UCLA

Affordable, wearable, embedded, wireless medical sensor systems that

enable continuous long term monitoring of physiological signals could

revolutionize health care. Realizing this vision demands devices that

are small, unobtrusive and low power. Effectively inferring health

conditions begins by acquiring physiological signals of interest and

decisions made about what signals are acquired, when, where and at

what rate affect not only the energy efficiency of the sampling

process but also that of other downstream components in the signal

processing chain.

While the Nyquist sampling theorem provides for

exact reconstruction from discrete-time samples,

the prescribed rate is often wasteful for physiological sensing applications

since it neither exploits the structure of signals fully nor does it take into

account that many applications don't require full reconstruction at all.

This dissertation illustrates how energy efficiency of the entire system can be improved

by targeting just the signal acquisition process while being cognizant of the entire

sensing information stack, from sampling, processing and communication to the

top-level application inferences.

A key ingredient that makes optimizing the sensing stack worthwhile is that

the sampling stage, which is usually abstracted away from the system,

can now utilize sophisticated methods that have emerged in the past few years.

Recent advances in sampling and recovery techniques have demonstrated

considerable rate reductions by employing stronger models of the phenomenon

coupled with application-specific objectives (detection or control vs.

reconstruction), which potentially translates to higher energy, processing and

communications efficiency at the system level.

This research describes four major thrusts that span the processing chain from

hardware to algorithms to inferences. First, recognizing that signal conditioning

front-end circuits could account for a large portion of the energy expenditure

in low power sensing, we demonstrate how prudently duty cycling them could

increase device lifetime by threefold and reduce data rate by almost fourfold

for an electrocardiography monitor.

Then, we go on to show how one could further

slash data rates using the new theory of compressed sensing. For a neural spike recorder, we exploit the fact that action potentials have both a structure and short term stability in their morphology. This meant that we could utilize historical signal information to optimize and adapt compressed sensing recovery, with only receiver-side modifications, doubling the compression ratio.

Third, since body area networks are prone to congestion and interference,

we propose a rate control algorithm for the wireless channel so that the

most important data from the most informative sensors gets delivered

for maximum inference quality. Finally, we prove that compressed

sensing could be utilized not only to compress signals but could also improve the robustness of sensor transmissions at low computational cost by viewing it as joint source-channel coding for wireless erasure channels.