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The field of mobile health aims to leverage recent advances in wearableon-body sensing technology and smart phone computing capabilities to developsystems that can monitor health states and deliver just-in-time adaptiveinterventions. However, existing work has largely focused on analyzingcollected data in the off-line setting. In this paper, we propose a novelapproach to learning shallow detection cascades developed explicitly for use ina real-time wearable-phone or wearable-phone-cloud systems. We apply ourapproach to the problem of cigarette smoking detection from a combination ofwrist-worn actigraphy data and respiration chest band data using two and threestage cascades.

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