HealthWear 2016 - 1st EAI International Conference on Wearables in Healthcare

A variety of relevant health and fitness parameters are now being captured via an ecosystem of consumer-oriented wearable self-tracking devices, smartphone apps and related services. Techniques from information science, sociology, psychology, statistics, machine learning and data mining are applied to analyze collected data. These techniques provide new opportunities to enrich understanding of individual and population health. Self-tracking data can provide better measures of everyday behavior and lifestyle and can complement more traditional clinical data collection, towards a comprehensive picture of health.
HealthWear'16 will bring together researchers, developers, and industry professionals from both Healthcare and Quantified Self communities to discuss key issues, opportunities and obstacles for personal health data research. These include challenges of capturing, summarizing, presenting and retrieving relevant information from heterogeneous sources to support a new vision of pervasive personal healthcare.
HIGHLIGHTS
Accepted papers will be published in Springer's LNICST series and will appear in the SpringerLink, one of the largest digital libraries online that covers a variety of scientific disciplines, as well as in EU Digital Library (EUDL).
The proceedings are submitted for inclusion to the leading indexing services: DBLP, Google Scholar, Thomson Scientific ISI Proceedings, EI Elsevier Engineering Index, CrossRef, Scopus, as well as ICST's own EU Digital Library (EUDL).
Selected papers will be invited for publication in the EAI endorsed Transaction Pervasive Health and Technology.
The event is endorsed by the European Alliance for Innovation, a leading community-based organization devoted to the advancement of innovation in the field of ICT.
TOPICS of interest include, but are not limited to:
Personal Health Informatics
Quantified Self for Healthcare
Activity Monitors and Devices
Self-Tracking
Healthcare Knowledge Representation & Reasoning
Health Data acquisition, analysis and mining
Healthcare Information Systems
Validity, reliability, usability, and effectiveness of Self-Tracking devices
Experiment Design
Social and Psychological investigation into Self-Tracking practices
Health Monitoring in clinical and lifestyle environments
Sensors and actuators for Wellness, Fitness and Rehabilitation
Innovative Algorithms for assessment of long-term physiological and behavioural data
Models for interpreting medical sensor data
Lifelogging, lifecaching, lifestreaming
Biometric data
Medical Self-diagnostics