Mobile Health
-15%
portes grátis
Mobile Health
Sensors, Analytic Methods, and Applications
Murphy, Susan A.; Kumar, Santosh; Rehg, James M.
Springer International Publishing AG
08/2018
542
Mole
Inglês
9783319846392
15 a 20 dias
884
Descrição não disponível.
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.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
mobile health;wearable sensors;mobile computing;health data analytics;low-power sensing and computing;behavioral medicine;health interventions;mHealth;chronic diseases and conditions;mental health;machine learning;data mining;reinforcement learning;control systems engineering;just-in-time adaptive interventions;fitness trackers
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.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
mobile health;wearable sensors;mobile computing;health data analytics;low-power sensing and computing;behavioral medicine;health interventions;mHealth;chronic diseases and conditions;mental health;machine learning;data mining;reinforcement learning;control systems engineering;just-in-time adaptive interventions;fitness trackers
