UA cloudflare authentication

Browsing by Author "Tiffany, Stephen"

Now showing 1 - 9 of 9
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    Development of a Multisensory Wearable System for Monitoring Cigarette Smoking Behavior in Free-Living Conditions
    (2017-11-28) Imtiaz, Masudul Haider; Ramos-Garcia, Raul I.; Senyurek, Volkan Yusuf; Tiffany, Stephen; Sazonov, Edward; University of Alabama Tuscaloosa
    This paper presents the development and validation of a novel multi-sensory wearable system (Personal Automatic Cigarette Tracker v2 or PACT2.0) for monitoring of cigarette smoking in free-living conditions. The contributions of the PACT2.0 system are: (1) the implementation of a complete sensor suite for monitoring of all major behavioral manifestations of cigarette smoking (lighting events, hand-to-mouth gestures, and smoke inhalations); (2) a miniaturization of the sensor hardware to enable its applicability in naturalistic settings; and (3) an introduction of new sensor modalities that may provide additional insight into smoking behavior e.g., Global Positioning System (GPS), pedometer and Electrocardiogram(ECG) or provide an easy-to-use alternative (e.g., bio-impedance respiration sensor) to traditional sensors. PACT2.0 consists of three custom-built devices: an instrumented lighter, a hand module, and a chest module. The instrumented lighter is capable of recording the time and duration of all lighting events. The hand module integrates Inertial Measurement Unit (IMU) and a Radio Frequency (RF) transmitter to track the hand-to-mouth gestures. The module also operates as a pedometer. The chest module monitors the breathing (smoke inhalation) patterns (inductive and bio-impedance respiratory sensors), cardiac activity (ECG sensor), chest movement (three-axis accelerometer), hand-to-mouth proximity (RF receiver), and captures the geo-position of the subject (GPS receiver). The accuracy of PACT2.0 sensors was evaluated in bench tests and laboratory experiments. Use of PACT2.0 for data collection in the community was validated in a 24 h study on 40 smokers. Of 943 h of recorded data, 98.6% of the data was found usable for computer analysis. The recorded information included 549 lighting events, 522/504 consumed cigarettes (from lighter data/self-registered data, respectively), 20,158/22,207 hand-to-mouth gestures (from hand IMU/proximity sensor, respectively) and 114,217/112,175 breaths (from the respiratory inductive plethysmograph (RIP)/bio-impedance sensor, respectively). The proposed system scored 8.3 ± 0.31 out of 10 on a post-study acceptability survey. The results suggest that PACT2.0 presents a reliable platform for studying of smoking behavior at the community level.
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    A Wearable Sensor System for Monitoring Cigarette Smoking
    (Rutgers Center of Alcohol & Substance Use Studies, 2013) Sazonov, Edward; Lopez-Meyer, Paulo; Tiffany, Stephen; University of Alabama Tuscaloosa; State University of New York (SUNY) Buffalo
    Objective: Available methods of smoking assessment (e.g., self-report, portable puff-topography instruments) do not permit the collection of accurate measures of smoking behavior while minimizing reactivity to the assessment procedure. This article suggests a. new method for monitoring cigarette smoking based on a wearable sensor system (Personal Automatic Cigarette Tracker [PACT]) that is completely transparent to the end user and does not require any conscious effort to achieve reliable monitoring of smoking in free-living individuals. Method: The proposed sensor system consists of a respiratory inductance plethysmograph for monitoring of breathing and a hand gesture sensor for detecting a cigarette at the mouth. The wearable sensor system was tested in a laboratory study of 20 individuals who performed 12 different activities including cigarette smoking. Signal processing was applied to evaluate the uniqueness of breathing patterns and their correlation with hand gestures. Results: The results indicate that smoking manifests unique breathing patterns that are highly correlated with hand-to-mouth cigarette gestures and suggest that these signals can potentially be used to identify and characterize individual smoke inhalations. Conclusions: With the future development of signal processing and pattern-recognition methods, PACT can be used to automatically assess the frequency of smoking and inhalation patterns (such as depth of inhalation and smoke holding) throughout the day and provide an objective method of assessing the effectiveness of behavioral and pharmacological smoking interventions.
