KTI MOSS - Mobile Sensing and Support for People with Depression
Type
applied research project
Start Date
April 1, 2014
End Date
October 31, 2015
Status
ongoing
Keywords
mobile Health
depression
mental health
sensing and support
machine learning
Description
Limited personnel resources and costs have a negative impact on the health supply in mental health therapy and intervention programs. People with mental disorders such as dementia, major depression, substance abuse or mental retardation experience serious health-related consequences. For example, depression alone accounts for 4.3% of the global burden of disease and is among the largest single cause of disability worldwide (WHO 2013). As an economic consequence, the increasing prevalence of non-communicable diseases (i.e. non-infectious and non-transmissible among people) to which mental disorders contribute a major extent, is expected to account for a loss of US$ 47 trillion in 2030, i.e. approximately 75% of the global gross domestic product in 2010. A range of web-based mental health interventions emerged over the last couple of years to tackle the problem of health supply shortage. Unfortunately to date, if no extra personal guidance is utilized, they fail to deliver tailored and context sensitive support and therefore have limited potential. With MOSS we try to fill this gap. Using off the shelve smartphone sensors and state of the art machine learning techniques we aim at inferring a users mental state in order to deliver best practices that maximize intervention success.
Leader contributor(s)
Member contributor(s)
Wahle, Fabian
Hanulova, Anna
Hörni, Anja
Schweinfurther, Jost
Partner(s)
Universitätsspital Zürich, ETH Zürich, makora AG
Funder
Topic(s)
mobile Health
depression
mental health
sensing and support
machine learning
Method(s)
machine learning
data mining
survey
Range
HSG Internal
Range (De)
HSG Intern
Eprints ID
240002