Testing the Feasibility of Batteryless Physiological Monitoring

University Of Florida, Gainesville FL

Investigators

Abstract

This award studies the ways to monitor the most common physiological variables (heart rate, blood pressure, respiration and brain activity) with miniature devices that can harvest energy from the body instead of being powered by batteries, and using electrodes that can be applied to the skin as a "tattoo". This next generation of Mobile Health (mHealth) devices will improve further the national health and wellbeing. A major bottleneck towards the goal is how to decrease the power consumption of algorithms required to extract information from the collected signals. The aim of this project is to design, implement and validate a new ultra-low power signal processing solution that does not require digital computers, but much simpler digital devices driven by input pulse trains. The project will also train two graduate students in the theory and technology to design the next generation of biomedical devices. The award will develop new pulse based algorithms and a reconfigurable hardware platform that amplifies, converts and quantifies structure of the signals in real time. More specifically, the research plan includes two synergestic aims: the first aim develops a mathematical framework based on signal processing and a statistical-syntactic approach to learn directly from data the structure of the input. A nonlinear state model called KAARMA (kernel adaptive autoregressive moving average model) will be trained statistically from data to recognize events with clinical significance. Once trained, KAARMA can be converted in a combination of finite state machines and memory tables that can easily be implemented in ultra-low power reconfigurable digital logic platform to design ambulatory monitoring of physiological variables for mHealth. No digital signal processors are needed in the deployed proposed device, lowering power consumption, maintaining programmability and the quality of the digital extraction of information. The second aim is to design an ultra-low power reconfigurable analog front-end sensing integrated circuit using mainly digital standard cells to implement a variable number of channels, multipurpose analog amplification and filtering, and the finite state machines. The expected goal is to demonstrate power consumption of less than 5 microwatts to analyze one channel of electrocardiogram (ECG). The KAARMA will be extended to blood pressure, respiration and brain activity. Validation with competing technologies will be conducted in the Physionet database.