DAHLGREN, Va. – As Midshipman Natalie LaPlaca works on her U.S. Naval Academy senior capstone project, she recalls her internship and real world data analysis at Naval Surface Warfare Center Dahlgren Division (NSWCDD).
In fact, LaPlaca’s work on human sensors as an NSWCDD intern is the focus of her capstone project, titled, “Correlating Sleep and Temperature Patterns in Navy Warfighters with Current and Future Health Status.”
“The real world data analysis I got to participate in at Dahlgren was invaluable and something that is not found in the classroom,” said LaPlaca. “I learned so many things about the data analysis process and coding in R [for statistical computing] that is serving me well for this project.”
During her 2019 summer internship at the NSWCDD Concepts and Experimentations Branch, LaPlaca researched big data analytics from wearable sensors that models warfighter readiness based on features extracted from physiological endpoints.
“The experience that Midshipman LaPlaca gained during her internship, in this rapidly growing field, will continue into her senior year,” said NSWCDD engineer Laura Maple who mentored the Midshipman. “She will finalize her software code developed at NSWC Dahlgren and apply her lessons learned to a dataset collected at the academy.”
LaPlaca’s senior capstone project includes the working knowledge in scripting code along with the software development she learned while interning at Dahlgren.
Meanwhile, the Midshipman is preparing and looking forward to her service as a submariner upon graduation from the Naval Academy in May 2020.
“While I may not be using statistical computing programs like R on a submarine, the experience I’ve gained while interning at Dahlgren with analyzing data will carry over into the submarine force,” said LaPlaca, who plays on the U.S. Naval Academy volleyball team. “Being able to practice receiving and dissecting new information and coming up with a course of action is something that will make me a better officer in the fleet.”
The field of machine learning – which includes human sensors – is rapidly growing with many potential applications for the Navy. According to a Feb. 18, 2018 Forbes article, “Roundup Of Machine Learning Forecasts And Market Estimates, 2018” – the International Data Corporation forecasts that spending on machine learning will grow from $12 billion in 2017 to $57.6 billion in 2021.
“The USNA internship program can be priceless to a midshipman,” said Maple. “Since there is so much focus on academics with high volume schedules, there is limited time to apply working knowledge during the academic year.”
“My experience at Dahlgren has truly been great,” added LaPlaca, an Owings, Maryland, native who earned U.S. Naval Academy academic honors in six semesters and was named to the 2017 and 2018 Patriot League academic honor rolls. “I would definitely recommend this internship to another midshipman, especially one whose major is in the mathematics field.”
Upon her 2020 graduation, LaPlaca will attend nuclear power school in Charleston, South Carolina, for about two years before reporting to her first submarine.
“I chose this path because the people I have met in the submarine force, both officers and enlisted, are so competent at what they do and I aspire to be like them in my future,” she said. “They left quite an impression on me, and it is an honor to follow in their footsteps and participate in arguably one of the Navy’s most important missions.”
Abstract: CORRELATING SLEEP AND TEMPERATURE PATTERNS IN NAVY WARFIGHTERS WITH CURRENT AND FUTURE HEALTH STATUS
By L. Maple, D. Marchette, B. Gutting, S. Anderson, R. Strand
Naval Surface Warfare Center, Dahlgren Division
Physiological endpoints, such as sleep patterns, heart rates, respiration rate, and body temperature are important indicators of overall health status and are often disrupted when the body is not in homeostasis. Such disruptions are common with bacterial infections (anthrax, plague, tularemia, etc.), viral infections (Ebola, rift valley fever, Venezuelan Equine Encephalitis Virus, etc.), exposure to toxins (ricin, botulinum, Staphylococcal enterotoxin B, etc.), and high stress levels such as those encountered during routine military operations and/or deployments. Monitoring these baseline physiological parameters in real time could represent a useful method to assess current and future health status. To this end, this project’s ultimate aims are to develop an early warning system that monitors physiological endpoints using state-of-the-art commercial off-the-shelf (COTS) biomonitoring devices and correlates that data with actual health status and medical readiness. To date, numerous COTS devices have been evaluated using several cohorts based on several operational criteria: PII security, performance, robustness, data security controls, and reliability in monitoring and recording physiological parameters of interest. In addition, statistical algorithms have been created using analytical software and machine learning techniques to analyze subject time-series data. The algorithms monitor sleep, heart rate from inter-beat-intervals, and diurnal patterns, providing working baseline data for classification.