Artificial Intelligence Concepts for Mental Health Application Development: Therapily for Mental Health Care

Authors

  • Hock Chuan Lim Faculty of Engineering and Information Science University of Wollongong in Dubai, UAE
  • Saniya Kolangde Faculty of Engineering and Information Science University of Wollongong in Dubai, UAE
  • Divya Mohan Faculty of Engineering and Information Science University of Wollongong in Dubai, UAE
  • Abeer Hezam Faculty of Engineering and Information Science University of Wollongong in Dubai, UAE
  • Madeeha Amatur Faculty of Engineering and Information Science University of Wollongong in Dubai, UAE
  • Shafiz Mohd Yusof Faculty of Engineering and Information Science University of Wollongong in Dubai, UAE

DOI:

https://doi.org/10.54536/ajmri.v1i6.1038

Keywords:

Mental Healthcare App, Artificial Intelligence, Deep Learning, Chatbot, Emotion Detection, Healthcare Education, NLP

Abstract

Mental health has become an increasingly important topic to address as a surge in mental health issues can be seen across the globe significantly owing to the COVID-19 pandemic. Several factors such as unawareness, daily life interruptions, and social stigma lead to people hesitating in acquiring appropriate mental health support, thereby arising the need for effective and round-the-clock mental health support solutions. Hence, the purpose of this paper is to address how Artificial Intelligence (AI) in conjunction with the portability of smartphones, can be leveraged to provide a type of first aid for mental health to users in their moment of educational need. This article proposes a new mental health assistance solution in the form of a mobile application called Therapily. The ideation, design, and implementation aspects of this application will be explored along with discussing the technical details of the key modules to aid the education of the health domain. An emotion detection module employing a Deep Neural Network to analyze the user’s facial expressions for recognizing in-the-moment emotion(s) followed by suggesting therapeutic activities based on the results, will be explored. Moreover, the creation of a Natural Language Processing (NLP) - based chatbot and the functionality of online consultations with licensed and experienced therapists will also be covered. Finally, the different ways in which Therapily is likely to be extended or modified in the future will also be identified.

Downloads

Download data is not yet available.

References

Bhargava, S. (2020). Important chatbot terms- utterance, intent, entity and NLP. Retrieved on 19 February 2022. from https://medium.com/@shruti.bhargava30/important-chatbot-terms-utterance-intent-entity-and-nlp-27c49a7babd9

Chandrashekar, P. (2018). Do mental health mobile apps work: evidence and recommendations for designing high-efficacy mental health mobile apps. Mhealth, 4.

Cheikh Ismail, L. (2021). Impact of the Coronavirus Pandemic (COVID-19) Lockdown on Mental Health and Well- Being in the United Arab Emirates. Frontiers in Psychiatry, 12. Retrieved from https://www.frontiersin.org/article/10.3389/fpsyt.2021.63323

Chen, S-C., Gulatitt, S., Hamid, S., Huang, X., Luo, L., Morisseau-Leroy, N., Powell, M. D., & Zhan, C. (2003). A Three-Tier System Architecture Design and Development for Hurricane Occurrence Simulation. [online]. International Conference on Information Technology: Research and Education. http://users.cis.fiu.edu/~chens/PDF/ITRE03.pdf

Coremltools. (2022). Introduction. [online]. https://coremltools.readme.io/docs/what-are-coreml-tools

Du, G., Long, S., & Yuan, H. (2020). Non-contact emotion recognition combining heart rate and facial expression for interactive gaming environments. IEEE Access, 8, 11896-11906. https://af.booksc.eu/book/80553017/721a50

Education, I. (2021). What is Three-Tier Architecture. [online] Ibm.com. https://www.ibm.com/cloud/learn/three-tier-architecture

Gates, V. (2019). Natural Language Processing for Psychotherapy. The Berkeley Science Review. https://berkeleysciencereview.com/2019/03/nlp-for- psychotherapy/

Giota, K., & Kleftaras, G. (2014). Mental Health Apps: Innovations, Risks and Ethical Considerations. Research Gate. https://www.researchgate.net/publication/265339587_Mental_Health_Apps_Innovations_Risks_and_Ethical_Considerations

Haak, M., Bos, S., Panic, S., & Rothkrantz, L.J.M. (2009). Detecting stress using eye blinks and brain activity from EEG Signals. [online] Proceeding of the 1st driver car interaction and interface, 35-60. https://www.stevenbos.com/dl/publications/Detecting_Stress

Hollemans, M. (2022). Faster neural nets for iOS and macOS. [online] Machinethink.net. https://machinethink.net/faster-neural-networks/

IBM (2022). How to Build a Chatbot - IBM Watson Assistant – Docs resources. [online]. https://www.ibm.com/ae-en/products/watson-assistant/docs- resources

Logi Analytics. (2021). 5 Benefits of a 3-Tier Architecture| Logi Analytics. [online] https://www.logianalytics.com/5-benefits-3-tier- architecture/

Downloads

Published

2022-12-28

How to Cite

Lim, H. C., Kolangde, S., Mohan, D., Hezam, A., Amatur, M., & Yusof, S. M. (2022). Artificial Intelligence Concepts for Mental Health Application Development: Therapily for Mental Health Care. American Journal of Multidisciplinary Research and Innovation, 1(6), 127–135. https://doi.org/10.54536/ajmri.v1i6.1038