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Algorithm and Associated Integration Hardware for Capturing Context-sensitive Metadata for Health Risk Assessments


RT&L FOCUS AREA(S): General Warfighting Requirements (GWR)


OBJECTIVE: Demonstrate technology for automatic association of environmental conditions and activities with chemical and physical exposures based on feedback from body worn and area monitors to augment health risk assessments.

DESCRIPTION: The collection of context-sensitive metadata during health risk assessments is critical to understand the circumstances that are associated with exposures and health outcomes. In a traditional occupational environment, an industrial hygienist or technician manually observes and logs events and work activities that are associated with exposure levels of concern. By automating the identification of activity and environmental conditions using feedback from body worn and area sensor systems and leveraging the internet-of things, the industrial hygienist can more readily provide specific feedback to workers to mitigate potentially hazardous exposure conditions. Further, context-sensitive data can be used to augment existing DoD environmental and biomonitoring programs, such as the Joint Health Risk Management (JHRM) program and the Army’s Health Readiness and Performance System (HRAPS). Activity data of interest include specific information about operational tasks, including operation of specific machinery in a maintenance shop, or various actions associated with flight line maintenance, such as pre-flight checks and refueling actions. Environmental data of interest include information such as indoors versus outdoors, local ventilation conditions and weather. Chemical and physical exposures of interest include particulate matter, total volatile organic compounds, ozone, carbon monoxide, carbon dioxide, nitrogen oxides, noise, and heat/cold stress. The algorithm and integration hardware should be designed to incorporate data from commercial off-the-shelf sensors, such as MultiRAE gas monitors (present at most military bases), standard noise dosimeters, weather monitors, smart wearable technologies (e.g. smart watches, smartphones), as well as next generation sensor technologies currently in development. The final algorithm and associated integration hardware must store logged data locally, incorporate a user-interface, and operate for at least 10 hours on battery power. The device will also incorporate user-configurable alarm settings and an option for the user to provide feedback to the device regarding notable activities. The data should be exportable in formats compatible with DoD environmental and biomonitoring programs.

PHASE I: During the phase I effort, a prototype system will be developed to demonstrate the technical feasibility for an algorithm and interface for context-sensitive environmental monitoring. The algorithm and associated integration hardware will be demonstrated for its ability to automatically identify maintenance-related tasks, such as painting, stripping, and sanding, completed in a controlled environment (e.g. laboratory or shop), as well as simple environmental conditions, such as indoors versus outdoors and location. An interface will be designed where the worker being monitored can provide feedback to train the algorithm and contextual information can be provided back to worker.

PHASE II: During the phase II effort, a robust system will be demonstrated that is capable of automatically and accurately identifying specific work tasks, such as welding, drilling, sanding, stripping, and painting, as well as basic environmental conditions, in a military field environment. The government will provide parameters for metadata needed. The 711th Human Performance Wing will test the prototype independently during this effort and provide feedback back to the small business in order to accelerate the development of a product that is practical to transition to an operational environment.

PHASE III DUAL USE APPLICATIONS: The context-sensitive sensor system should demonstrate connectivity with DoD programs, such as the Army’s Health Readiness and Performance System (HRAPS), Joint Health Risk Management (JHRM) program, and other comparable systems. In addition to providing value to the DoD, context-sensitive technology capable of automatically associating environmental conditions and activities with chemical and physical exposures would be valuable to industrial hygienists working in construction, manufacturing, and maintenance industries where workplace exposures require consistent monitoring to ensure health and safety of workers. The final product will be relevant for research applications where activities and locations linked with exposure levels could be associated with epigenetic markers or chronic health outcomes, such as noise-induced hearing loss, heart disease, and cancer.


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  3. Yang L, Li W, Ghandehari M, Fortino G. 2018. People-centric cognitive internet of things for the quantitative analysis of environmental exposure. IEEE Internet of Things Journal 5(4): 2353-2366.
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