Power Optimization for Sensor Hubs in Biomedical Applications

The design and development of wearable inertial sensor systems for health monitoring has garnered a huge attention in the scientific community and the industry during the last years. Such platforms have a typical architecture and common building blocks to enable data collection, data processing and feedback restitution. In this thesis we analyze power optimization techniques that can be applied to such systems. When reducing power consumption in a wearable system, different trade-offs have to be inevitably faced. We thus propose software techniques that span from well known duty cycling, frequency scaling, data compression to new paradigm such as radio triggering, heterogeneous multi-core and context aware power management.