@inproceedings{55a0ac7f7b674d879416b0e0b73f18dd,
title = "A behaviour patterns extraction method for recognizing generalized anxiety disorder",
abstract = "Generalized anxiety disorder (GAD), as one of the most common chronic anxiety disorders, faces difficulties in clinical diagnosis. With the rapid development and wide application of smartphones in recent years, smartphones have a vivid application prospect in the field of mental disease monitoring and diagnosis. Based on WeChat applet platform on smartphones, an APP that integrates scale testing and inertial sensor data collection is developed to study the detection of subjects with GAD in task state. A behavior patterns extraction method is proposed using sliding windows to split behavior data, and processing data segments for clustering. Distribution information are extracted from the subjects' behavior patterns and are combined with the descriptive statistical features of the sample to identify GAD. The results show that this method has an accuracy of 66.44\% for female subjects and 71.43\% for male subjects in GAD recognition.",
keywords = "Accelerometer, Behavior patterns, Cluster, Generalized anxiety disorder, Smartphone",
author = "Minqiang Yang and Jingsheng Tang and Yushan Wu and Zhenyu Liu and Xiping Hu and Bin Hu",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 22nd IEEE International Conference on E-Health Networking, Application and Services, HEALTHCOM 2020 ; Conference date: 01-03-2021 Through 02-03-2021",
year = "2021",
month = mar,
day = "1",
doi = "10.1109/HEALTHCOM49281.2021.9398995",
language = "English",
series = "2020 IEEE International Conference on E-Health Networking, Application and Services, HEALTHCOM 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2020 IEEE International Conference on E-Health Networking, Application and Services, HEALTHCOM 2020",
address = "United States",
}