Framework and Algorithms for Wearable Medical Applications
Wearable embedded systems with sensing, communication, and computing capabilities have given rise to innovations in e-health and telemedicine in general. The scope of such systems ranges from devices and mobile apps to cloud backend and analysis algorithms, all of which must be well integrated. To manage the development, operation, and evolution of such complex systems, a framework systematic framework is needed. This dissertation makes contributions in two parts. First is a framework for defining the structure of a wide range of wearable medical applications with modern cloud support. The second part includes several algorithms that can be plugged into this framework for making these systems more efficient in terms of processing performance and data size. We propose a novel QT analysis algorithm that can take advantage of GPU as well as in a server-client environment, and we show competitive results in terms of both performance and energy consumption with or without parallelization. We also propose ECG compression techniques using trained overcomplete dictionary. After constructing the dictionary through learning process with a given dataset, the signal can be compressed by sparse estimation using the trained dictionary. We propose reconstructing ECG signal from undersampled data based on compressive sensing framework that can reconstruct the ECG signals precisely from fewer samples so long as the signal is sparse or compressible. Together, these algorithms operating in the context of our proposed framework validate the effectiveness of our structured approach to the framework for wearable medical applications.