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Browsing by Author "Belsare, Prajakta"

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    Cigarette Smoking Detection with an Inertial Sensor and a Smart Lighter
    (MDPI, 2019) Senyurek, Volkan; Imtiaz, Masudul; Belsare, Prajakta; Tiffany, Stephen; Sazonov, Edward; University of Alabama Tuscaloosa; State University of New York (SUNY) Buffalo
    In recent years, a number of wearable approaches have been introduced for objective monitoring of cigarette smoking based on monitoring of hand gestures, breathing or cigarette lighting events. However, non-reactive, objective and accurate measurement of everyday cigarette consumption in the wild remains a challenge. This study utilizes a wearable sensor system (Personal Automatic Cigarette Tracker 2.0, PACT2.0) and proposes a method that integrates information from an instrumented lighter and a 6-axis Inertial Measurement Unit (IMU) on the wrist for accurate detection of smoking events. The PACT2.0 was utilized in a study of 35 moderate to heavy smokers in both controlled (1.5-2 h) and unconstrained free-living conditions (similar to 24 h). The collected dataset contained approximately 871 h of IMU data, 463 lighting events, and 443 cigarettes. The proposed method identified smoking events from the cigarette lighter data and estimated puff counts by detecting hand-to-mouth gestures (HMG) in the IMU data by a Support Vector Machine (SVM) classifier. The leave-one-subject-out (LOSO) cross-validation on the data from the controlled portion of the study achieved high accuracy and F1-score of smoking event detection and estimation of puff counts (97%/98% and 93%/86%, respectively). The results of validation in free-living demonstrate 84.9% agreement with self-reported cigarettes. These results suggest that an IMU and instrumented lighter may potentially be used in studies of smoking behavior under natural conditions.
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    A CNN-LSTM neural network for recognition of puffing in smoking episodes using wearable sensors
    (Springer Nature, 2020) Senyurek, Volkan Y.; Imtiaz, Masudul H.; Belsare, Prajakta; Tiffany, Stephen; Sazonov, Edward; University of Alabama Tuscaloosa; State University of New York (SUNY) Buffalo
    A detailed assessment of smoking behavior under free-living conditions is a key challenge for health behavior research. A number of methods using wearable sensors and puff topography devices have been developed for smoking and individual puff detection. In this paper, we propose a novel algorithm for automatic detection of puffs in smoking episodes by using a combination of Respiratory Inductance Plethysmography and Inertial Measurement Unit sensors. The detection of puffs was performed by using a deep network containing convolutional and recurrent neural networks. Convolutional neural networks (CNN) were utilized to automate feature learning from raw sensor streams. Long Short Term Memory (LSTM) network layers were utilized to obtain the temporal dynamics of sensor signals and classify sequence of time segmented sensor streams. An evaluation was performed by using a large, challenging dataset containing 467 smoking events from 40 participants under free-living conditions. The proposed approach achieved an F1-score of 78% in leave-one-subject-out cross-validation. The results suggest that CNN-LSTM based neural network architecture sufficiently detect puffing episodes in free-living condition. The proposed model be used as a detection tool for smoking cessation programs and scientific research.
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    Computation of Cigarette Smoke Exposure Metrics From Breathing
    (IEEE, 2020) Belsare, Prajakta; Senyurek, Volkan Yusuf; Imtiaz, Masudul H.; Tiffany, Stephen; Sazonov, Edward; University of Alabama Tuscaloosa; State University of New York (SUNY) Buffalo
    Traditional metrics of smoke exposure in cigarette smokers are derived either from self-report, biomarkers, or puff topography. Methods involving biomarkers measure concentrations of nicotine, nicotine metabolites, or carbon monoxide. Puff-topography methods employ portable instruments to measure puff count, puff volume, puff duration, and inter-puff interval. In this article, we propose smoke exposure metrics calculated from the breathing signal and describe a novel algorithm for the computation of these metrics. The Personal Automatic Cigarette Tracker v2 (PACT-2) sensors, puff topography devices (CReSS), and video observation were used in a study of 38 moderate to heavy smokers in a controlled environment. Parameters of smoke inhalation including the start and end of each puff, inhale and exhale cycle, and smoke holding were computed from the breathing signal. From these, the traditional metrics of puff duration, inhale-exhale cycle duration, smoke holding duration, inter-puff interval, and novel Respiratory Smoke Exposure Metrics (RSEMs) such as inhale-exhale cycle volume, and inhale-exhale volume over time were calculated. The proposed RSEM algorithm to extract smoke exposure metrics named generated interclass correlations (ICCs) of 0.85 and 0.87 and Pearson's correlations of 0.97 and 0.77 with video observation and CReSS, respectively, for puff duration. Similarly, for the inhale-exhale duration, an ICC of 0.84 and Pearson's correlation of 0.81 was obtained with video observation. The RSEMs provided measures previously unavailable in research that are proportional to the depth and duration of smoke inhalation. The results suggest that the breathing signal may be used to compute smoke exposure metrics.
