Key result
An intelligent knowledge service system based on cloud computing and mobile devices was designed to dynamically monitor college students' depression status using sleep, exercise, and heart rate data.
Why the study?
College students suffering from depression have increased, prompting the need for an intelligent monitoring system incorporating sleep, exercise, and heart rate to help administrations monitor and support students' mental health.
The paper proposes a cloud-based intelligent monitoring system using sleep, exercise, and heart rate data to detect and manage depression in college students.
May enable real-time student depression monitoring via wearables; leaves open clinical validation before any practice use.
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.
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Yu et al. (2019) studied Depression. Intelligent Knowledge Service System was evaluated. An intelligent knowledge service system based on cloud computing and mobile devices was designed to dynamically monitor college students' depression status using sleep, exercise, and heart rate data.
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