keg.v2i2.626

Authors:
Abstract:

Sitting is a common behavior of human body in daily life. It is found that poor sitting postures can link to pains and other complications for people in literature. In order to avoid the adverse effects of poor sitting behavior, we have developed a highly practical design of smart chair system in this paper, which is able to monitor the sitting behavior of human body accurately and non-invasively. The pressure patterns of eight standardized sitting postures of human subjects were acquired and transmitted to the computer for the automatic sitting posture recognition with the application of artificial neural network classifier. The experimental results showed that it can recognize eight sitting postures of human subjects with high accuracy. The sitting posture monitoring in the developed smart chair system can help or promote people to achieve and maintain healthy sitting behavior, and prevent or reduce the chronic disease caused by poor sitting behavior. These promising results suggested that the presented system is feasible for sitting behavior monitoring, which can find applications in many areas including healthcare services, human-computer interactions and intelligent environment.

References:

[1] A. M. Lis, K. M. Black, H. Korn, and M. Nordin, Association between sitting and occupational LBP, European Spine Journal, 16, 283–298, (2007), 10.1007/s00586-006-0143-7.


[2] J. Van Dieen, M. De Looze, and V. Hermans, Effects of dynamic office chairs on trunk kinematics, trunk extensor EMG and spinal shrinkage, Ergonomics, 44, 739–750, (2001), 10.1080/00140130120297.


[3] M. Huang, K. Hajizadeh, I. Gibson, and T. Lee, Analysis of compressive load on intervertebral joint in standing and sitting postures, Technology and Health Care, 24, 215–223, (2016), 10.3233/THC-151100.


[4] H. Z. Tan, L. Slivovsky, and A. Pentland, A sensing chair using pressure distribution sensors, Mechatronics, IEEE/ASME Transactions on, 6, 261–268, (2001), 10.1109/3516.951364.


[5] M. Zhu, A. M. Martinez, and H. Z. Tan, Template-based recognition of static sitting postures, in Computer Vision and Pattern Recognition Workshop, 2003. CVPRW’03. Conference, pp. 50–50, (2003).


[6] B. Mutlu, A. Krause, J. Forlizzi, C. Guestrin, and J. Hodgins, Robust, low-cost, non-intrusive sensing and recognition of seated postures, in Proceedings of the 20th annual ACM symposium on User interface software and technology, pp. 149–158, (2007).


[7] W. Xu, Z. Li, M.-C. Huang, N. Amini, and M. Sarrafzadeh, ecushion: An etextile device for sitting posture monitoring, in Body Sensor Networks (BSN), 2011 International Conference on, 2011, pp. 194–199.

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