TY - GEN
T1 - A method for simplified HRQOL measurement by smart devices
AU - Amenomori, Chishu
AU - Mizumoto, Teruhiro
AU - Suwa, Hirohiko
AU - Arakawa, Yutaka
AU - Yasumoto, Keiichi
N1 - Funding Information:
Acknowledgment. This work is partly supported by JSPS KAKENHI Grant Number 16H06980, Grants-in-Aid for Humanophilic Innovation Project, and Health and Labor Sciences Research Grants (201621010A).
Publisher Copyright:
© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018.
PY - 2018
Y1 - 2018
N2 - Health-related quality of life (HRQOL) is a useful indicator that rates a person’s activities in various physical, mental and social domains. Continuously measuring HRQOL can help detect the early signs of declines in these activities and lead to steps to prevent such declines. However, it is difficult to continuously measure HRQOL by conventional methods, since its measurement requires each user to answer burdensome questionnaires. In this paper, we propose a simplified HRQOL measurement method for a continuous HRQOL measurement which can reduce the burden of questionnaires. In our method, sensor data from smart devices and the questionnaire scores of HRQOL are collected and used to construct a machine-learning model that estimates the score for each HRQOL questionnaire item. Our experiment result showed our method’s potential and found effective features for some questions.
AB - Health-related quality of life (HRQOL) is a useful indicator that rates a person’s activities in various physical, mental and social domains. Continuously measuring HRQOL can help detect the early signs of declines in these activities and lead to steps to prevent such declines. However, it is difficult to continuously measure HRQOL by conventional methods, since its measurement requires each user to answer burdensome questionnaires. In this paper, we propose a simplified HRQOL measurement method for a continuous HRQOL measurement which can reduce the burden of questionnaires. In our method, sensor data from smart devices and the questionnaire scores of HRQOL are collected and used to construct a machine-learning model that estimates the score for each HRQOL questionnaire item. Our experiment result showed our method’s potential and found effective features for some questions.
UR - http://www.scopus.com/inward/record.url?scp=85053143388&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85053143388&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-98551-0_11
DO - 10.1007/978-3-319-98551-0_11
M3 - Conference contribution
AN - SCOPUS:85053143388
SN - 9783319985503
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 91
EP - 98
BT - Wireless Mobile Communication and Healthcare - 7th International Conference, MobiHealth 2017, Proceedings
A2 - Rahmani, Amir M.
A2 - TaheriNejad, Nima
A2 - Perego, Paolo
PB - Springer Verlag
T2 - 7th International Conference on Wireless Mobile Communication and Healthcare, MobiHealth 2017
Y2 - 14 November 2017 through 15 November 2017
ER -