Application of data fusion techniques and technologies for wearable health monitoring

- This paper emphasises the growth of interest in wearable technologies that are leading to a paradigm shift in personalised healthcare through continuous monitoring using body worn sensors.
- Data fusion techniques are discussed that provide the means to combine data from wearable sensors to infer our activities, at varying levels of detail.
- This paper studies the attributes of commercial devices in light of a continuously changing landscape.
- The key challenges that still need to be addressed are discussed so as to advance the understanding of what is needed to create truly pervasive and invisible wearable health sensing systems.

Technological advances in sensors and communications have enabled discrete integration into everyday objects, both in the home and about the person. Information gathered by monitoring physiological, behavioural, and social aspects of our lives, can be used to achieve a positive impact on quality of life, health, and well-being. Wearable sensors are at the cusp of becoming truly pervasive, and could be woven into the clothes and accessories that we wear such that they become ubiquitous and transparent. To interpret the complex multidimensional information provided by these sensors, data fusion techniques are employed to provide a meaningful representation of the sensor outputs. This paper is intended to provide a short overview of data fusion techniques and algorithms that can be used to interpret wearable sensor data in the context of health monitoring applications. The application of these techniques are then described in the context of healthcare including activity and ambulatory monitoring, gait analysis, fall detection, and biometric monitoring. A snap-shot of current commercially available sensors is also provided, focusing on their sensing capability, and a commentary on the gaps that need to be bridged to bring research to market.