SBIR-STTR Award

The objective of this Phase I application is the focused development of an advanced bioinformatic based wearable critical care monitor to enhance warfighter medical care. This will be accomplished by building upon an existing advanced sensor, hardware and software platform developed by BodyMedia Advanced Development combined with additional computational and clinical expertise provided by Virginia Commonwealth University (VCU). The effort will utilize advanced computational techniques allowing for data fusion of easily obtained low level signals to derive meaningful physiologic outputs. In this application we will harness our current capability to record galvanic skin response, temperature, heat flux, and heart rate to produce usable values of blood pressure, pulse pressure, oxygen consumption, oxygen debt, and heart rate related changes in volume loss. Physiologic data for the initial level of computational development will be obtained through an existing relationship between VCU and the US Army Institute of Surgical Research using models of lower body negative pressure. The device will provide contextual information such as level of activity to the analysis of physiologic signals thus making their interpretation more useful. Such a device should enable remote triage as well assisting in the point of care diagnosis and treatment of the wounded warfighter.

Keywords:
Warfighter Physiologic Status Monitoring, Remote Triage, Bioinformatics, Hemorrhagic Shock, Supervised Machine Learning, Wavelet Transformation, Trauma, Noninvasive

There is a well-understood need for a small bioinformatics based wearable critical care monitor allowing for the remote monitoring and triage of wounded warfighters. Our Phase I research demonstrated that by augmenting a commercial wearable body monitoring device (BodyMedia’s SenseWear Armband) with ECG and sophisticated signal processing including the wavelet transform invented at Virginia Commonwealth University, the hemorrhagic shock status of a wounded warfighter was accurately detectable in a simulated shock setting. Algorithms were also developed to predict useful critical parameters such as pulse pressure, shock index, stroke volume, and blood pressure from the armband signals. Subsequent research suggests that adding arm impedance to the existing set of sensors (ECG, heat flux, galvanic skin response, skin temperature, ambient temperature, and motion-based-parameters) further improves the accuracy of the system. Our Phase II proposal is to incorporate all of these sensors into a commercially ready wearable platform with additional memory and networking capabilities and to further improve and validate the prediction algorithms using existing collaborative efforts with the US Army Institute of Surgical Research (USAISR) as well as on critically ill and injured trauma and surgical patients at a busy urban Level I Trauma Center (VCU Medical Center).

Keywords:
Critical Care, Remote Triage, Warfighter, Combat Casualty, Hemorrhagic Shock, Machine Learning, Bioi