%L discovery10062940
%I NIPS Proceedings
%C Long Beach, CA, USA
%E I Guyon
%E UV Luxburg
%E S Bengio
%E H Wallach
%E R Fergus
%E S Vishwanathan
%E R Garnett
%A G Mikelsons
%A M Smith
%A A Mehrotra
%A M Musolesi
%V 2017
%D 2017
%T Towards Deep Learning Models for Psychological State Prediction using Smartphone Data: Challenges and Opportunities
%O This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
%X 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
%S Conference on Neural Information Processing Systems (NIPS)
%J CoRR
%B Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017)