# Towards Deep Learning Models for Psychological State Prediction using Smartphone Data: Challenges and Opportunities

arXiv: https://arxiv.org/abs/1711.06350 · HTML: https://mlsanity.com/paper/1711.06350/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2017.

## Built on by (most cited first, 3 of 3)

- [Toward Foundation Model for Multivariate Wearable Sensing of Physiological Signals](https://mlsanity.com/paper/2412.09758/) (2024)
- [Analyzing the contribution of different passively collected data to predict Stress and Depression](https://mlsanity.com/paper/2310.13607/) (2023)
- [Learning Longitudinal Stress Dynamics from Irregular Self-Reports via Time Embeddings](https://mlsanity.com/paper/2509.01569/) (2025)

## Abstract (arXiv)

There is an increasing interest in exploiting mobile sensing technologies and machine learning techniques for mental health monitoring and intervention. Researchers have effectively used contextual information, such as mobility, communication and mobile phone usage patterns for quantifying individuals' mood and wellbeing. In this paper, we investigate the effectiveness of neural network models for predicting users' level of stress by using the location information collected by smartphones. We characterize the mobility patterns of individuals using the GPS metrics presented in the literature and employ these metrics as input to the network. We evaluate our approach on the open-source StudentLife dataset. Moreover, we discuss the challenges and trade-offs involved in building machine learning models for digital mental health and highlight potential future work in this direction.