Learning Shallow Detection Cascades for Wearable Sensor-Based Mobile Health Applications

Citation:
Dadkhahi, H., N. Saleheen, S. Kumar, and B. Marlin, "Learning Shallow Detection Cascades for Wearable Sensor-Based Mobile Health Applications", ICML On Device Intelligence Workshop, 2016.

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.

Notes:

n/a