Currently, understanding and assessing the readiness of the warfighter involves medical intervention with the help of advanced equipment, such as electrocardiographs (EKGs) and other specialized medical devices, that are too expensive and cumbersome to employ continuously or without supervision in non-controlled environments. On the other hand, currently 92 percent of adults in the United States own a cell phone, which could be used as the basis for continuous, passive health, and readiness assessment.
The Warfighter Analytics using Smartphones for Health (WASH) program seeks to use data collected from cellphone sensors to enable novel algorithms that conduct passive, continuous, real-time assessment of the warfighter. The objective of WASH is to extract physiological signals, which may be weak and noisy, that are embedded in the data obtained through existing mobile device sensors (e.g., accelerometer, screen, microphone). Such extraction and analysis, done on a continuous basis, may help determine current health status and identify latent or developing health disorders (Figure 1).
Figure 1: WASH health determination
WASH research will explore the development of algorithms and techniques for identifying both known indicators of physiological problems (such as disease, illness, and/or injury) and deviations from the warfighter’s micro-behaviors that could indicate such problems. It is also expected that additional “digital biomarkers” of physiological problems may be identified during the research through the combination of big data analytics and medical ground truth provided to performers. Digital biomarkers are consumer-generated physiological and behavioral measures collected through connected digital tools, in this case a smartphone.
A prerequisite for the extraction and interpretation of the raw sensor data and any identified digital biomarkers is determining the context of such data collection and analysis, which may affect the relevance of any given sensor and permit “denoising,” or elimination of irrelevant or misleading readings. For example, relying on cellphone accelerometer data while the warfighter is in a moving vehicle would likely negatively influence the utility of such data for WASH-type analysis unless the auxiliary motion is identified and cancelled. Thus, key focus areas will be the extraction of the signal context and the identification of complicated actions and environmental variables, and the association of user state with symptoms of illness conditions in order to identify potential illnesses and conditions before conventional symptomatic display. It is the union of personal behavior/characteristics, smartphone sensor collection, context of smartphone use, and disease biomarkers that will define the preclinical health determination of the WASH program (Figure 2).
Figure 2: WASH program concept
The program goal is to enable the creation of a mobile application that passively assesses a warfighter’s readiness immediately and over time. This application seeks to provide:
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