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    Measurement and Analysis of Cigarette Smoke Exposure and Smoking Behavior Using Wearable Sensors
    (University of Alabama Libraries, 2021) Belsare, Prajakta; Sazonov, Edward; University of Alabama Tuscaloosa
    Cigarette smoking is the most prevalent cause of preventable deaths in the whole world. There are hundreds of toxins in a single cigarette that can have harmful effects on both active and passive smokers. Researchers think it is important to understand and provide accurate information regarding daily cigarette smoking and smoke exposure to understand the health impacts of cigarette consumption. There are many tools available for estimation of daily cigarette consumption such as self-report, biomarkers of cigarette smoke exposure, puff topography devices, and, recently, wearable sensors. However, these methods have few limitations, such as recall biases or digit preference in self-reporting. Biomarkers are objective and accurate, but they are expensive and not feasible for monitoring daily consumption. Puff-topography devices can provide puffing and cigarette consumption information but fail to report the post-puff information, such as duration of cigarette smoke holding in the lungs. Research shows wearable sensors can objectively and automatically detect cigarette smoking in the free-living environment. However, they are limited to detecting the number of cigarettes, the number of puffs, duration of puff, or duration of cigarette smoking. None of the methods available to date can identify the post-puff information such as depth of inhalation, smoke holding duration, etc. This information is vital in understanding the detailed smoking behavior of an individual smoker. Thus, there was a need for the development of a reliable method for extracting the puffing and post-puffing information of daily cigarette consumption of individual smoker. This dissertation proposes the use of breathing signal for extracting smoke exposure metrics. This dissertation also proposes the development of deep learning architecture for monitoring cigarette smoking in free-living; and signal processing/pattern recognition methods for extracting post-puff information. The main accomplishments of this dissertation are (a) review of existing methods for monitoring cigarette smoking and measurement of cigarette smoke exposure; (b) development of a novel algorithm named RSEM (Respiratory Smoke Exposure Metrics) for extracting puffing and post-puffing information ( such as puff duration, inhale-exhale duration and volume, volume over time, smoke hold duration, inter-puff interval) from breathing signal. The proposed algorithm provided measures previously unavailable in research; (c) establishing a relationship between smoke exposure metrics computed using RSEM algorithm and the biomarkers of smoke exposure expire CO and Cotinine level; (d) development of DeepPuff algorithm for automatic identification of smoking inhalation in the free-living environment; (e) analysis of cigarette smoking in the free-living environment and the effects of using puff topography devices on the number of puffs, smoking duration, puff duration, inhale-exhale duration, inhale-exhale volume, smoke hold duration, and inter-puff interval.
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    Smoking detection based on regularity analysis of hand to mouth gestures
    (Elsevier, 2019) Senyurek, Volkan Y.; Imtiaz, Masudul H.; Belsare, Prajakta; Tiffany, Stephen; Sazonov, Edward; University of Alabama Tuscaloosa; State University of New York (SUNY) Buffalo
    A number of studies have been introduced for the detection of smoking via a variety of features extracted from the wrist IMU data. However, none of the previous studies investigated gesture regularity as a way to detect smoking events. This study describes a novel method to detect smoking events by monitoring the regularity of hand gestures. Here, the regularity of hand gestures was estimated from a one axis accelerometer worn on the wrist of the dominant hand. To quantify the regularity score, this paper applied a novel approach of unbiased autocorrelation to process the temporal sequence of hand gestures. The comparison of regularity score of smoking events with other activities substantiated that hand-to-mouth gestures are highly regular during smoking events and have the potential to detect smoking from among a plethora of daily activities. This hypothesis was validated on a dataset of 140 cigarette smoking events generated by 35 regular smokers in a controlled setting. The regularity of gestures detected smoking events with an F1-score of 0.81. However, the accuracy dropped to 0.49 in the free-living study of same 35 smokers smoking 295 cigarettes. Nevertheless, regularity of gestures may be useful as a supportive tool for other detection methods. To validate that proposition, this paper further incorporated the regularity of gestures in an instrumented lighter based smoking detection algorithm and achieved an improvement in F1-score from 0.89 (lighter only) to 0.91 (lighter and regularity of hand gestures). (C) 2019 Elsevier Ltd. All rights reserved.
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    Wearable Egocentric Camera as a Monitoring Tool of Free-Living Cigarette Smoking: A Feasibility Study
    (Oxford University Press, 2020) Imtiaz, Masudul H.; Hossain, Delwar; Senyurek, Volkan Y.; Belsare, Prajakta; Tiffany, Stephen; Sazonov, Edward; University of Alabama Tuscaloosa; State University of New York (SUNY) Buffalo
    Introduction: Wearable sensors may be used for the assessment of behavioral manifestations of cigarette smoking under natural conditions. This paper introduces a new camera-based sensor system to monitor smoking behavior.The goals of this study were (1) identification of the best position of sensor placement on the body and (2) feasibility evaluation of the sensor as a free-living smoking-monitoring tool. Methods: A sensor system was developed with a 5MP camera that captured images every second for continuously up to 26 hours. Five on-body locations were tested for the selection of sensor placement. A feasibility study was then performed on 10 smokers to monitor full-day smoking under free-living conditions. Captured images were manually annotated to obtain behavioral metrics of smoking including smoking frequency, smoking environment, and puffs per cigarette. The smoking environment and puff counts captured by the camera were compared with self-reported smoking. Results: A camera located on the eyeglass temple produced the maximum number of images of smoking and the minimal number of blurry or overexposed images (53.9%, 4.19%, and 0.93% of total captured, respectively). During free-living conditions, 286,245 images were captured with a mean (+/- standard deviation) duration of sensor wear of 647(+/- 74) minutes/participant. Image annotation identified consumption of 5(+/- 2.3) cigarettes/participant, 3.1(+/- 1.1) cigarettes/participant indoors, 1.9(+/- 0.9) cigarettes/participant outdoors, and 9.02(+/- 2.5) puffs/cigarette. Statistical tests found significant differences between manual annotations and self-reported smoking environment or puff counts. Conclusions: A wearable camera-based sensor may facilitate objective monitoring of cigarette smoking, categorization of smoking environments, and identification of behavioral metrics of smoking in free-living conditions.