Negative Information Measurement at AI Edge: A New Perspective for Mental Health Monitoring

Author:

Chen Min1ORCID,Shen Ke2,Wang Rui2,Miao Yiming3,Jiang Yingying2,Hwang Kai3,Hao Yixue2,Tao Guangming4,Hu Long2,Liu Zhongchun5

Affiliation:

1. Sport and Health Initiative, Optical Valley Laboratory and Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology and School of Computer Science and Technology, Wuhan, China

2. Huazhong University of Science and Technology, Huazhong University of Science and Technology, Wuhan, China

3. School of Data Science, The Chinese University of Hong Kong, and the Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China

4. Sport and Health Initiative, Optical Valley Laboratory and Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology and State Key Laboratory of Material Processing and Die & Mould Technology, School of Materials Science and Engineering, Wuhan, China

5. Department of Psychiatry, Renmin Hospital, Wuhan University, Wuhan, China

Abstract

The outbreak of the corona virus disease 2019 (COVID-19) has caused serious harm to people’s physical and mental health. Due to the serious situation of the epidemic, a lot of negative energy information increases people’s psychological burden. However, effective interventions against mental health problems are not in abundance. To address such challenges, in this article, we propose the concept of negative information to describe information that has a negative impact on people’s mental health. To achieve the measurement of negative information, the level of mental health inversely measures the degree of negative information. Specifically, we design a system to measure the negative information used to monitor the mental health state of the user under the impact of negative information. The cognition of mental health is realized based on the intelligent algorithm deployed on the edge cloud, and the needs of users can be responded to in real time in practical applications. Finally, we use real collected dataset to verify the influence of negative information. The experiments show that the system can achieve negative information measurement and provide an effective countermeasure for solving mental health problems during a pandemic situation.

Funder

China National Natural Science Foundation

Shenzhen Institute of Artificial Intelligence and Robotics for Society

Technology Innovation Project of Hubei Province of China

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications

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