2016 IEEE International Conference on Healthcare Informatics (ICHI) (2016)
Chicago, Illinois, United States
Oct. 4, 2016 to Oct. 7, 2016
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICHI.2016.89
Wearable health-monitoring systems must achieve a balance between the often opposing goals of hardware overhead and classification accuracy. Prior works have presented various approaches to dynamically scale the accuracy of these systems as a function of available resources. In this paper, we present a framework which retroactively improves the accuracy of prior estimates when resources become available, using a novel global cost minimization function. We benchmark our algorithm on an audio-based nutrition monitoring dataset. Results confirm the efficacy of our technique.
Feature extraction, Sensors, Hardware, Biomedical monitoring, Monitoring, Computational modeling, Mathematical model
H. Kalantarian, M. Sarrafzadeh, S. Zhang and N. Alshurafa, "An Iterative Dimensionality-Scaling System for Real-Time Health Monitoring Applications," 2016 IEEE International Conference on Healthcare Informatics (ICHI), Chicago, Illinois, United States, 2016, pp. 488-494.