Smoking recognition with smartwatch sensors in different postures and impact of user’s height

Ağaç, Sümeyye; Shoaib, Muhammad; İncel, Özlem Durmaz

doi:10.3233/ais-200558

Cited by 4 publications

(8 citation statements)

References 28 publications

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“…Although there have been prior reports [8,9,[12][13][14]18] of identifying smoking sessions using smartwatches, to our knowledge, there has been no other smoking topography work with which to compare these results. Our reported results constitute the first instance of comparing smoking data collected from smartwatches to smoking data collected from the industry-standard CReSS device.…”

Section: Comparison To Prior Workmentioning

confidence: 96%

“…These include generic activities such as step counts, sleep detection, and rest periods, while others include more specific activities such as eating [4], drinking [5], managing diabetes [6,7], or smoking [8]. Previous work has established the use of wristworn devices in observing and interpreting smoking behavior in laboratory settings [9][10][11][12][13] and in situ [8,14]. Some of these devices use proprietary sensors [5,[15][16][17], while others use off-the-shelf devices such as smartwatches [8,9,14,18,19].…”

Section: Introductionmentioning

confidence: 99%

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Quantification of Smoking Characteristics Using Smartwatch Technology: Pilot Feasibility Study of New Technology

Cole¹,

Powers²,

Tomko³

et al. 2021

JMIR Form Res

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Background While there have been many technological advances in studying the neurobiological and clinical basis of tobacco use disorder and nicotine addiction, there have been relatively minor advances in technologies for monitoring, characterizing, and intervening to prevent smoking in real time. Better understanding of real-time smoking behavior can be helpful in numerous applications without the burden and recall bias associated with self-report. Objective The goal of this study was to test the validity of using a smartwatch to advance the study of temporal patterns and characteristics of smoking in a controlled laboratory setting prior to its implementation in situ. Specifically, the aim was to compare smoking characteristics recorded by Automated Smoking PerceptIon and REcording (ASPIRE) on a smartwatch with the pocket Clinical Research Support System (CReSS) topography device, using video observation as the gold standard. Methods Adult smokers (N=27) engaged in a video-recorded laboratory smoking task using the pocket CReSS while also wearing a Polar M600 smartwatch. In-house software, ASPIRE, was used to record accelerometer data to identify the duration of puffs and interpuff intervals (IPIs). The recorded sessions from CReSS and ASPIRE were manually annotated to assess smoking topography. Agreement between CReSS-recorded and ASPIRE-recorded smoking behavior was compared. Results ASPIRE produced more consistent number of puffs and IPI durations relative to CReSS, when comparing both methods to visual puff count. In addition, CReSS recordings reported many implausible measurements in the order of milliseconds. After filtering implausible data recorded from CReSS, ASPIRE and CReSS produced consistent results for puff duration (R2=.79) and IPIs (R2=.73). Conclusions Agreement between ASPIRE and other indicators of smoking characteristics was high, suggesting that the use of ASPIRE is a viable method of passively characterizing smoking behavior. Moreover, ASPIRE was more accurate than CReSS for measuring puffs and IPIs. Results from this study provide the foundation for future utilization of ASPIRE to passively and accurately monitor and quantify smoking behavior in situ.

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Section: Comparison To Prior Workmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Quantification of Smoking Characteristics Using Smartwatch Technology: Pilot Feasibility Study of New Technology

Cole¹,

Powers²,

Tomko³

et al. 2021

JMIR Form Res

View full text Add to dashboard Cite

show abstract

“…In our previous work [4], the same dataset was used and the effects of the parameters were analyzed using 4 different window sizes, 63 features which are calculated separately for each sensor, 4 different sensors, 2 different sensor combinations, 3 classification algorithms (SVM, RF, MLP)). The results showed that simple features including median, standard deviation, minimum, maximum, range, and mean can be useful in recognizing smoking activities.…”

Section: Related Workmentioning

confidence: 99%

“…Smoking is one of the activities that users may be interested in tracking. Especially, for smoking cessation programs, it may be practical to automatically track the number of cigarettes smoked instead of self-reporting which puts a burden on the user [4].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Deep Learning Models for Smoking Recognition with Smartwatch and Smartphone Sensors

Akan

Ağaç

İncel

2021

Balkan Journal of Electrical and Computer Engineering

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Smartwatches and smartphones are extensively used in human activity recognition, particularly for step counting and daily sports applications, thanks to the motion sensors integrated into these devices. Machine learning algorithms are often utilized to process sensor data and classify the activities. There are many studies that explore the use of traditional classification algorithms in activity recognition, however, recently, deep learning approaches are also receiving attention. In this paper, we utilize a dataset that particularly consists of smoking-related activities and explore the recognition performance of three deep learning architectures, namely Long-Short Term Memory (LSTM), Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN).We evaluate their performances according to different hyperparameters, different sensor types and device types. The results exhibit that the performance of LSTM is much higher than that of CNN and RNN. Moreover, the use of magnetometer and gyroscope together with accelerometer data improves the performance. Use of data from smartphone sensors also enhances the performance results and the final accuracy with the best parameter combinations is observed to be 98.2%.

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Human Activity Recognition with Smart Watches Using Federated Learning

Gönül,

Incel,

Isiklar Alptekin

2022

Lecture Notes in Networks and Systems

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Smoking recognition with smartwatch sensors in different postures and impact of user’s height

Cited by 4 publications

References 28 publications

Quantification of Smoking Characteristics Using Smartwatch Technology: Pilot Feasibility Study of New Technology

Quantification of Smoking Characteristics Using Smartwatch Technology: Pilot Feasibility Study of New Technology

Evaluation of Deep Learning Models for Smoking Recognition with Smartwatch and Smartphone Sensors

Human Activity Recognition with Smart Watches Using Federated Learning

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