USMA Digital Commons (United States Military Academy, West Point)
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Walter Reed: How Iraq War Veterans and Their Families Commanded the Ear of Congress and Demanded Better Health Care from the U.S Department of Veterans Affairs
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Translating digital anthropometry measurements obtained from different 3D body image scanners
No full textBackground
Body image scanners are used in industry and research to reliably provide a wealth of anthropometric measurements within seconds. The demonstrated utility of the scanners drives the current proliferation of more commercially available devices that rely on their own reference body sites and proprietary algorithms to output anthropometric measurements. Since each scanner relies on its own algorithms, measurements obtained from different scanners cannot directly be combined or compared. Objectives
To develop mathematical models that translate anthropometric measurements between the three popular commercially available scanners. Methods
A unique database that contained 3D scanner measurements in the same individuals from three different scanners (Styku, Human Solutions, and Fit3D) was used to develop linear regression models that translate anthropometric measurements between each scanner. A limits of agreement analysis was performed between Fit3D and Styku against Human Solutions measurements and the coefficient of determination, bias, and 95% confidence interval were calculated. The models were then applied to normalized scanner data from four different studies to compare the results of a k-means cluster analysis between studies. A scree plot was used to determine the optimal number of clusters derived from each study. Results
Correlations ranged between R2 = 0.63 (Styku and Human Solutions mid-thigh circumference) to R2 = 0.97 (Human Solutions and Fit3D neck circumference). In general, Fit3D had better agreement with Human Solutions compared to Styku. The widest disagreement was found in chest circumference (Fit3D (bias = 2.30, 95% CI = [−3.83, 8.43]) and Styku (bias = −5.60, 95% CI = [−10.98, −0.22]). The optimal number of body shape clusters in each of the four studies was consistently 5. Conclusions
The newly developed models that translate measurements between the scanners Styku and Fit3D to predict Human Solutions measurements make it possible to standardize data between scanners allowing for data pooling and comparison
David\u27s Warriors: The Influence of Biblical Mythology on the Development of the Israeli Defense Forces, 1948-1973
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An Analytic Framework for Assessing Artificial Intelligence and Assistive Automation Enabled Command and Control Decision Aids for Mission Effectiveness
No full textThe U.S. Army has significant interest in operationalizing Artificial Intelligence and Assistive Automation (AI/AA) technologies on the battlefield to help collate, classify, and clarify multiple streams of situational and sensor data to provide a Commander with a clear, accurate operating picture to enable rapid and appropriate decision-making. This paper offers a methodology integrated with combat simulation output data into an analytic assessment framework. This framework helps assess AI/AA enabled Decision Aids for command and control with respect to mission effectiveness. Our methodology is demonstrated via a real-world operational vignette of an AI/AA-augmented Battalion assigned to clearing a sector of the battlefield. Results indicate that the simulated scenario with an AI/AA advantage modeled led to a higher expected mission effectiveness score
