An Intelligence and Creative Computing Based Mental Health Care Framework

Artificial intelligence is being developed and applied in business, medical, tourism and other subjects. Health care can be related to artificial intelligence, serving more on protecting people’s health through supervising some indexes of people and providing advice for people’s normal life. As health care is required colleting the physical data, voice data and video data, wearable products are suitable. The user is connected with wearable products through minor cameras, sensors, and chips. In this research, an artificial intelligence-based health care framework is achieved for supervising and managing user’s health conditions and being applied with intelligent shoes. The aim of this research is to serve people on mental health care in normal life based on physical data analysis and mental data analysis, to protect people from being sub-health, to advise people on different aspects of being healthy. An applicable system can be generated with specific AI algorithms and machine learning algorithms, including body index data statistical model analysis, voice data analysis algorithm, and video data analysis. The framework can collect health related data and analyse it, generating health state reports and advice report for the user. The entire system is based on artificial intelligence theories and methods, and machine learning algorithms, completing health data processing. Abstraction method is combined with conventional nueral network and recurrent neural network are used for voice data analysis and video data analysis. Statistical model is created based on body index data categories. Three kinds of data can be processed for supervising the emotions of the user. This research will generate a mental health care framework and a mental health supervision and management system that can be set on the wearable products for the application.

Name dc:type.qualificationname

PhD

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

De Montfort University

Year dc:date.issued

2020

Author dc:creator

• Ma, Fengbao

Licence

• https://dora.dmu.ac.uk/bitstreams/fc6eb76a-1c7d-452e-8713-c3573d23e359/download