東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Wearable Sensor Systems to Study the...

Imtiaz, Masudul Haider.

FindBook

Google Book

Amazon

博客來

Wearable Sensor Systems to Study the Physiological and Behavioral Manifestation of Cigarette Smoking in Free-Living.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Wearable Sensor Systems to Study the Physiological and Behavioral Manifestation of Cigarette Smoking in Free-Living./
作者:	Imtiaz, Masudul Haider.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:	188 p.
附註:	Source: Dissertations Abstracts International, Volume: 81-04, Section: A.
Contained By:	Dissertations Abstracts International81-04A.
標題:	Electrical engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13897444
ISBN:	9781085799911

Wearable Sensor Systems to Study the Physiological and Behavioral Manifestation of Cigarette Smoking in Free-Living.
Imtiaz, Masudul Haider.

Wearable Sensor Systems to Study the Physiological and Behavioral Manifestation of Cigarette Smoking in Free-Living. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 188 p.

Source: Dissertations Abstracts International, Volume: 81-04, Section: A.

Thesis (Ph.D.)--The University of Alabama, 2019.

This item must not be sold to any third party vendors.

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.

ISBN: 9781085799911Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

Cigarette Smoking

Wearable Sensor Systems to Study the Physiological and Behavioral Manifestation of Cigarette Smoking in Free-Living.
LDR:04405nmm a2200493 4500 001 2277266
005 20210521101643.5
008 220723s2019 ||||||||||||||||| ||eng d
020 $a 9781085799911
035 $a (MiAaPQ)AAI13897444
035 $a AAI13897444
040 $a MiAaPQ $c MiAaPQ
100 1 $a Imtiaz, Masudul Haider. $3 3555580
245 1 0 $a Wearable Sensor Systems to Study the Physiological and Behavioral Manifestation of Cigarette Smoking in Free-Living.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2019
300 $a 188 p.
500 $a Source: Dissertations Abstracts International, Volume: 81-04, Section: A.
500 $a Advisor: Sazonov, Edward.
502 $a Thesis (Ph.D.)--The University of Alabama, 2019.
506 $a This item must not be sold to any third party vendors.
506 $a This item must not be added to any third party search indexes.
520 $a 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.
590 $a School code: 0004.
650 4 $a Electrical engineering. $3 649834
650 4 $a Physiology. $3 518431
650 4 $a Epidemiology. $3 568544
650 4 $a Computer engineering. $3 621879
650 4 $a Public health. $3 534748
650 4 $a Biomedical engineering. $3 535387
650 4 $a Health sciences. $3 3168359
650 4 $a Toxicology. $3 556884
650 4 $a Information technology. $3 532993
650 4 $a Behavioral psychology. $3 2122788
650 4 $a Information science. $3 554358
653 $a Cigarette Smoking
653 $a Deep Learning
653 $a Egocentric Camera
653 $a Heart Rate Variability
653 $a Object Detection
653 $a Wearable Sensors
690 $a 0544
690 $a 0573
690 $a 0383
690 $a 0384
690 $a 0489
690 $a 0566
690 $a 0464
690 $a 0541
690 $a 0723
690 $a 0766
690 $a 0719
710 2 $a The University of Alabama. $b Electrical and Computer Engineering. $3 1674201
773 0 $t Dissertations Abstracts International $g 81-04A.
790 $a 0004
791 $a Ph.D.
792 $a 2019
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13897444