國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Mobile healthsensors, analytic metho...

Kumar, Santosh.

Mobile healthsensors, analytic methods, and applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Mobile healthedited by James M. Rehg, Susan A. Murphy, Santosh Kumar.
其他題名:	sensors, analytic methods, and applications /
其他作者:	Rehg, James M.
出版者:	Cham :Springer International Publishing :2017.
面頁冊數:	xl, 542 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Medical informatics.
電子資源:	http://dx.doi.org/10.1007/978-3-319-51394-2
ISBN:	9783319513942$q(electronic bk.)

Mobile healthsensors, analytic methods, and applications /
Mobile healthsensors, analytic methods, and applications /[electronic resource] :edited by James M. Rehg, Susan A. Murphy, Santosh Kumar. - Cham :Springer International Publishing :2017. - xl, 542 p. :ill., digital ;24 cm.

Introduction to Section 1: mHealth Applications and Tools -- StudentLife: Using Smartphone to Assess Mental Health and Academic Performance of College Students -- Circadian Computing: Sensing, Modeling, and Maintaining Biological Rhythms -- Design Lessons from a Micro-Randomized Pilot Study in Mobile Health -- The Use of Asset-Based Community Development in a Research Project Aimed at Developing mHealth Technologies for Older Adults -- Designing Mobile Health Technologies for Self-Monitoring: The Bit Counter as a Case Study -- mDebugger: Assessing and Diagnosing the Fidelity and Yield of Mobile Sensor Data -- Introduction to Section II: Sensors to mHealth Markers -- Challenges and Opportunities in Automated Detection of Eating Activity -- Detecting Eating and Smoking Behavior Using Smartwatches -- Wearable Motion Sensing Devices and Algorithms for Precise Healthcare Diagnostics and Guidance -- Paralinguistic Analysis of Children's Speech in Natural Environments -- Pulmonary Monitoring Using Smartphones -- Wearable Sensing of Left Ventricular Function -- A new direction for Biosensing: RF sensors for monitoring cardio-pulmonary function -- Wearable Optical Sensors -- Introduction to Section III: Markers to mHealth Predictors -- Exploratory Visual Analytics of Mobile Health Data: Sensemaking Challenges and Opportunities -- Learning Continuous-Time Hidden Markov Models for Event Data -- Time-series Feature Learning with Applications to Healthcare Domain -- From Markers to Interventions: The Case of Just-in-Time Stress Intervention -- Introduction to Section IV: Predictors to mHealth Interventions -- Modeling Opportunities in mHealth Cyber-Physical Systems -- Control Systems Engineering for Optimizing Behavioral mHealth Interventions -- From Ads to Interventions: Contextual Bandits in Mobile Health -- Towards Health Recommendation Systems: An Approach for Providing Automated Personalized Health Feedback from Mobile Data.

This volume provides a comprehensive introduction to mHealth technology and is accessible to technology-oriented researchers and practitioners with backgrounds in computer science, engineering, statistics, and applied mathematics. The contributing authors include leading researchers and practitioners in the mHealth field. The book offers an in-depth exploration of the three key elements of mHealth technology: the development of on-body sensors that can identify key health-related behaviors (sensors to markers), the use of analytic methods to predict current and future states of health and disease (markers to predictors), and the development of mobile interventions which can improve health outcomes (predictors to interventions) Chapters are organized into sections, with the first section devoted to mHealth applications, followed by three sections devoted to the above three key technology areas. Each chapter can be read independently, but the organization of the entire book provides a logical flow from the design of on-body sensing technology, through the analysis of time-varying sensor data, to interactions with a user which create opportunities to improve health outcomes. This volume is a valuable resource to spur the development of this growing field, and ideally suited for use as a textbook in an mHealth course.

ISBN: 9783319513942$q(electronic bk.)