  • Thumbnail Image
    Item
    Wearable sensor systems to study the physiological and behavioral manifestation of cigarette smoking in free-living
    (University of Alabama Libraries, 2019) Imtiaz, Masudul; Sazonov, Edward; University of Alabama Tuscaloosa
    Worldwide, cigarette smoking is one of the major causes of preventable death. A single cigarette contains more than a hundred toxins that have detrimental effects on the smoker himself and the people in his or her surroundings. Despite knowledge of these harms, smokers often struggle to quit. Accurate information on daily smoking might help for evaluating the smoking behavior of an individual and the effectiveness of related intervention process. Self-reporting, puff topography meters, and biomarkers are the primary tools available for the estimation of daily cigarette consumption. However, these methods have been proved to be either biased, inaccurate, obtrusive, or not suitable for all smokers. Thus, there was a need for the development of solutions for objective, accurate and automatic detection of cigarette smoking, especially under free-living conditions. This dissertation proposes new wearable sensor systems and related signal/image processing and pattern recognition methods for the objective, accurate and automatic detection of cigarette smoking with minimal effort from the person being observed. Main accomplishments of this dissertation are a) development and validation of a novel multi-sensory wearable system (Personal Automatic Cigarette Tracker v2 aka PACT 2.0) to facilitate studying the behavioral and physiological manifestations of cigarette smoking. The validation study involving forty participants suggests that this wearable system presents a reliable platform for collecting objective information on smoking behavior in the free-living; b) development and validation of a method to identify smoking events from the associated changes in heart rate parameters of the wearer. The proposed method also accounts for the breathing rate and body motion of the smoker to better distinguish these changes from intense physical activities. The validation study involving twenty participants suggests that these physiological parameters could be a useful indicator of cigarette smoking even in the free-living; c) validation of a wearable egocentric camera system to capture minute details of smoking events from the eye-level such as hand to mouth gestures during smoking puff, smoking environment, body posture or activities during smoking, etc. The human study involving ten participants demonstrates that this novel sensor system may facilitate the objective monitoring of cigarette smoking, categorizing smoking environment, and obtaining an overview of the smoking habit in free-living; d) development and validation of computer models to automatically extract behavioral metrics of cigarette smoking (such as smoking time of day, frequency, inter-cigarette interval, etc.) from images captured by the egocentric camera. The validation study performed on a large free-living image set shows the applicability of proposed models to extract an objective summary of daily smoking.
  • Thumbnail Image
    Item
    Wearable Sensors for Monitoring of Cigarette Smoking in Free-Living: A Systematic Review
    (MDPI, 2019) Imtiaz, Masudul H.; Ramos-Garcia, Raul I.; Wattal, Shashank; Tiffany, Stephen; Sazonov, Edward; University of Alabama Tuscaloosa; State University of New York (SUNY) Buffalo
    Globally, cigarette smoking is widespread among all ages, and smokers struggle to quit. The design of effective cessation interventions requires an accurate and objective assessment of smoking frequency and smoke exposure metrics. Recently, wearable devices have emerged as a means of assessing cigarette use. However, wearable technologies have inherent limitations, and their sensor responses are often influenced by wearers' behavior, motion and environmental factors. This paper presents a systematic review of current and forthcoming wearable technologies, with a focus on sensing elements, body placement, detection accuracy, underlying algorithms and applications. Full-texts of 86 scientific articles were reviewed in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines to address three research questions oriented to cigarette smoking, in order to: (1) Investigate the behavioral and physiological manifestations of cigarette smoking targeted by wearable sensors for smoking detection; (2) explore sensor modalities employed for detecting these manifestations; (3) evaluate underlying signal processing and pattern recognition methodologies and key performance metrics. The review identified five specific smoking manifestations targeted by sensors. The results suggested that no system reached 100% accuracy in the detection or evaluation of smoking-related features. Also, the testing of these sensors was mostly limited to laboratory settings. For a realistic evaluation of accuracy metrics, wearable devices require thorough testing under free-living conditions.