Mikelsons, G; Smith, M; Mehrotra, A; Musolesi, M; (2017) Towards Deep Learning Models for Psychological State Prediction using Smartphone Data: Challenges and Opportunities. In: Guyon, I and Luxburg, UV and Bengio, S and Wallach, H and Fergus, R and Vishwanathan, S and Garnett, R, (eds.) Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017). NIPS Proceedings: Long Beach, CA, USA.

Preview

Text 1711.06350v1.pdf - Accepted Version Download (243kB) | Preview

Abstract

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

Download activity - last month

Export as CSVJSONXML

Download activity - last 12 months

Export as JSONXMLCSV

Downloads by country - last 12 months

Export as CSVXMLJSON

Archive Staff Only

View Item