Design and Implementation of Sensor-Embedded Chair for Continuous Sitting Posture Recognition

Teruhiro, MIZUMOTO, Yasuhiro, OTODA, Chihiro, NAKAJIMA, Mitsuhiro, KOHANA, Motohiro, UENISHI, Keiichi, YASUMOTO, Yutaka, ARAKAWA: Design and Implementation of Sensor-Embedded Chair for Continuous Sitting Posture Recognition. In: IEICE Transactions on Information and Systems, E103.D (5), pp. 1067-1077, 2020.

Abstract

In this paper, we design and develop a sensor-embedded office chair that can measure the posture of the office worker continuously without disturbing their job. In our system, eight accelerometers, that are attached at the back side of the fabric surface of the chair, are used for recognizing the posture. We propose three sitting posture recognition algorithms by considering the initial position of the chair and the difference of physique. Through the experiment with 28 participants, we confirm that our proposed chair can recognize the sitting posture by 75.4% (algorithm 1), 83.7% (algorithm 2), and 85.6% (algorithm 3) respectively.

BibTeX (Download)

@article{TeruhiroMIZUMOTO20202019EDP7226,
title = {Design and Implementation of Sensor-Embedded Chair for Continuous Sitting Posture Recognition},
author = {Teruhiro, MIZUMOTO and Yasuhiro, OTODA and Chihiro, NAKAJIMA and Mitsuhiro, KOHANA and Motohiro, UENISHI and Keiichi, YASUMOTO and Yutaka, ARAKAWA},
url = {https://www.jstage.jst.go.jp/article/transinf/E103.D/5/E103.D_2019EDP7226/_pdf/-char/en},
doi = {10.1587/transinf.2019EDP7226},
year  = {2020},
date = {2020-05-01},
journal = {IEICE Transactions on Information and Systems},
volume = {E103.D},
number = {5},
pages = {1067-1077},
abstract = {In this paper, we design and develop a sensor-embedded office chair that can measure the posture of the office worker continuously without disturbing their job. In our system, eight accelerometers, that are attached at the back side of the fabric surface of the chair, are used for recognizing the posture. We propose three sitting posture recognition algorithms by considering the initial position of the chair and the difference of physique. Through the experiment with 28 participants, we confirm that our proposed chair can recognize the sitting posture by 75.4% (algorithm 1), 83.7% (algorithm 2), and 85.6% (algorithm 3) respectively.},
keywords = {Activity recognition, Smart Office},
pubstate = {published},
tppubtype = {article}
}