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