@article{15094,
  author = {Enrique Garcia-Ceja and Michael Riegler and Tine Nordgreen and Petter Jakobsen and Ketil Oedegaard and Jim T{\o}rresen},
  title = {Mental Health Monitoring with Multimodal Sensing and Machine Learning: A Survey},
  abstract = {Personal and ubiquitous sensing technologies such as smartphones have allowed the continuous collection of data in an unobtrusive manner. Machine learning methods have been applied to continuous sensor data to predict user contextual information such as location, mood, physical activity, etc. Recently, there has been a growing interest in leveraging ubiquitous sensing technologies for mental health care applications, thus, allowing the automatic continuous monitoring of different mental conditions such as depression, anxiety, stress, and so on. This paper surveys recent research works in mental health monitoring systems (MHMS) using sensor data and machine learning. We focused on research works about mental disorders/conditions such as: depression, anxiety, bipolar disorder, stress, etc. We propose a classification taxonomy to guide the review of related works and present the overall phases of MHMS. Moreover, research challenges in the field and future opportunities are also discussed.},
  year = {2018},
  journal = {Pervasive and Mobile Computing},
  volume = {51},
  pages = {1-26},
  month = {12/2018},
  publisher = {Elsevier},
  doi = {https://doi.org/10.1016/j.pmcj.2018.09.003},
}