Application of data fusion techniques and technologies for wearable health monitoring

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

Family Context as It Relates to Weight-Related Behaviors in Preadolescent African American Girls

To examine family context in relation to body image, weight concerns, and weight control behaviors in preadolescent African American girls. Methods: Cross-sectional baseline data were analyzed from 303 African American girls 8 to 10 years old and a caregiver in the Girls health Enrichment Multi-site Studies Phase 2(GEMS), an obesity prevention intervention trial. Results: Fruit, juice, and vegetable accessibility and family support for healthy eating and physical activity were significantly related to girls\u27 body image and weight control behaviors. Conclusions: A comprehensive understanding of family factors may improve future programs aimed at preadolescent girls