Learning Shallow Detection Cascades for Wearable Sensor-Based Mobile Health Applications
This work addresses the need for real-time health monitoring in mobile health applications, though it appears incremental as it builds on existing detection methods.
The authors tackled the problem of real-time cigarette smoking detection from wearable sensor data by proposing a novel approach to learning shallow detection cascades, achieving detection in two and three stage cascades.
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.