Using Convolutional Variational Autoencoders to Predict Post-Trauma Health Outcomes from Actigraphy Data
This work addresses mental health monitoring for trauma survivors using non-invasive wearable data, but it is incremental as it applies an existing VAE method to a new dataset with modest performance gains.
The study tackled predicting post-trauma mental health outcomes like depression and PTSD using actigraphy data from 1113 individuals, achieving an AUC of 0.64 with a logistic regression classifier based on VAE-extracted features and pre-trauma health status.
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.