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.
@article{arxiv.1607.03730,
title = {Learning Shallow Detection Cascades for Wearable Sensor-Based Mobile Health Applications},
author = {Hamid Dadkhahi and Nazir Saleheen and Santosh Kumar and Benjamin Marlin},
journal= {arXiv preprint arXiv:1607.03730},
year = {2016}
}