Standard No.: 10.1007/978-3-319-51394-2doiSubjects--Topical Terms:

196487
Medical informatics.

LC Class. No.: R858 / .M63 2017

Dewey Class. No.: 610.285

Mobile healthsensors, analytic methods, and applications /
LDR:04259nmm a2200313 a 4500 001 520132
003 DE-He213
005 20170712145722.0
006 m d
007 cr nn 008maaau
008 180425s2017 gw s 0 eng d
020 $a 9783319513942$q(electronic bk.)
020 $a 9783319513935$q(paper)
024 7 $a 10.1007/978-3-319-51394-2 $2 doi
035 $a 978-3-319-51394-2
040 $a GP $c GP
041 0 $a eng
050 4 $a R858 $b .M63 2017
072 7 $a UBH $2 bicssc
072 7 $a MED000000 $2 bisacsh
082 0 4 $a 610.285 $2 23
090 $a R858 $b .M687 2017
245 0 0 $a Mobile health $h [electronic resource] : $b sensors, analytic methods, and applications / $c edited by James M. Rehg, Susan A. Murphy, Santosh Kumar.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2017.
300 $a xl, 542 p. : $b ill., digital ; $c 24 cm.
505 0 $a Introduction to Section 1: mHealth Applications and Tools -- StudentLife: Using Smartphone to Assess Mental Health and Academic Performance of College Students -- Circadian Computing: Sensing, Modeling, and Maintaining Biological Rhythms -- Design Lessons from a Micro-Randomized Pilot Study in Mobile Health -- The Use of Asset-Based Community Development in a Research Project Aimed at Developing mHealth Technologies for Older Adults -- Designing Mobile Health Technologies for Self-Monitoring: The Bit Counter as a Case Study -- mDebugger: Assessing and Diagnosing the Fidelity and Yield of Mobile Sensor Data -- Introduction to Section II: Sensors to mHealth Markers -- Challenges and Opportunities in Automated Detection of Eating Activity -- Detecting Eating and Smoking Behavior Using Smartwatches -- Wearable Motion Sensing Devices and Algorithms for Precise Healthcare Diagnostics and Guidance -- Paralinguistic Analysis of Children's Speech in Natural Environments -- Pulmonary Monitoring Using Smartphones -- Wearable Sensing of Left Ventricular Function -- A new direction for Biosensing: RF sensors for monitoring cardio-pulmonary function -- Wearable Optical Sensors -- Introduction to Section III: Markers to mHealth Predictors -- Exploratory Visual Analytics of Mobile Health Data: Sensemaking Challenges and Opportunities -- Learning Continuous-Time Hidden Markov Models for Event Data -- Time-series Feature Learning with Applications to Healthcare Domain -- From Markers to Interventions: The Case of Just-in-Time Stress Intervention -- Introduction to Section IV: Predictors to mHealth Interventions -- Modeling Opportunities in mHealth Cyber-Physical Systems -- Control Systems Engineering for Optimizing Behavioral mHealth Interventions -- From Ads to Interventions: Contextual Bandits in Mobile Health -- Towards Health Recommendation Systems: An Approach for Providing Automated Personalized Health Feedback from Mobile Data.
520 $a This volume provides a comprehensive introduction to mHealth technology and is accessible to technology-oriented researchers and practitioners with backgrounds in computer science, engineering, statistics, and applied mathematics. The contributing authors include leading researchers and practitioners in the mHealth field. The book offers an in-depth exploration of the three key elements of mHealth technology: the development of on-body sensors that can identify key health-related behaviors (sensors to markers), the use of analytic methods to predict current and future states of health and disease (markers to predictors), and the development of mobile interventions which can improve health outcomes (predictors to interventions) Chapters are organized into sections, with the first section devoted to mHealth applications, followed by three sections devoted to the above three key technology areas. Each chapter can be read independently, but the organization of the entire book provides a logical flow from the design of on-body sensing technology, through the analysis of time-varying sensor data, to interactions with a user which create opportunities to improve health outcomes. This volume is a valuable resource to spur the development of this growing field, and ideally suited for use as a textbook in an mHealth course.
650 0 $a Medical informatics. $3 196487
650 0 $a Wireless communication systems in medical care. $3 676419
650 1 4 $a Computer Science. $3 212513
650 2 4 $a Health Informatics. $3 274212
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 252959
650 2 4 $a Statistics for Life Sciences, Medicine, Health Sciences. $3 274067
650 2 4 $a Data Mining and Knowledge Discovery. $3 275288
650 2 4 $a Computer Communication Networks. $3 218087
700 1 $a Rehg, James M. $3 789229
700 1 $a Murphy, Susan A. $3 789230
700 1 $a Kumar, Santosh. $3 682997
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-51394-2
950 $a Computer Science (Springer-11645)