Early adverse physiological event detection using commercial wearables: challenges and opportunities.

Data from commercial off-the-shelf (COTS) wearables leveraged with machine learning algorithms provide an unprecedented potential for the early detection of adverse physiological events. However, several challenges inhibit this potential, including (1) heterogeneity among and within participants that make scaling detection algorithms to a general population less precise, (2) confounders that lead to incorrect assumptions regarding a participant's healthy state, (3) noise in the data at the sensor level that limits the sensitivity of detection algorithms, and (4) imprecision in self-reported labels that misrepresent the true data values associated with a given physiological event. The goal of this study was two-fold: (1) to characterize the performance of such algorithms in the presence of these challenges and provide insights to researchers on limitations and opportunities, and (2) to subsequently devise algorithms to address each challenge and offer insights on future opportunities for advancement.

Prospective measurements of hearing threshold during military rifle training with in-ear, protected, noise exposure monitoring.

Although a causal relationship exists between military occupational noise exposure and hearing loss, researchers have struggled to identify and/or characterize specific operational noise exposures that produce measurable changes in hearing function shortly following an exposure. Growing evidence suggests that current standards for noise-exposure limits are not good predictors of true hearing damage. In this study, the aim was to capture the dose-response relationship during military rifle training exercises for noise exposure and hearing threshold.

Detecting Pathogen Exposure During the Non-symptomatic Incubation Period Using Physiological Data: Proof of Concept in Non-human Primates.

Early warning of bacterial and viral infection, prior to the development of overt clinical symptoms, allows not only for improved patient care and outcomes but also enables faster implementation of public health measures (patient isolation and contact tracing). Our primary objectives in this effort are 3-fold. , we seek to determine the upper limits of early warning detection through physiological measurements.

In-ear and on-body measurements of impulse-noise exposure.

Accurate quantification of noise exposure in military environments is challenging due to movement of listeners and noise sources, spectral and temporal noise characteristics, and varied use of hearing protection. This study evaluates a wearable recording device designed to measure on-body and in-ear noise exposure, specifically in an environment with significant impulse noise resulting from firearms. A commercial audio recorder was augmented to obtain simultaneous measurements inside the ear canal behind an integrated hearing protector, and near the outer ear.

Noise dosimetry for tactical environments.

Noise exposure and the subsequent hearing loss are well documented aspects of military life. Numerous studies have indicated high rates of noise-induced hearing injury (NIHI) in active-duty service men and women, and recent statistics from the U.S.

Development of anatomically realistic numerical breast phantoms with accurate dielectric properties for modeling microwave interactions with the human breast.

Computational electromagnetics models of microwave interactions with the human breast serve as an invaluable tool for exploring the feasibility of new technologies and improving design concepts related to microwave breast cancer detection and treatment. In this paper, we report the development of a collection of anatomically realistic 3-D numerical breast phantoms of varying shape, size, and radiographic density which can readily be used in finite-difference time-domain computational electromagnetics models. The phantoms are derived from T1-weighted MRIs of prone patients.

Breast tumor characterization based on ultrawideband microwave backscatter.

Characterization of architectural tissue features such as the shape, margin, and size of a suspicious lesion is commonly performed in conjunction with medical imaging to provide clues about the nature of an abnormality. In this paper, we numerically investigate the feasibility of using multichannel microwave backscatter in the 1-11 GHz band to classify the salient features of a dielectric target. We consider targets with three shape characteristics: smooth, microlobulated, and spiculated; and four size categories ranging from 0.

Ultrawideband microwave breast cancer detection: a detection-theoretic approach using the generalized likelihood ratio test.

Microwave imaging has been suggested as a promising modality for early-stage breast cancer detection. In this paper, we propose a statistical microwave imaging technique wherein a set of generalized likelihood ratio tests (GLRT) is applied to microwave backscatter data to determine the presence and location of strong scatterers such as malignant tumors in the breast. The GLRT is formulated assuming that the backscatter data is Gaussian distributed with known covariance matrix.