Touching Minds: Deep Generative Models Composing the Digital Contents to Practice Mindfulness

ABSTRACT

Interest in the proper treatment of mental health has been rapidly growing under the steep changes in society, family structure and lifestyle. COVID-19 pandemic in addition drastically accelerates this necessity worldwide, which brings about a huge demand on digital therapeutics for this purpose. One of the key ingredients to this attempt is the appropriately designed practice contents for the prevention and treatment of mental illness. In this paper, we present novel deep generative models to construct the mental training contents based upon mindfulness approach, with a particular focus on providing Acceptance and Commitment Therapy (ACT) on the self-talk techniques. To this end, we first introduce ACT script generator for mindfulness meditation. With over one-thousand sentences collected from the various sources for ACT training practices, we develop a text generative model through fine-tuning on the variant of GPT-2. Next, we introduce a voice generator to implement the self-talk technique, a text-to-speech application using the ACT training script generated above. Computational and human evaluation results demonstrate the high quality of generated training scripts and self-talk contents. To the best of our knowledge, this is the first approach to generate the meditation contents using artificial intelligence techniques, which is able to deeply touch the human mind to care and cure the mental health of individuals. Applications would be main treatment contents for digital therapeutics and meditation curriculum design. © 2022, Springer Nature Switzerland AG.