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arXiv · 1607.03730

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

Hamid Dadkhahi·Nazir Saleheen·Santosh Kumar·Benjamin Marlin
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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.

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Hamid Dadkhahi, Nazir Saleheen, Santosh Kumar, Benjamin Marlin. 2016-07-13. Learning Shallow Detection Cascades for Wearable Sensor-Based Mobile Health Applications. https://arxiv.org/abs/1607.03730

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