Early Prediction of Impending Exertional Heat Stroke With Wearable Multimodal Sensing and Anomaly Detection.

We employed wearable multimodal sensing (heart rate and triaxial accelerometry) with machine learning to enable early prediction of impending exertional heat stroke (EHS). US Army Rangers and Combat Engineers (N = 2,102) were instrumented while participating in rigorous 7-mile and 12-mile loaded rucksack timed marches. There were three EHS cases, and data from 478 Rangers were analyzed for model building and controls. The data-driven machine learning approach incorporated estimates of physiological strain (heart rate) and physical stress (estimated metabolic rate) trajectories, followed by reconstruction to obtain compressed representations which then fed into anomaly detection for EHS prediction. Impending EHS was predicted from 33 to 69 min before collapse. These findings demonstrate that low dimensional physiological stress to strain patterns with machine learning anomaly detection enables early prediction of impending EHS which will allow interventions that minimize or avoid pathophysiological sequelae. We describe how our approach can be expanded to other physical activities and enhanced with novel sensors.

Passive underwater acoustic identification tags using multi-layered shells.

The development of pre-deployed underwater infrastructures to aid in autonomous underwater vehicle (AUV) navigation is of keen interest, with the increased use of AUVs for undersea operations. Previous literature has introduced a class of passive underwater acoustic markers, termed acoustic identification (AID) tags [Satish, Trivett, and Sabra, J. Acoust. Soc. Am. 147(6), EL517-EL522 (2020)], which are inexpensive to construct, simple to deploy, and reflect unique engineered acoustic signatures that can be detected by an AUV instrumented with high-frequency sonar systems. An AID tag is built of multi-layer shells with different acoustic properties and thicknesses to generate a unique acoustic signature, composed of the multiple reflections created by the layer interfaces, thus akin to an "acoustic barcode." AID tags can be used as geospatial markers to highlight checkpoints in AUV trajectories or mark areas of interest underwater. This article investigates the optimization of the AID tag's design using energy based metrics and evaluates the detectability of an AID tag in the presence of interfering signals, such as clutter using matched-filter based techniques. Furthermore, experimental results of AID tags interrogated by a standard high-frequency sonar are presented to provide proof of concept of AID tag detection in a reverberant water tank.

Omnidirectional passive acoustic identification tags for underwater navigation.

A class of passive acoustic identification (AID) tags with curved symmetry for underwater navigation is presented. These AID tags are composed of radially stratified shells designed to backscatter a unique specular reflection pattern independent of the incidence orientation in a monostatic configuration, thus acting as acoustic bar-codes. The AID tag's response can be uniquely engineered by selecting the thicknesses and material properties of the individual constitutive shells. Furthermore, in the high-frequency regime, the specular component of the AID tag's response can be simply predicted numerically assuming horizontally stratified layers. This approach is demonstrated using scaled experiments with an AID tag constructed from 3D printed hemispherical shells.

Passive underwater acoustic tags using layered media.

Autonomous Underwater Vehicle (AUV) navigation requires accurate positioning information from the environment. Existing underwater navigation paradigms employ active acoustic transponders that assist in this task, but these more complex and costly systems require maintenance and power. This paper presents instead a passive underwater marker made of different horizontally stacked acoustically reflective materials that is cost effective and relatively simple to service. A marker's characteristic acoustic signature can be detected by AUVs as acoustic backscattering upon tag insonification, and hence be used for navigation purposes.