# Posttraumatic Stress Disorder Hyperarousal Event Detection Using Smartwatch Physiological and Activity Data

arXiv: https://arxiv.org/abs/2109.14743 · HTML: https://mlsanity.com/paper/2109.14743/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 72 references that are in the map.
- First posted on arXiv 29 Sep 2021.

## Builds on (most cited first, 2 of 2)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Automatic Stress Detection in Working Environments from Smartphones' Accelerometer Data: A First Step](https://mlsanity.com/paper/1510.04221/) (2015)

## Built on by (most cited first, 5 of 5)

- [Personalized Weight Loss Management through Wearable Devices and Artificial Intelligence](https://mlsanity.com/paper/2409.08700/) (2024)
- [When neural implant meets multimodal LLM: A dual-loop system for neuromodulation and naturalistic neuralbehavioral research](https://mlsanity.com/paper/2503.12334/) (2025)
- [Real-Time Stress Monitoring, Detection, and Management in College Students: A Wearable Technology and Machine-Learning Approach](https://mlsanity.com/paper/2505.15974/) (2025)
- [Machine Learning, Deep Learning and Data Preprocessing Techniques for Detection, Prediction, and Monitoring of Stress and Stress-related Mental Disorders: A Scoping Review](https://mlsanity.com/paper/2308.04616/) (2023)
- [Ride, Track, and Recover: Pilot Randomized Trial of a Wearable Digital Self-Management Intervention During a Veteran Endurance-Cycling Program](https://mlsanity.com/paper/2606.13529/) (2026)

## Abstract (arXiv)

Posttraumatic Stress Disorder (PTSD) is a psychiatric condition affecting nearly a quarter of the United States war veterans who return from war zones. Treatment for PTSD typically consists of a combination of in-session therapy and medication. However; patients often experience their most severe PTSD symptoms outside of therapy sessions. Mobile health applications may address this gap, but their effectiveness is limited by the current gap in continuous monitoring and detection capabilities enabling timely intervention. The goal of this article is to develop a novel method to detect hyperarousal events using physiological and activity-based machine learning algorithms. Physiological data including heart rate and body acceleration as well as self-reported hyperarousal events were collected using a tool developed for commercial off-the-shelf wearable devices from 99 United States veterans diagnosed with PTSD over several days. The data were used to develop four machine learning algorithms: Random Forest, Support Vector Machine, Logistic Regression and XGBoost. The XGBoost model had the best performance in detecting onset of PTSD symptoms with over 83% accuracy and an AUC of 0.70. Post-hoc SHapley Additive exPlanations (SHAP) additive explanation analysis showed that algorithm predictions were correlated with average heart rate, minimum heart rate and average body acceleration. Findings show promise in detecting onset of PTSD symptoms which could be the basis for developing remote and continuous monitoring systems for PTSD. Such systems may address a vital gap in just-in-time interventions for PTSD self-management outside of scheduled clinical appointments.