Machine learning prediction of combat basic training injury from 3D body shape images

Mobile phone applications for 3-dimensional scanning and digital anthropometry: a precision comparison with traditional scanners

BACKGROUND

The precision of digital anthropometry through 3-dimensional (3D) scanning has been established for relatively large, expensive, non-portable systems. The comparative performance of modern mobile applications is unclear.

SUBJECTS/METHODS

Forty-six adults (age: 23.3 ± 5.3 y; BMI: 24.4 ± 4.1 kg/m2) were assessed in duplicate using: (1) a mobile phone application capturing two individual 2D images, (2) a mobile phone application capturing serial images collected during a subject's complete rotation, (3) a traditional scanner with a time of flight infrared sensor collecting visual data from a subject being rotated on a mechanical turntable, and (4) a commercial measuring booth with structured light technology using 20 infrared depth sensors positioned in the booth. The absolute and relative technical error of measurement (TEM) and intraclass correlation coefficient (ICC) for each method were established.

RESULTS

Averaged across circumferences, the absolute TEM, relative TEM, and ICC were (1) 0.9 cm, 1.5%, and 0.975; (2) 0.5 cm, 0.9%, and 0.986; (3) 0.8 cm, 1.5%, and 0.974; and (4) 0.6 cm, 1.1%, and 0.985. For total body volume, these values were (1) 2.2 L, 3.0%, and 0.978; (2) 0.8 L, 1.1%, and 0.997; (3) 0.7 L, 0.9%, and 0.998; and (4) 0.8 L, 1.1%, and 0.996, with segmental volumes demonstrating higher relative errors.

CONCLUSION

A 3D scanning mobile phone application involving full rotation of subjects in front of a smartphone camera exhibited similar reliability to larger, less portable, more expensive 3D scanners. In contrast, larger errors were observed for a mobile scanning application utilizing two 2D images, although the technical errors were acceptable for some applications.

Raising the U.S. Army Height-Weight (Body Mass Index) Standards: Quantifying Metabolic Risk

BACKGROUND & OBJECTIVES

The U.S. Army fell 25% short of its recruitment goal in 2022 and therefore, increasing the eligibility pool for potential recruits is of interest. Raising the body mass index (BMI) standards for eligibility presents a path to increase the recruitable population; however, there may be additional costs incurred due to attendant health risks that may be present in individuals with higher BMI.

METHODS

We filtered the 2017-2020 National Health and Nutrition Examination Survey by age (17-25 years) and BMI (up to 30 kg/m2). A k-means cluster analysis was performed on the filtered dataset for the variables used to determine metabolic syndrome. Metabolic syndrome Clusters were characterized through summary statistics and compared over clinical measurements and questionnaire responses.

RESULTS

Five distinct clusters were identified and mean BMI in two clusters (Clusters1 and 3) exceeded the current U.S. Army BMI thresholds. Of these two clusters, Cluster 1 members had metabolic syndrome. Cluster 3 members were at higher risk for metabolic syndrome compared to members of Clusters 2, 4, and 5. Mean waist circumference was slightly lower in Cluster 3 compared to Cluster 1. None of the clusters had significant differences in depression scores, poverty index, or frequency of dental visits.

CONCLUSIONS

Potential recruits from Cluster 1 have excessive health risk and may incur substantial cost to the U.S. Army if enlisted. However, potential recruits from Cluster 3 appear to add little risk and offer an opportunity to increase the pool for recruiting.

Translating digital anthropometry measurements obtained from different 3D body image scanners

BACKGROUND

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.

