scholarly journals Wearable Egocentric Camera as a Monitoring Tool of Free-Living Cigarette Smoking: A Feasibility Study

2019 ◽  
Vol 22 (10) ◽  
pp. 1883-1890 ◽  
Author(s):  
Masudul H Imtiaz ◽  
Delwar Hossain ◽  
Volkan Y Senyurek ◽  
Prajakta Belsare ◽  
Stephen Tiffany ◽  
...  

Abstract 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. Implications The proposed camera-based sensor system can be employed to examine cigarette smoking under free-living conditions. Smokers may accept this unobtrusive sensor for extended wear, as the sensor would not restrict the natural pattern of smoking or daily activities, nor would it require any active participation from a person except wearing it. Critical metrics of smoking behavior, such as the smoking environment and puff counts obtained from this sensor, may generate important information for smoking interventions.

Electronics ◽  
2017 ◽  
Vol 6 (4) ◽  
pp. 104 ◽  
Author(s):  
Masudul Imtiaz ◽  
Raul Ramos-Garcia ◽  
Volkan Senyurek ◽  
Stephen Tiffany ◽  
Edward Sazonov

2010 ◽  
Vol 7 (6) ◽  
pp. 706-717 ◽  
Author(s):  
Weimo Zhu ◽  
Miyoung Lee

Background:The purpose of this study was to investigate the validity and reliability evidences of the Omron BI pedometer, which could count steps taken even when worn at different locations on the body.Methods:Forty (20 males and 20 females) adults were recruited to walk wearing 5 sets, 1 set at a time, of 10 BI pedometers during testing, 1 each at 10 different locations. For comparison, they also wore 2 Yamax Digi-Walker SW-200 pedometers and a Dynastream AMP 331 activity monitor. The subjects walked in 3 free-living conditions: a fat sidewalk, stairs, and mixed conditions.Results:Except for a slight decrease in accuracy in the pant pocket locations, Omron BI pedometers counted steps accurately across other locations when subjects walked on the fat sidewalk, and the performance was consistent across devices and trials. When the subjects climbed up stairs, however, the absolute error % of the pant pocket locations increased significantly (P < .05) and similar or higher error rates were found in the AMP 331 and SW-200s.Conclusions:The Omron BI pedometer can accurately count steps when worn at various locations on the body in free-living conditions except for front pant pocket locations, especially when climbing stairs.


2017 ◽  
Author(s):  
Andy Skinner ◽  
Christopher J Stone ◽  
Hazel Doughty ◽  
Marcus Robert Munafo

Introduction: Passive detection of cigarette smoking offers potential for considerable benefits to researchers exploring smoking behaviour and designing precision behaviour change interventions. A number of systems have been developed that either use bespoke sensing technology, or rely on connected smartphones to run analytical software. Here we present StopWatch, a system for passive detection of cigarette smoking that runs on a smartwatch and does not require additional sensing or a connected smartphone.Methods: Our system uses motion data from the accelerometer and gyroscope in an Android smartwatch to detect the signature hand movements of cigarette smoking. It uses a three-stage analytical pipeline to transform raw motion data into motion features, and in turn into individual drags and instances of smoking. This pipeline runs on the smartwatch, and does not require a smartphone.Results: We validated the system in daily smokers (n=13) in laboratory and free-living conditions running on an Android LG G-Watch. In free-living conditions, over a 24 hour period, the system achieved precision of 86% and recall of 71%.Conclusions: StopWatch is a system for passive measurement of cigarette smoking that runs entirely on a commercially available smartwatch. It runs on an Android smartwatch and requires no smartphone so the cost is low. No bespoke sensing equipment is needed, and it uses a mass-market smartwatch, so participant burden is low. Performance is currently lower than other more expensive and complex systems, though adequate for some applications. Future developments will focus on enhancing performance, and validation on a range of smartwatches.


2020 ◽  
Author(s):  
Paige Hulls ◽  
Christopher J Stone ◽  
Frank de Vocht ◽  
Marcus Robert Munafo ◽  
Rebecca Richmond ◽  
...  

