An Exploration of Machine Learning and Reinforcement Learning for Emotional Well-Being

Abstract: With the high levels of stress in Singapore, mental and emotional well-being is an important health and social issue today. Research has shown the positive effects of pet ownership on mental and emotional well-being, however challenges of owning a pet in Singapore such as pet licensing restrictions, high costs, fear of losing a pet, a busy lifestyle and even allergies may deter pet lovers from owning a pet. Thus, we propose a technology-driven solution to emulate the useful effects of pets while mitigating the challenges of pet ownership. This project focuses on designing emotion recognition and reinforcement learning models as a stepping stone to individualise responses to a person’s emotions. Our approach utilises the output from the emotion recognition model as an input in the proposed reinforcement learning algorithm. Hence, the paper first compares pre-trained and custom trained facial recognition models, and postulates the use of physiological signals via hardware sensors to further enhance the emotion recognition model. This is inspired from the ability of pets to perceive and respond to different emotions based on facial expressions and physiological signals like heart rate. The paper then outlines the development of novel K-Bandit algorithms in reinforcement learning tested on simulated reward functions, with the aim of optimising parameters for individualised responses to a person’s emotions. Since reinforcement learning is typically used in simulation scenarios, this paper works towards developing a model that will eventually learn a person’s preferences in real time by monitoring their emotional changes. To conclude, this project has showcased the feasibility of facial expressions and physiological signals for emotion recognition, and established the effectiveness of our proposed parameter optimisation functions in the K armed bandit reinforcement learning model to customise responses based on an individual’s emotions. We hope this paper can act as a basis for future works in creating a human-friendly prototype to emulate man’s best friend.

Keywords: human systems integration, machine learning, reinforcement learning, empathetic robotics, human robot interaction

DOI: 10.54941/ahfe1005482

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