  Post Traumatic Stress Disorder is a psychiatric condition experienced by
individuals after exposure to a traumatic event. Prior work has shown promise
in detecting PTSD using physiological data such as heart rate. Despite the
promise shown by the machine learning based algorithms for PTSD, the validation
approaches used in previous research largely rely on theoretical and
computational validation methods rather than naturalistic evaluations that
account for users perceived precision and validity. Previous research has shown
that users perceptions of physiological changes may not always align well with
automated detection of such variables and such misalignment may lead to
distrust in automated detection which may affect adoption or sustainable usage
of such technologies. Therefore, the goal of this article is to investigate the
perceived precision of the PTSD hyperarousal detection tool (developed
previously) in a home study with a group of PTSD patients. Naturalistic
evaluation of such data driven algorithms may provide foundational insight into
the efficacy of such tools for non intrusive and cost efficient remote
monitoring of PTSD symptoms and will pave the way for their future adoption and
sustainable use. The results showed over sixty five percent of perceived
precision in naturalistic validation of the detection tool. Further, the
results indicated that longitudinal exposure to the detection tool might
calibrate users trust in automation.
