Acoustic monitoring of first responders physiology for health and performance surveillance

Comments on, "Acoustic Monitoring of first responder's physiology for health and performance surveillance" Michael Scanlon, Proceedings of SPIE -- Volume 4708, Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Defense and Law Enforcement, Edward M. Carapezza, Editor, August 2002, pp. 342-353

The main focus is on body-worn acoustic sensors located at the nech to detect heartbeats and other physiological parameters. The author suggests that these sensors do a pretty good job but during rigorous activity session, a lot of artifacts get added which prevents infering conclusions. But the author argues that if there is a lot of activity then it would indicate the person is "in good shape" and there is nothing to worry about.

The parameter measured in - heart-rate variability (beat-to-beat timing fluctuations derived from the interval between two adjacent beats.)

The technique used:
Lomb prediodogram is used to derive heart-rate variability. Simple peak-detection above and below a certain threshold or waveform derivative parameters can produce the timing and amplitude features necessart for the Lomb periodogram and cross-correlation technique.

The sensor - gel-coupled sensor - has impedance properties similar to that offered by the skin, but has a significant mismatch for the airborne noises.
This technology can be used to measure: heartbeats, breaths, blood presure, motion, voice and other indicators. Other specific events - cough, gag, wheeze, and vomits can also be detect. [the author does not say it, but I feel that the sensor can help detect this events as well.]

(pg 345) Getting the resting heart-rate helps to know HRV and is a good indicator of which personnel is ready to reenter the hazard situation. The duration of elevated heart rates and the maximum rate achieved can also be an indicator of a person's ability to safely and effectively perform his/her mission (task).

data details: (remarks only on the data of concern to me)
fs=1500, anti-aliasing filter corner frequency=500Hz, 30-minutes of data.
heart beats clearly seen from the neck-sensors (left and right).

** how the IBI's fluctuate on a beat-by-beat basis, as well as long-term trends, is termed HRV - and gives an indication of how well the body is regulating blood pressure, breathing, and core temperature. These IBI's also can indicated mental activity related to concentration on a task, suh as when the IBI's become very regular due to a task with intense concentration and precision muscle control, whereas the IBI's may vary significantly for tasks with varying mental and physical distractions. --refer Mulder G and Mulder LJM, "Information Processing and Cadiovascular Control," Psychophysiology, 1981, 18, pp 392-405.

To measure blood pressure, we need to have heart-rate measurement done at two different (would be nice if these to locations are far off from each other) locations on the body causing the time lag in heart beat measurement, time lag and the distance relates to the blood pressure. delta-time between the neck and wrist acoustic pulses: a long time-delta indicates a slow wave (low systolic pressure) while short - fast wave - high systolic pressure.

** Systolic pressure can also (use this method with caution) be done from the slope of second heart-sound but is not accurate. It is also possible that breath rates can be derived from acoustic pulses at the wrist by analyzing changes in amplitude that result from the lungs over- and under-pressurizing the heart.

(pg 348) the neck acoustic data clearly shows high-amplitude heartbeat pulsations in the low-frequency (0-120Hz) region, high-amplitude harmonics of the voice structure, and medium-level braodband breath sounds in the 200- and 500-Hz region. The anti-alising filters can be seen to attenuate those sounds above 500Hz.
(pg 350) one method to monitor the personnel isto look at tshort-term energy detected at the sensors. The RMS energy from the right-neck sensor shows high levels from head turns, voice, jacket, hood, mask movements, and muscular activity from lifting or crawling.

decrease in RMS energy at all acoustic sensors will indicate a decrease in activity.

for breath-rate detection, high-passed neck data reveals a lot of braodband high-frequency energy resulting from the airflow in the throat. Using FFT to monitor the temporal fluctuations of the RMS energy produces a breath rate peak in the power spectrum results. If the data is clipped (at the level of three times the median value of the absolute value of the band-passed filtered data), the advantage is it removes the influence high-amplitude motion artifacts have on the RMS calculations.