Loneliness Recognition Based on Mobile Phone Data

DOI

10.2991/isaeece-16.2016.34How to use a DOI?

Keywords

loneliness recognition, smartphone, machine learning, mental health

Abstract

Nowadays, the definition of health is not only the absence of disease, but both physical and mental health. Loneliness as an important measure of mental health has become a topic that can not be ignored. In this paper, we study the problem about loneliness of individuals can be automatically recognized using mobile phone data (app usage data, call log, SMS, GPS data, Bluetooth proximity data and so on). In our study, we used 46 participants’ data, divided them into risk and non-risk group based on self-reported scales for loneliness. We then compared the two groups to analyze the differences of mobile phone usage. And then we selected four kinds of classifiers — Support Vector Machine (SVM), Random Forest, Neural Network, and Gradient Tree Boosting (GTB) —to recognize loneliness automatically based on mobile phone data. The result showed that Random Forest can obtain the best performance with the accuracy of 70.68% for a 2-class loneliness recognition problem.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Zhongqiu Li
AU  - Dianxi Shi
AU  - Feng Wang
AU  - Fan Liu
PY  - 2016/04
DA  - 2016/04
TI  - Loneliness Recognition Based on Mobile Phone Data
BT  - Proceedings of the 2016 International Symposium on Advances in Electrical, Electronics and Computer Engineering
PB  - Atlantis Press
SP  - 165
EP  - 172
SN  - 2352-5401
UR  - https://doi.org/10.2991/isaeece-16.2016.34
DO  - 10.2991/isaeece-16.2016.34
ID  - Li2016/04
ER  -