Background: A number of different systems are available for passive detection of cigarette smoking, but few studies have reported the feasibility of using these in free-living conditions, and none so far have reported specifically on the feasibility of using these in workplace settings. Methods: We conducted a feasibility study of using stopWatch, a smartwatch-based system for passive detection of cigarette smoking, in workers in the construction industry. Participants wore stopWatch for three days midweek at work. Some also wore for three days over a weekend at home. They also kept paper diaries of cigarettes smoked. Results: Six cigarette smokers and two vapers were recruited. Mean number of cigarettes smoked per day was 6.1 and stopWatch detected on average 31% of these. Insufficient data were available for meaningful comparison of performance at work and home. No occurrences of vaping were detected as cigarette smoking by stopWatch. Discussion: The percentage of cigarettes smoked detected by stopWatch was lower than previously reported in free-living conditions (71%). We identified a number of practical reasons for this, including not keeping the smartwatch battery properly charged, the stopWatch application not being restarted correctly after the battery ran flat, and participants not wearing the smartwatch correctly. We make recommendations for addressing these issues.Conclusion: This is the first study of the feasibility of using a system for passive detection of cigarette smoking in a workplace setting. Several practical issues have been identified and recommendations made for improving the use of systems of this kind in future studies.


Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 1376-P
Author(s):  
GREGORY P. FORLENZA ◽  
BRUCE BUCKINGHAM ◽  
JENNIFER SHERR ◽  
THOMAS A. PEYSER ◽  
JOON BOK LEE ◽  
...  

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 207-OR
Author(s):  
BRUCE A. BUCKINGHAM ◽  
JENNIFER SHERR ◽  
GREGORY P. FORLENZA ◽  
THOMAS A. PEYSER ◽  
JOON BOK LEE ◽  
...  

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jari Lipsanen ◽  
Liisa Kuula ◽  
Marko Elovainio ◽  
Timo Partonen ◽  
Anu-Katriina Pesonen

AbstractThe individual variation in the circadian rhythms at the physiological level is not well understood. Albeit self-reported circadian preference profiles have been consolidated, their premises are grounded on human experience, not on physiology. We used data-driven, unsupervised time series modelling to characterize distinct profiles of the circadian rhythm measured from skin surface temperature in free-living conditions. We demonstrate the existence of three distinct clusters of individuals which differed in their circadian temperature profiles. The cluster with the highest temperature amplitude and the lowest midline estimating statistic of rhythm, or rhythm-adjusted mean, had the most regular and early-timed sleep–wake rhythm, and was the least probable for those with a concurrent delayed sleep phase, or eveningness chronotype. While the clusters associated with the observed sleep and circadian preference patterns, the entirely unsupervised modelling of physiological data provides a novel basis for modelling and understanding the human circadian functions in free-living conditions.


Nutrients ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 4154
Author(s):  
Emily Bell ◽  
Sabrina Binkowski ◽  
Elaine Sanderson ◽  
Barbara Keating ◽  
Grant Smith ◽  
...  

The optimal time to bolus insulin for meals is challenging for children and adolescents with type 1 diabetes (T1D). Current guidelines to control glucose excursions do not account for individual differences in glycaemic responses to meals. This study aimed to examine the within- and between-person variability in time to peak (TTP) glycaemic responses after consuming meals under controlled and free-living conditions. Participants aged 8–15 years with T1D ≥ 1 year and using a continuous glucose monitor (CGM) were recruited. Participants consumed a standardised breakfast for six controlled days and maintained their usual daily routine for 14 free-living days. CGM traces were collected after eating. Linear mixed models were used to identify within- and between-person variability in the TTP after each of the controlled breakfasts, free-living breakfasts (FLB), and free-living dinners (FLD) conditions. Thirty participants completed the study (16 females; mean age and standard deviation (SD) 10.5 (1.9)). The TTP variability was greater within a person than the variability between people for all three meal types (between-person vs within-person SD; controlled breakfast 18.5 vs 38.9 minutes; FLB 14.1 vs 49.6 minutes; FLD 5.7 vs 64.5 minutes). For the first time, the study showed that within-person variability in TTP glycaemic responses is even greater than between-person variability.


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