Year: 2016
Type: preprint
Abstract: The field of mobile health aims to leverage recent advances in wearable on-body sensing technology and smart phone computing capabilities to develop systems that can monitor health states and deliver just-in-time adaptive interventions. However, existing work has largely focused on analyzing collected data in the off-line setting. In this paper, we propose a novel approach to learning shallow detection cascades developed explicitly for use in a real-time wearable-phone or wearable-phone-cloud systems. We apply our approach to the problem of cigarette smoking detection from a combination of wrist-worn actigraphy data and respiration chest band data using two and three stage cascades.
Source: arXiv (Cornell University)
Institutions: University of Massachusetts Amherst, University of Memphis
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: Center of Excellence for Mobile Sensor Data-to-Knowledge (MD2K) - OVERALL, CAREER: Machine Learning for Complex Health Data Analytics, G3229617247, Eliminating Tobacco-Related Disparities amount African American Smokers, G3632283481, Predicting Smoking Abstinence via Mobile Monitoring of Stress and Social Context, SHB: Type I (EXP): Collaborative Research: EasySense: Contact-less Physiological Sensing in the Mobile Environment Using Compressive Radio Frequency Probes
OpenAlex ID: W2462310032