Special Section: Sensor Informatics and Quantified Self
Wearable sensors, combined with signal processing, machine learning, and the ability to collect large sets of human data comfortably 24/7, are advancing new ways to learn about human wellbeing. Measurements that used to be confined to short-term sampling in a lab or medical facility are now able to be conducted continuously while at home, work, sleep, or play. Studies are no longer limited to a focus on disease progression or to the effect of therapeutic measures provided in clinical settings – instead, it is becoming possible to quantify healthy activities and behavior, and capture how these slowly change as illness develops or progresses.
The Quantified Self movement, where people can monitor their own health and fitness-related data, is closely linked to the emergence of new methods in biometric sensing. As the papers in this volume show, relatively inexpensive sensors, such as accelerometers, can be pressed into service for research and medical applications with complex analytical payoffs. The research progress evident in the papers published here, which mainly focus on increased sensitivity, accuracy, and individualized calibration, as well as on the identification of human relevant and human readable patterns from unannotated data, is likely to drive a wide range of new applications whose reach extends far beyond conventional health and medical research.
This special issue includes the following papers, which all passed over a high bar including iterations of scientific peer review with a team of expert reviewers:
- Design and Evaluation of an Intelligent Remote Tidal Volume Variability Monitoring System in E-Health Applications
- Quantifying and Reducing Posture-Dependent Distortion in Ballistocardiogram Measurements
- Data-Driven Rule Mining and Representation of Temporal Patterns in Physiological Sensor Data
- Identifying Physical Activity Profiles in COPD Patients Using Topic Models
- Personalization of Energy Expenditure Estimation in Free Living Using Topic Models