Abstract
It is becoming increasingly apparent that a significant amount of the population suffers from mental health problems, such as stress, depression, and anxiety. These issues are a result of a vast range of factors, such as genetic conditions, social circumstances, and lifestyle influences. A key cause, or contributor, for many people is their work; poor mental state can be exacerbated by jobs and a person’s working environment. Additionally, as the information age continues to burgeon, people are increasingly sedentary in their working lives, spending more of their days seated, and less time moving around. It is a well-known fact that a decrease in physical activity is detrimental to mental well-being. Therefore, the need for innovative research and development to combat negativity early is required. Implementing solutions using Artificial Intelligence has great potential in this field of research. This work proposes a solution to this problem domain, utilising two concepts of Artificial Intelligence, namely, Convolutional Neural Networks and Generative Adversarial Networks. A CNN is trained to accurately predict when an individual is experiencing negative emotions, achieving a top accuracy of 80.38% with a loss of 0.42. A GAN is trained to synthesise images from an input domain that can be attributed to evoking position emotions. A Graphical User Interface is created to display the generated media to users in order to boost mood and reduce feelings of stress. The work demonstrates the capability for using Deep Learning to identify stress and negative mood, and the strategies that can be implemented to reduce them.
Highlights
It has been reported by Mind [1] that mental health problems in the UK affect 1 in 4 people, and that 1 in 6 people, in England alone, experience depressive or anxious episodes in any given week
The objective of the research was to build a system capable of identifying emotions from facial expressions, and to combat negative emotions by producing media that can be argued to have a positive impact on mood
The usage of such technology would provide a form of support in the immediate time for the individual, potentially reducing effects felt by long-term suffering. Implementation of such a system would contribute to highlighting the importance in taking care of mental health and could encourage people to be more outspoken about any negativity they are experiencing
Summary
It has been reported by Mind [1] that mental health problems in the UK affect 1 in 4 people, and that 1 in 6 people, in England alone, experience depressive or anxious episodes in any given week. A larger percentage of the population occupy office jobs, which are typically very sedentary It is a well-researched concept that sitting down for longer periods of time and an increase in depressive/anxious mood are correlated. These changes in psychological well-being often become a realisation to people over time This is where the opportunity for research lies; by monitoring individuals’ emotions on a regular basis in a secondary capacity, i.e., the person can continue with their working activities as normal, stress and negative emotions may be better managed in the short term, leading to reduced long term effects. This work proposes a solution to the identified problem, whereby two concepts of Artificial Intelligence (AI) are combined to, firstly, identify negative emotions, and secondly, to try to reduce their presence. This paper proposes a novel application for AI and Deep Neural Networks to improve the mental health of system users
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