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
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.