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cr nn 008mamaa |
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170712s2017 gw | s |||| 0|eng d |
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|a 9783319513942
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| 024 |
7 |
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|a 10.1007/978-3-319-51394-2
|2 doi
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| 040 |
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|a Sistema de Bibliotecas del Tecnológico de Costa Rica
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| 245 |
1 |
0 |
|a Mobile Health :
|b Sensors, Analytic Methods, and Applications /
|c edited by James M. Rehg, Susan A. Murphy, Santosh Kumar.
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| 250 |
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|a 1st ed. 2017.
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| 260 |
# |
# |
|a Cham :
|b Springer International Publishing :
|b Imprint: Springer,
|c 2017.
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| 300 |
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|a XL, 542 p. 128 illus., 100 illus. in color. :
|b online resource.
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 505 |
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|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.
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| 650 |
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0 |
|a Health informatics.
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| 650 |
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0 |
|a Artificial intelligence.
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| 650 |
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0 |
|a Statistics .
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| 650 |
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0 |
|a Data mining.
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| 650 |
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0 |
|a Computer communication systems.
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| 650 |
1 |
4 |
|a Health Informatics.
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| 650 |
2 |
4 |
|a Artificial Intelligence.
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| 650 |
2 |
4 |
|a Statistics for Life Sciences, Medicine, Health Sciences.
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| 650 |
2 |
4 |
|a Data Mining and Knowledge Discovery.
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| 650 |
2 |
4 |
|a Health Informatics.
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| 650 |
2 |
4 |
|a Computer Communication Networks.
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| 700 |
1 |
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|a Rehg, James M.
|e editor.
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| 700 |
1 |
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|a Murphy, Susan A.
|e editor.
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| 700 |
1 |
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|a Kumar, Santosh.
|e editor.
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| 710 |
2 |
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|a SpringerLink (Online service)
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| 773 |
0 |
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|t Springer eBooks
|