# Using Convolutional Variational Autoencoders to Predict Post-Trauma Health Outcomes from Actigraphy Data

arXiv: https://arxiv.org/abs/2011.07406 · HTML: https://mlsanity.com/paper/2011.07406/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2020.

## Built on by (most cited first, 2 of 2)

- [Lifelong Adaptive Machine Learning for Sensor-based Human Activity Recognition Using Prototypical Networks](https://mlsanity.com/paper/2203.05692/) (2022)
- [Assessing Distribution Shift in Human Activity Recognition for Domain Generalization](https://mlsanity.com/paper/2606.24781/) (2026)

## Abstract (arXiv)

Depression and post-traumatic stress disorder (PTSD) are psychiatric conditions commonly associated with experiencing a traumatic event. Estimating mental health status through non-invasive techniques such as activity-based algorithms can help to identify successful early interventions. In this work, we used locomotor activity captured from 1113 individuals who wore a research grade smartwatch post-trauma. A convolutional variational autoencoder (VAE) architecture was used for unsupervised feature extraction from four weeks of actigraphy data. By using VAE latent variables and the participant's pre-trauma physical health status as features, a logistic regression classifier achieved an area under the receiver operating characteristic curve (AUC) of 0.64 to estimate mental health outcomes. The results indicate that the VAE model is a promising approach for actigraphy data analysis for mental health outcomes in long-term studies.