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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Identification of cigarette smoke inhalations from wearable sensor data using a Support Vector Machine classifier.

Paulo Lopez-Meyer1, Stephen Tiffany, Edward Sazonov

  • 1Department of Electrical and computer Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA. plopezmeyer@ua.edu

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|February 1, 2013
PubMed
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This study developed a wearable sensor system to detect smoking behavior by analyzing hand-to-mouth movements and breathing changes. The model accurately identifies smoke inhalations, offering a non-invasive way to monitor smoking patterns.

Area of Science:

  • Biomedical Engineering
  • Wearable Technology
  • Behavioral Science

Background:

  • Smoking cessation and behavior analysis are critical public health concerns.
  • Objective monitoring of smoking behavior is challenging with current methods.
  • Wearable sensors offer a promising avenue for unobtrusive physiological data collection.

Purpose of the Study:

  • To develop a subject-independent model for detecting smoke inhalations using wearable sensors.
  • To analyze characteristic hand-to-mouth gestures and breathing pattern changes during smoking.
  • To evaluate the performance of a Support Vector Machine (SVM) classification model for smoking behavior analysis.

Main Methods:

  • Utilized wearable sensors to capture hand-to-mouth proximity and respiratory patterns.

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  • Extracted signal waveforms from sensor data as features.
  • Employed a Support Vector Machine (SVM) classification model for smoke inhalation detection.
  • Validated the model on a dataset of 20 participants.

    • Main Results:

    • Achieved a precision of over 87% for correct identification of smoke inhalations.
    • Obtained a recall of over 80% in detecting smoke inhalations.
    • Demonstrated subject-independent performance of the developed model.

    Conclusions:

    • A wearable, non-invasive sensor system can effectively analyze smoking behavior.
    • The developed model shows high accuracy in detecting smoke inhalations.
    • This technology has potential applications in smoking cessation programs and research.