Using sensor patterns to predict a depression or addiction relapse - Jan Peter Larsen

727 views
649 views

Published on

Published in: Health & Medicine, Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
727
On SlideShare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
0
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Using sensor patterns to predict a depression or addiction relapse - Jan Peter Larsen

  1. 1. Towards an application for depression and addiction relapse prevention
  2. 2. Complexity Sociological Psychological Biological
  3. 3. Question from doctors• Collect objective data on all three levels (biological, psychological, sociological)• Collect data continuously
  4. 4. CommonSense- Storage - Interpret- Visualisation - Predict- Sharing - Triggers- Combine - Learning
  5. 5. Growing functionality• Measuring data• Pattern analysis – Converting sensor data into behaviour data – Higher level patterns for correlation and forecasting• Interventions• Aggregation of data for research
  6. 6. Lessons learned• Sensordata from smartphones is very promising as useful data for monitoring people with depression and addiction• Dynamics of patterns is more in days and weeks, than in minutes / hours• All steps already have value as application• Next steps: – Testing with 60 patients for 4-6 weeks – Extending pattern recognition and intervention capabilities

×