Mobile Health Sensors, Analytic Methods, and Applications /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Rehg, James M. (Επιμελητής έκδοσης), Murphy, Susan A. (Επιμελητής έκδοσης), Kumar, Santosh (Επιμελητής έκδοσης)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2017.
• Θέματα: Computer science.; Health informatics.; Computer communication systems.; Data mining.; Artificial intelligence.; Statistics.; Computer Science.; Health Informatics.; Artificial Intelligence (incl. Robotics).; Statistics for Life Sciences, Medicine, Health Sciences.; Data Mining and Knowledge Discovery.; Computer Communication Networks.

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 practitioner...

Πίνακας περιεχομένων:

• 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.