Towards Early Detection of Depression through Smartphone Sensing

被引：18

作者：

Asare, Kennedy Opoku ^{[1
]}

Visuri, Aku ^{[1
]}

Ferriera, Denzil S. T. ^{[1
]}

机构：

[1] Univ Oulu, Oulu, Finland

来源：

UBICOMP/ISWC'19 ADJUNCT: PROCEEDINGS OF THE 2019 ACM INTERNATIONAL JOINT CONFERENCE ON PERVASIVE AND UBIQUITOUS COMPUTING AND PROCEEDINGS OF THE 2019 ACM INTERNATIONAL SYMPOSIUM ON WEARABLE COMPUTERS | 2019年

基金：

芬兰科学院;

关键词：

Mobile Sensing; Mental Health; Depression;

D O I：

10.1145/3341162.3347075

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Major depressive disorder is a complex and common mental health disorder that is heterogeneous and varies between individuals. Predictive measures have previously been used to predict depression in individuals. Given the complexity, heterogeneity of major depressive disorder in individuals, and the scarcity of labelled objective depressive behavioural data, predictive measures have shown limited applicability in detecting the early onset of depression. We present a developed system that collects similar smartphone sensor data like in previous predictive analysis studies. We discuss that anomaly detection and entropy analysis methods are best suited for developing new metrics for the early detection of the onset and progression of major depressive disorder.

引用

页码：1158 / 1161

页数：4

共 19 条

[1] AN INVENTORY FOR MEASURING DEPRESSION [J].