オカムラと共同開発をしている姿勢認識チェアCENSUSに関する研究をまとめたものが電子情報通信学会論文誌D（英）に採択されました。この椅子は、第1世代のもので、メッシュチェアを対象とし、たわみで姿勢を計測する椅子となります。
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 Journal Article
In: IEICE Transactions on Information and Systems, vol. E103.D, no. 5, pp. 1067-1077, 2020.
@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 = {},
pubstate = {published},
tppubtype = {article}
}
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.
