| Advances in technology have led to the development of various lightweight sensor devices that can be woven into the physical environment of our daily lives. Such systems, formally known as body sensor networks (BSNs), enable continuous and remote healthcare monitoring. The focus of this research is in the area of physical movement monitoring platforms that operate with inertial sensors and find applications in fall detection, rehabilitation, geriatric care, sports medicine, gait analysis and sports training. Since these distributed systems are extremely constrained in terms of computational power, communication, memory, and battery lifetime, development of lightweight signal processing and data reduction techniques is essential for practical deployment. Power optimization, an important objective in BSNs design, is the main motivation for this study. The tasks that are accomplished in this thesis include (1) a normative study on applications of BSNs in healthcare and sports training fields to explore signal processing requirements of the platform; (2) development of novel signal processing algorithms that optimize power consumption of the system via data reduction and inter-node collaboration; (3) development of new energy-efficient communication models and routing algorithms under collaborative signal processing paradigm. |
