TY - GEN
T1 - User's Height Estimation based on Acceleration from Smartphone Sensors
AU - Sato, Yusuke
AU - Yamaguchi, Saneyasu
AU - Kamiyama, Takeshi
AU - Fukuda, Akira
AU - Oguchi, Masato
PY - 2019/5
Y1 - 2019/5
N2 - The recent advance of machine learning enabled a variety of estimation data from sensors. In this study, we investigate estimation of the heights of the users of smartphones from the sensed data for the next step of the studies of estimation based on the machine learning. We propose a method for estimating the two-classed height, which is tall or short, by the linear regression. Our evaluation shows that the proposed method estimated the height class with 92% accuracy in case of persons whose heights are far from the median height.
AB - The recent advance of machine learning enabled a variety of estimation data from sensors. In this study, we investigate estimation of the heights of the users of smartphones from the sensed data for the next step of the studies of estimation based on the machine learning. We propose a method for estimating the two-classed height, which is tall or short, by the linear regression. Our evaluation shows that the proposed method estimated the height class with 92% accuracy in case of persons whose heights are far from the median height.
UR - http://www.scopus.com/inward/record.url?scp=85080113610&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85080113610&partnerID=8YFLogxK
U2 - 10.1109/ICCE-TW46550.2019.8991753
DO - 10.1109/ICCE-TW46550.2019.8991753
M3 - Conference contribution
T3 - 2019 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2019
BT - 2019 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2019
Y2 - 20 May 2019 through 22 May 2019
ER -