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Related Experiment Video

Updated: Jul 8, 2025

Creating Dynamic Images of Short-lived Dopamine Fluctuations with lp-ntPET: Dopamine Movies of Cigarette Smoking

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DeepPuff: Utilizing Deep Learning for Smoking Behavior Identification in Free-living Environment.

Prajakta Belsare, Volkan Y Senyurek, Masudul H Imtiaz

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 12, 2023

Summary

DeepPuff, a novel deep learning model, accurately quantifies respiratory smoke exposure metrics (RSEM) using breathing and gesture data. This technology offers a reliable method for assessing smoke exposure in real-world conditions.

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Area of Science:

Biomedical Engineering
Public Health
Machine Learning

Background:

Assessing cigarette smoking behavior and health effects necessitates accurate measurement of smoke exposure.
Existing methods for quantifying smoke exposure can be limited, especially in real-world settings.

Purpose of the Study:

To develop and validate a deep learning model, DeepPuff, for quantifying Respiratory Smoke Exposure Metrics (RSEM).
To enable precise measurement of smoke inhalation events and associated respiratory metrics.

Main Methods:

A CNN-LSTM deep learning architecture (DeepPuff) was developed using data from breathing and hand gesture sensors (PACT 2.0).
The model was trained on 190 smoking events and validated on an independent dataset of 459 events (lab and free-living).
Respiratory Smoke Exposure Metrics were computed and compared against video-annotated ground truth.

Main Results:

DeepPuff achieved high precision in detecting smoke inhalations: 82.39% (training) and 93.80% (testing).
Testing precision was 95.88% in lab conditions and 93.78% in free-living conditions.
RSEM metrics (puff duration, inhale-exhale duration, inhalation duration) showed no statistical difference compared to video annotation.

Conclusions:

DeepPuff demonstrates high accuracy and reliability in quantifying respiratory smoke exposure metrics.
The model is suitable for measuring smoke exposure, even under free-living conditions.
This technology can advance the comprehensive assessment of smoking behavior and its health implications.