A context-aware experience sampling tool
CHI '03 Extended Abstracts on Human Factors in Computing Systems
Acquiring in situ training data for context-aware ubiquitous computing applications
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Recognizing Mimicked Autistic Self-Stimulatory Behaviors Using HMMs
ISWC '05 Proceedings of the Ninth IEEE International Symposium on Wearable Computers
MyExperience: a system for in situ tracing and capturing of user feedback on mobile phones
Proceedings of the 5th international conference on Mobile systems, applications and services
SAPhyRA: Stream Analysis for Physiological Risk Assessment
CBMS '07 Proceedings of the Twentieth IEEE International Symposium on Computer-Based Medical Systems
Century: Automated Aspects of Patient Care
RTCSA '07 Proceedings of the 13th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications
Remote health-care monitoring using personal care connect
IBM Systems Journal
A hybrid discriminative/generative approach for modeling human activities
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
A practical approach to recognizing physical activities
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
HealthAware: tackling obesity with health aware smart phone systems
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Security management in Wireless Sensor Networks for healthcare
International Journal of Mobile Communications
A survey on privacy in mobile participatory sensing applications
Journal of Systems and Software
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Remote patient monitoring generates much more data than healthcare professionals are able to manually interpret. Automated detection of events of interest is therefore critical so that these points in the data can be marked for later review. However, for some important chronic health conditions, such as pain and depression, automated detection is only partially achievable. To assist with this problem we developed HealthSense, a framework for real-time tagging of health-related sensor data. HealthSense transmits sensor data from the patient to a server for analysis via machine learning techniques. The system uses patient input to assist with classification of interesting events (e.g., pain or itching). Due to variations between patients, sensors, and condition types, we presume that our initial classification is imperfect and accommodate this by incorporating user feedback into the machine learning process. This is done by occasionally asking the patient whether they are experiencing the condition being monitored. Their response is used to confirm or reject the classification made by the server and continually improve the accuracy of the classifier's decisions on what data is of interest to the health-care provider.