Suicide surpassed homicide to be the second leading cause of death among people 10-24 years old in the United States \cite{1}. This statistic is alarming especially when combined with the more than eight distinctly different types of clinical depression among society today \cite{2}. To further complicate this health crisis, let’s consider the current worldwide isolating pandemic often referred to as COVID-19 that has spanned 12 months. It is more important than ever to consider how we can get ahead of the crisis by identifying the symptoms as they set in and more importantly ahead of the decision to commit suicide. To capitalize on the modern shift to electronic-based interactions \cite{1}, the use of Artificial Intelligence (AI) and Machine Learning (ML) methods to aid in identification have been previously implemented in Virtual Human interviewing platforms. This effort examines these existing approaches and includes an independent survey that is used to solve the gap in early identification of depression and suicidal ideation using a virtual human interviewing platform by soliciting honest, open, and current feedback from Soldiers on how to optimize such a system to encourage its use in the future. Specifically, the analysis of the survey results identify critical gaps from a participants perspective to be security, customization's, and error handling recommended to be included in future development of the EMPOWER (Enhancing Mental Performance and Optimizing Warfighter Effectiveness and Resilience: From MultiSense to OmniSense) platform. These recommendations are provided to the USC-ICT EMPOWER team to be included in the next prototype and system test.
| Identifer | oai:union.ndltd.org:CALPOLY/oai:digitalcommons.calpoly.edu:theses-3969 |
| Date | 01 December 2021 |
| Creators | Monahan, Christina M |
| Publisher | DigitalCommons@CalPoly |
| Source Sets | California Polytechnic State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Master's Theses |
Page generated in 0.0019 seconds