B ecause stress is a leading cause of illness and disease and is so pervasive, there is an inherent need to be able to monitor stress in real time over extended periods. A real-time personal stress monitor would benefit individuals by providing continuous feedback about their stress levels and by helping their physicians to objectively evaluate stress exposure between visits. We are developing personal health monitors based on a wireless body area network (BAN) of intelligent sensors [1]. Individual monitors will be integrated into a distributed wireless system for synchronized monitoring of a group of subjects. This system could be used during the selection process and as part of a psychophysiological evaluation of military members undergoing intense training. We use measures of heart-rate variability (HRV) to quantify stress level prior to and during training as well as to predict stress resistance. This task requires reliable, high-precision instrumentation and synchronized measurements from a group of individuals over prolonged periods (days of training). Our preliminary results indicate that individuals who have better stress tolerance also exhibit significantly different patterns of HRV, both before and during stress exposure. These baseline differences in HRV are predictive of actual military and cognitive neuropsychological test performance scores assessed during and after stress exposure [1], [2]. During our preliminary investigations, we used a stressful component of aviation water survival training, the 9D5 Multi-place Underwater Egress Trainer, as our event for the whole group. The 9D5 is a reasonably realistic representation of a helicopter conducting an emergency landing, turning upside down, and sinking. Trainees report the 9D5 session as the most stressful training event during water survival training.

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Medics and first responders to emergencies are often faced with monitoring and assessing victims with very limited resources. Therefore, there is an inherent need for a real-time ambulatory monitoring capability that is portable and low power. This is particularly important for physiological monitoring of life-threatening conditions such as internal hemorrhaging. We propose the use of photoplethysmogram (PPG) morphology as an indicator of hypovolemic states and study its correlation with blood pressure. In this paper, we compared the PPG morphology with pulse transit time (PTT), which has been investigated for clinical and ambulatory applications. The indicators were tested on data obtained from experiments using lower body negative pressure (LBNP) as a model to simulate hemorrhage in humans. The results of this study indicate that PPG morphology is associated with pulse pressure (systolic minus diastolic blood pressure) and is therefore a promising feature for detection and real-time tracking of hypovolemic states.

The purpose of this study was to test the hypotheses that digital infrared thermographic imaging (DITI) during simulated uncontrolled hemorrhage will reveal 1) respiratory rate and 2) changes of skin temperature that track reductions of stroke volume. In 45 healthy volunteers (25 men and 20 women), we recorded the ECG, finger photoplethysmographic arterial pressure, respiratory rate (pneumobelt and DITI of the nose), cardiac output (inert rebreathing), and skin temperature of the forehead during lower body negative pressure (LBNP) at three continuous decompression rates; slow (-3 mmHg/min), medium (-6 mmHg/min), and fast (-12 mmHg/min) to an ending pressure of -60 mmHg. Respiratory rates calculated from the pneumobelt (14.7 ± 0.9 breaths/min) and DITI (14.9 ± 1.2 breaths/min) were not different (P = 0.21). LBNP induced an average stroke volume reduction of 1.3 ml/mmHg regardless of decompression speed. Maximal reductions of stroke volume and forehead temperature were -100 ± 12 ml and -0.32 ± 0.12°C (slow), -86 ± 12 ml and -0.74 ± 0.27°C (medium), and -78 ± 5 ml and -0.17 ± 0.02°C (fast). Changes of forehead temperature as a function of changes of stroke volume were best described by a quadratic fit to the data (slow R(2) = 0.95; medium R(2) = 0.89; and fast R(2) = 0.99).Our results suggest that a thermographic camera may prove useful for the remote assessment of traumatically injured patients. Life sign detection may be determined by verifying respiratory rate. Determining the magnitude and rate of hemorrhage may also be possible based on future algorithms derived from associations between skin temperature and stroke volume.