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Post Traumatic Stress Disorder is a psychiatric condition experienced byindividuals after exposure to a traumatic event. Prior work has shown promisein detecting PTSD using physiological data such as heart rate. Despite thepromise shown by the machine learning based algorithms for PTSD, the validationapproaches used in previous research largely rely on theoretical andcomputational validation methods rather than naturalistic evaluations thataccount for users perceived precision and validity. Previous research has shownthat users perceptions of physiological changes may not always align well withautomated detection of such variables and such misalignment may lead todistrust in automated detection which may affect adoption or sustainable usageof such technologies. Therefore, the goal of this article is to investigate theperceived precision of the PTSD hyperarousal detection tool (developedpreviously) in a home study with a group of PTSD patients. Naturalisticevaluation of such data driven algorithms may provide foundational insight intothe efficacy of such tools for non intrusive and cost efficient remotemonitoring of PTSD symptoms and will pave the way for their future adoption andsustainable use. The results showed over sixty five percent of perceivedprecision in naturalistic validation of the detection tool. Further, theresults indicated that longitudinal exposure to the detection tool mightcalibrate users trust in automation.

Investigating the Perceived Precision and validity of a Field-Deployable  Machine Learning-based Tool to Detect Post-Traumatic Stress Disorder (PTSD)  Hyperarousal Events