Passive capture and ensuing issues for a personal lifetime store
Proceedings of the the 1st ACM workshop on Continuous archival and retrieval of personal experiences
Gait phase effects in mobile interaction
CHI '05 Extended Abstracts on Human Factors in Computing Systems
Computer Animation and Virtual Worlds - Special Issue: The Very Best Papers from CASA 2004
Cross-cultural differences in recognizing affect from body posture
Interacting with Computers
Head tilting for interaction in mobile contexts
Proceedings of the 11th International Conference on Human-Computer Interaction with Mobile Devices and Services
Multimodal mobile interactions: usability studies in real world settings
ICMI '11 Proceedings of the 13th international conference on multimodal interfaces
| Hi-index | 0.00 |
The need to monitor patients after they leave the hospital or clinics is of growing concern and doctors may need the facility to monitor certain patients more than others. For example patients with high blood pressure are sometimes fitted with a mobile monitor which can be used to track the patients blood pressure over time. Patients suffering from depression, however, may also need to be monitored to ensure that they are in a happy emotional state. In this paper we introduce an alternative approach to mood detection and tracking based on built-in accelerometer sensors found in common mobile phones. Our method can be seen to compliment the need to monitor such patients allowing for doctors to get in touch with them when their mood has altered. We build a system based on neural networks which takes the gait information and learns the associated mood of the user. This trained model is then used to detect the mood of the individuals. We demonstrate preliminary results on mood detection using a mobile prototype system.