UCLA

Wearable sensing systems are becoming widely used for a variety of applications including sports, entertainment, and the military. These systems have recently enabled a variety of medical monitoring and diagnostic applications. Such sensing systems have high potential to significantly improve the quality of life for large segments of the population and enable conceptually new types of applications. However, various research challenges including energy sensitivity and semantic complexity must be addressed before these devices can reach their full potential in improving our lives. The need for multiple sensors, high frequency sampling and constant monitoring leads these systems to be power-hungry and expensive with a short operation lifetime. In addition these systems generate a massive amount of data, where designing efficient and effective data mining algorithms to interpret and analyze the data is very costly due to field experts' involvement.

This dissertation presents a methodology that takes advantage of contextual and semantic properties in human physiological behavior to enable efficient design and optimization of such systems from the data and information point of view. The methodology uses combinatorial modeling and simultaneous minimization to reduce the wireless communication and local processing power consumption. The effectiveness of this technique is shown on an insole instrumented with 99 pressure sensors placed in each shoe, which is used for human gait analysis. Further, the proposed methodology is used to design unsupervised data mining techniques to extract information such as frequent or rare events and patterns from collected multi-dimensional time series data of wearable sensing devices, where high level of noise and uncertainty is inherent. This approach transforms multi-dimensional time series data to combinatorial space and constructs behavior models, which is used for frequent and rare event detection and pattern classification. The effectiveness of this method is demonstrated by applying this technique to discovery and classification of frequent human activities and abnormalities.