Pervasive Sensing and Machine Learning for Mental Health

Recent advances in imaging and sensing have facilitated the acquisition of detailed neurological signals and imaging techniques for better understanding of the disorder. In addition, new wearable technologies have enabled continuous sensing of neurological, physiological, and behavioral information of the users. These technologies have led to new insights into mental illnesses providing the needed data to improve the diagnosis, identify triggers of episodes, and enable preventative interventions with diverse machine learning approaches.

This special issue is in cooperation with the 2018 Researcher Links workshop on Mental Health Technologies ( and also beyond it. It is dedicated to cover the related topics on technological advancements for mental health care and diagnosis with focus on pervasive sensing and machine learning. Only original research contributions will be considered.

Topics of interest include, but are not limited to, the following:

  • Clinical challenges in mental health
  • Pervasive sensing for mental health
  • Computational methods for assessing cognitive impairment
  • Machine learning for episode detection and early intervention
  • Healthcare information system for managing mental health
  • Wearable technologies for real-time and long-term monitoring of mental conditions
  • Assistive technologies for self-management of mental diseases
  • Data security and privacy for care support systems
  • Decentralisation of mental health care systems using Blockchain and DLTs for mental health
  • Innovative technologies for non-pharmaceutical mental health treatment

Guest Editors

Benny Lo – Imperial College London, UK

Yuan Zhang – University of Jinan, China

Omer T. Inan – Georgia Tech, USA

Joshua Ellul – University of Malta, Malta

Key Dates

Deadline for Submission: 31 Dec, 2018
First Reviews Due: 30 Mar, 2019
Revised Manuscript Due: 30 May, 2019
Final Decision: 30 June, 2019

Download Call for Papers (PDF)