An Improved Data Representation for Smoking Detection with Wearable Respiration Sensors

Laura Hiatt1, Roy Adams2, Benjamin Marlin2

  • 1Davidson College
  • 2University of Massachusetts, Amherst
Also at:
11:45 - 12:15 | Thursday 27 October 2016 | Main Auditorium

Details

Category

Research Abstracts (Poster)

Sessions

08:30 - 19:30 | Wed 26 Oct | Auditorium Foyer | WePOS

Poster Session

11:45 - 12:15 | Thu 27 Oct | Main Auditorium | IS-2

Ignite Session 2

Abstract

Within the field of mobile health, cigarette smoking detection using wearable sensors is a key problem with the potential to improve health outcomes by enabling continuous monitoring as well as personalized, adaptive smoking cessation interventions. Prior work on cigarette smoking detection has demonstrated encouraging results using respiratory inductance plethysmography (RIP) sensors and features derived from inferred respiration cycles. In this work, we argue that the step of pre-segmenting respiration data into respiration cycles is prone to error due to noise in the RIP data. We propose an alternative framing of the problem based on peaks in the RIP data and show improvements in smoking detection accuracy.

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