Research on the Design of Mental Health Service for the Elderly Based on Smart Home Environment (79800)

Session Information:

Session: On Demand
Room: Virtual Video Presentation
Presentation Type:Virtual Presentation

All presentation times are UTC + 1 (Europe/London)

In the context of rapid global ageing, more than 14% of the elderly suffer from mental health problems such as depression and anxiety, which directly affect their physical health and quality of life. To detect and deal with these problems promptly, we propose that the rapid development of smart home-based technologies provides cutting-edge opportunities for improving the health of the elderly. Our design aims to meet the needs of the elderly for smart and healthy elderly care. By comprehensively understanding the prevalence of geriatric diseases in a smart home environment, we use non-contact sensing technology to collect real-time mental state data of elderly users and use machine learning for prediction and evaluation. We have established a dynamic monitoring mechanism for the mental health of the elderly centred on elderly users. The system not only provides real-time mental health monitoring and professional psychological guidance, depression prevention and management, remote consultation and safety monitoring and other comprehensive services for elderly users, but also effectively reduces the cost of traditional psychological services and solves the problem of medical resources and solves the problems of shortage and inequality of medical resources.
By proposing this service design plan, we hope to improve the mental health problems of the elderly, thereby optimizing the quality of life of the elderly so that they can enjoy their later years healthier and happier.

Authors:
Qi Chen, Guangzhou Academy of Fine Arts, China
Nan Sheng, Guangzhou Academy of Fine Arts; Lancaster University, China


About the Presenter(s)
Dr Nan Sheng is a University Doctoral Student at Lancaster University in United Kingdom

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Posted by Clive Staples Lewis

Last updated: 2023-02-23 23:45:00