Empath: A System for the Collection, Storage, Analysis, and Reporting of Objective Behavioral Measures 901 views

Remote health monitoring deployed in homes could help streamline the efficiency of the medical system by decreasing the costs of senior care and providing preventative care to keep people out of hospitals. Besides for caring for a particular person, these systems will generate tremendous amount of aggregate data that can be used to help diagnose diseases and run longitudinal studies on epidemiology. Most systems are not yet flexible enough to handle adding additional modalities into the system or to leverage the cloud's horizontal scalablity for storage, analysis, and display of this data. This dissertation presents a novel framework that sets itself apart from existing remote health monitoring systems for its scalability and flexibility. In addition, this work advances two key technologies for home healthcare. The first is the creation and evaluation of cheap and non-invasive sleep monitoring systems and novel accelerometor-based systems from RFIDs and motes. Secondly, this thesis demonstrates that the general features from speech in the home can be a useful modality for measuring social interaction and mood and promotes a solution to existing technical problems. Ambient monitoring of speech in the home has not been successful primarily because distortion from a room's acoustics negatively impacts classification results. This work presents a novel matched-condition classifier using cuboid acoustic simulation to achieve accuracy comparable to ideal close-to-microphone conditions. Most health monitoring systems have only been tested in a lab and under very scripted scenarios. This system, however, has been used in three different applications: monitoring sleep behaviors and stress for those who suffer from severe epilepsy in a clinical study, another clinical application that studies the relationship of incontinence with sleep agitation for those suffering from Alzheimer's disease, and an in-home deployment monitoring important factors of depression. We present the commonalities among these different applications and show how to adapt the system for these purposes.

PHD (Doctor of Philosophy)

smart homes; speech monitoring; depression monitoring

English

All rights reserved (no additional license for public reuse)

2013-11-12