Identifying Smoking from Smartphone Sensor Data and Multivariate Hidden Markov Models

Yang, Qin; Qian, Weicheng; Shojaati, Narjes; Osgood, Nathaniel D.

doi:10.1007/978-3-319-60240-0_27

Cited by 3 publications

(8 citation statements)

References 7 publications

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“…A total of 8 studies [ 68 , 80 - 85 , 92 ] described the measurement properties of approaches assessing tobacco use. The number of participants involved in these studies ranged from 3 to 146 (N=363).…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Measurement Properties of Smartphone Approaches to Assess Diet, Alcohol Use, and Tobacco Use: Systematic Review

Thornton¹,

Osman²,

Champion³

et al. 2022

JMIR Mhealth Uhealth

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Background Poor diet, alcohol use, and tobacco smoking have been identified as strong determinants of chronic diseases, such as cardiovascular disease, diabetes, and cancer. Smartphones have the potential to provide a real-time, pervasive, unobtrusive, and cost-effective way to measure these health behaviors and deliver instant feedback to users. Despite this, the validity of using smartphones to measure these behaviors is largely unknown. Objective The aim of our review is to identify existing smartphone-based approaches to measure these health behaviors and critically appraise the quality of their measurement properties. Methods We conducted a systematic search of the Ovid MEDLINE, Embase (Elsevier), Cochrane Library (Wiley), PsycINFO (EBSCOhost), CINAHL (EBSCOHost), Web of Science (Clarivate), SPORTDiscus (EBSCOhost), and IEEE Xplore Digital Library databases in March 2020. Articles that were written in English; reported measuring diet, alcohol use, or tobacco use via a smartphone; and reported on at least one measurement property (eg, validity, reliability, and responsiveness) were eligible. The methodological quality of the included studies was assessed using the Consensus-Based Standards for the Selection of Health Measurement Instruments Risk of Bias checklist. Outcomes were summarized in a narrative synthesis. This systematic review was registered with PROSPERO, identifier CRD42019122242. Results Of 12,261 records, 72 studies describing the measurement properties of smartphone-based approaches to measure diet (48/72, 67%), alcohol use (16/72, 22%), and tobacco use (8/72, 11%) were identified and included in this review. Across the health behaviors, 18 different measurement techniques were used in smartphones. The measurement properties most commonly examined were construct validity, measurement error, and criterion validity. The results varied by behavior and measurement approach, and the methodological quality of the studies varied widely. Most studies investigating the measurement of diet and alcohol received very good or adequate methodological quality ratings, that is, 73% (35/48) and 69% (11/16), respectively, whereas only 13% (1/8) investigating the measurement of tobacco use received a very good or adequate rating. Conclusions This review is the first to provide evidence regarding the different types of smartphone-based approaches currently used to measure key behavioral risk factors for chronic diseases (diet, alcohol use, and tobacco use) and the quality of their measurement properties. A total of 19 measurement techniques were identified, most of which assessed dietary behaviors (48/72, 67%). Some evidence exists to support the reliability and validity of using smartphones to assess these behaviors; however, the results varied by behavior and measurement approach. The methodological quality of the included studies also varied. Overall, more high-quality studies validating smartphone-based approaches against criterion measures are needed. Further research investigating the use of smartphones to assess alcohol and tobacco use and objective measurement approaches is also needed. International Registered Report Identifier (IRRID) RR2-https://doi.org/10.1186/s13643-020-01375-w

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Section: Resultsmentioning

confidence: 99%

“…A total of 3 studies [ 84 , 85 , 92 ] investigated passive objective approaches to measure tobacco use via smartphones. Overall, 67% (2/3) of these studies [ 84 , 85 ] used wrist-worn sensor devices (eg, smartwatches) in conjunction with smartphone apps to detect episodes of smoking.…”

Section: Resultsmentioning

confidence: 99%

Measurement Properties of Smartphone Approaches to Assess Diet, Alcohol Use, and Tobacco Use: Systematic Review

Thornton¹,

Osman²,

Champion³

et al. 2022

JMIR Mhealth Uhealth

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“…The study reported by Skinner et al [70] provided an approach to eliminate the necessity of smartphones and integrated everything into a single node (a wristwatch). McClernon and Choudhury [114] and Qin et al [115] proposed methods to use only smartphone sensors (Wi-Fi, GPS, and Accelerometer) data to detect smoking events. These above-mentioned approaches may be capable of recruiting social support groups to inhibit smoking behavior.…”

Section: Discussionmentioning

confidence: 99%

Wearable Sensors for Monitoring of Cigarette Smoking in Free-Living: A Systematic Review

Imtiaz

Ramos-Garcia

Wattal

et al. 2019

Sensors

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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.

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“…The above two smoking event detection systems both run on lowcost smartwatches, which necessitates higher smartwatch computing capacity requirements. With the increased use of smartphones, McClernon et al [10] and Qin et al [11] proposed methods to use only smartphone sensor (Wi-Fi, GPS, or accelerometer) data to detect smoking events. More commonly, the inertial sensing module of a smartwatch is paired with a smartphone and used to detect smoking events using the developed mobile application.…”

Section: Related Workmentioning

confidence: 99%

“…Due to the convenience of capturing hand gestures using a smartwatch, some research uses motion sensor data from a smartwatch to detect smoking-related hand gestures and recognize high-level activities on its Android-based system [7,8]. Other works employ motion sensors embedded in a smartwatch to detect smoking events by pairing the smartwatch with a smartphone and running the detection algorithms on the phone [1,9], and several other studies use only smartphone sensor data to detect smoking events [10,11]. In these related works, the motion context information from a smartwatch and smartphone is mainly used in isolation, and the efficiency of online smoking prediction on the mobile device is rarely evaluated.…”

Section: Introductionmentioning

confidence: 99%

A New Approach for Smoking Event Detection Using a Variational Autoencoder and Neural Decision Forest

Fan

Gao

2020

IEEE Access

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Smoking is associated with cancer, cardiovascular disease and premature death and can cause severe fire hazards. To assist with smoking cessation, this paper presents a wireless body area networkbased system consisting of two off-the-shelf devices, one smartphone and one smartwatch, to detect smoking events by mining the inertial sensor data from both devices. In the system, an end-to-end trainable unified model is implemented by combining a variational autoencoder with a random forest to classify the collected data into smoking and nonsmoking after data preprocessing. The variational autoencoder is adopted to learn the feature representation and deal with the class imbalance problem, and the stochastic decision forest is adopted to guide the global optimization of the parameter learning process. Extensive validation of our scheme is performed on a large dataset we collected, and our scheme yields quite promising results in terms of accuracy and efficiency.

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Identifying Smoking from Smartphone Sensor Data and Multivariate Hidden Markov Models

Cited by 3 publications

References 7 publications

Measurement Properties of Smartphone Approaches to Assess Diet, Alcohol Use, and Tobacco Use: Systematic Review

Measurement Properties of Smartphone Approaches to Assess Diet, Alcohol Use, and Tobacco Use: Systematic Review

Wearable Sensors for Monitoring of Cigarette Smoking in Free-Living: A Systematic Review

A New Approach for Smoking Event Detection Using a Variational Autoencoder and Neural Decision Forest

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