UCLA

Deployments of sensors for real-world applications face critical challenges that can make large-scale adoption difficult or impossible to achieve due to prohibitive cost. Much of this cost can be reduced by enabling the adoption of more cost-effective behaviors by users and by guiding users on proper usage of technology solutions. Proper guidance for both subject behaviors and sensor usage training can reduce barriers to adoption by improving compliance and providing guarantees.

The aim of this research is to design and develop tractable and scalable monitoring system architectures for large-scale, real world usage that can guide users to adopt more beneficial behaviors. To achieve this aim, system designs were developed in direct support of monitoring applications to address problems in healthcare and energy conservation.

For energy conservation, the research used low-cost energy monitoring as a building block to test the effectiveness of various types of information feedback on real consumers in controlled experiments. In healthcare, we addressed critical unmet needs in post-operative patient care using interactive mobile technology solutions, integrated with physiological sensing to implement sensor usage guidance. Scalability was enabled by the development of hardware and software systems that allow subjects to easily perform data collection with guarantees on reliability and validity. To provide the complete system, end-to-end software and hardware architectures were developed to combine the core research with the other necessary components and field validations have been conducted.