Abstract
The number of college students suffering from depression has increased in recent years. In order to help the college student administration departments understand students' psychological state of depression better and keep college students mentally healthy through mental health services, this paper studies an intelligent monitoring system for depression. Different from previous researches, this study, based on the cloud services platform, incorporates three indicators closely related to depression-sleeping, exercise and heart rate-into the monitoring database subsystem and establishes a relatively macroscopic intelligent knowledge service system for depression monitoring of college students. It uses the Mobile Material Link Device (MMLD) to collect data and information to monitor and analyze the changes in the depression status of college students dynamically, which also provides timely warnings and a chain of personalized intelligent knowledge services based on individuals’ depression status.
Highlights
The college period is an important stage for the self-improvement and growth of college students, where psychological problems often appear among them, including depression
This paper advocates the use of an intelligent cloud model to improve the mental health services for college students and designs a public service platform based on the cloud model
User data are stored in the cloud and delivered to the users’ intelligent terminal through the node server in two ways: ─ If the results of the initial screening against the scale are normal, the data of the student will be stored in the individual’s health file in the system for future analysis of mental health conditions ─ If the results of the initial screening against the scale are abnormal, the system will automatically mark the student, and recommend the student wearing the Mobile Material Link Device (MMLD), and at the same time push the basic information and the preliminary screening results of the depression tendency scale to the student’s administrator
Summary
The college period is an important stage for the self-improvement and growth of college students, where psychological problems often appear among them, including depression. Based on the knowledge base of the symptom self-rating scale (SCL-90) and the depression self-rating scale (SDS) and that of the monitoring indicators, this paper uses a cloud model to perform real-time data exchanges between the terminal and the cloud, and establishes an intelligent monitoring service system for the depression of college students to better understand, monitor, judge and warn about their depression status. This paper advocates the use of an intelligent cloud model to improve the mental health services for college students and designs a public service platform based on the cloud model This model performs real-time health data exchanges between the terminal and the cloud, and pushes related health information through websites or apps to users
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