Classification of heart sounds using an artificial neural network
Pattern Recognition Letters
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
NETRA: interactive display for estimating refractive errors and focal range
ACM SIGGRAPH 2010 papers
Motion-tolerant magnetic earring sensor and wireless earpiece for wearable photoplethysmography
IEEE Transactions on Information Technology in Biomedicine - Special section on new and emerging technologies in bioinformatics and bioengineering
Accurate and privacy preserving cough sensing using a low-cost microphone
Proceedings of the 13th international conference on Ubiquitous computing
mobileSpiro: accurate mobile spirometry for self-management of asthma
Proceedings of the First ACM Workshop on Mobile Systems, Applications, and Services for Healthcare
Respiration monitoring during sleeping
Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
Improving the Compliance of Transplantation Medicine Patients with an Integrated Mobile System
HICSS '12 Proceedings of the 2012 45th Hawaii International Conference on System Sciences
Analysis of chewing sounds for dietary monitoring
UbiComp'05 Proceedings of the 7th international conference on Ubiquitous Computing
Tracking lung function on any phone
Proceedings of the 3rd ACM Symposium on Computing for Development
mCOPD: mobile phone based lung function diagnosis and exercise system for COPD
Proceedings of the 6th International Conference on PErvasive Technologies Related to Assistive Environments
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Home spirometry is gaining acceptance in the medical community because of its ability to detect pulmonary exacerbations and improve outcomes of chronic lung ailments. However, cost and usability are significant barriers to its widespread adoption. To this end, we present SpiroSmart, a low-cost mobile phone application that performs spirometry sensing using the built-in microphone. We evaluate SpiroSmart on 52 subjects, showing that the mean error when compared to a clinical spirometer is 5.1% for common measures of lung function. Finally, we show that pulmonologists can use SpiroSmart to diagnose varying degrees of obstructive lung ailments.