Military Body Composition Standards and Physical Performance: Historical Perspectives and Future Directions

ABSTRACT

Harty, PS, Friedl, KE, Nindl, BC, Harry, JR, Vellers, HL, and Tinsley, GM. Military body composition standards and physical performance: historical perspectives and future directions. J Strength Cond Res 36(12): 3551-3561, 2022-US military physique and body composition standards have been formally used for more than 100 years. These metrics promote appropriate physical fitness, trim appearance, and long-term health habits in soldiers, although many specific aspects of these standards have evolved as evidence-based changes have emerged. Body composition variables have been shown to be related to many physical performance outcomes including aerobic capacity, muscular endurance, strength and power production, and specialized occupational tasks involving heavy lifting and load carriage. Although all these attributes are relevant, individuals seeking to improve military performance should consider emphasizing strength, hypertrophy, and power production as primary training goals, as these traits appear vital to success in the new Army Combat Fitness Test introduced in 2020. This fundamental change in physical training may require an adjustment in body composition standards and methods of measurement as physique changes in modern male and female soldiers. Current research in the field of digital anthropometry (i.e., 3-D body scanning) has the potential to dramatically improve performance prediction algorithms and potentially could be used to inform training interventions. Similarly, height-adjusted body composition metrics such as fat-free mass index might serve to identify normal weight personnel with inadequate muscle mass, allowing for effective targeted nutritional and training interventions. This review provides an overview of the origin and evolution of current US military body composition standards in relation to military physical readiness, summarizes current evidence relating body composition parameters to aspects of physical performance, and discusses issues relevant to the emerging modern male and female warrior.

Military Applications of Machine Learning: A Bibliometric Perspective

The military environment generates a large amount of data of great importance, which makes necessary the use of machine learning for its processing. Its ability to learn and predict possible scenarios by analyzing the huge volume of information generated provides automatic learning and decision support. This paper aims to present a model of a machine learning architecture applied to a military organization, carried out and supported by a bibliometric study applied to an architecture model of a nonmilitary organization. For this purpose, a bibliometric analysis up to the year 2021 was carried out, making a strategic diagram and interpreting the results. The information used has been extracted from one of the main databases widely accepted by the scientific community, ISI WoS. No direct military sources were used. This work is divided into five parts: the study of previous research related to machine learning in the military world; the explanation of our research methodology using the SciMat, Excel and VosViewer tools; the use of this methodology based on data mining, preprocessing, cluster normalization, a strategic diagram and the analysis of its results to investigate machine learning in the military context; based on these results, a conceptual architecture of the practical use of ML in the military context is drawn up; and, finally, we present the conclusions, where we will see the most important areas and the latest advances in machine learning applied, in this case, to a military environment, to analyze a large set of data, providing utility, machine learning and decision support.

What Is a 2021 Reference Body?

The historical 1975 Reference Man is a ‘model’ that had been used as a basis for the calculation of radiation doses, metabolism, pharmacokinetics, sizes for organ transplantation and ergonomic optimizations in the industry, e.g., to plan dimensions of seats and other formats. The 1975 Reference Man was not an average individual of a population; it was based on the multiple characteristics of body compositions that at that time were available, i.e., mainly from autopsy data. Faced with recent technological advances, new mathematical models and socio-demographic changes within populations characterized by an increase in elderly and overweight subjects a timely ‘state-of-the-art’ 2021 Reference Body are needed. To perform this, in vivo human body composition data bases in Kiel, Baton Rouge, San Francisco and Honolulu were analyzed and detailed 2021 Reference Bodies, and they were built for both sexes and two age groups (≤40 yrs and >40 yrs) at BMIs of 20, 25, 30 and 40 kg/m2. We have taken an integrative approach to address ‘structure−structure’ and ‘structure−function’ relationships at the whole-body level using in depth body composition analyses as assessed by gold standard methods, i.e., whole body Magnetic Resonance Imaging (MRI) and the 4-compartment (4C-) model (based on deuterium dilution, dual-energy X-ray absorptiometry and body densitometry). In addition, data obtained by a three-dimensional optical scanner were used to assess body shape. The future applications of the 2021 Reference Body relate to mathematical modeling to address complex metabolic processes and pharmacokinetics using a multi-level/multi-scale approach defining health within the contexts of neurohumoral and metabolic control.

The role of machine learning in the primary prevention of work-related musculoskeletal disorders: A scoping review

To determine the applications of machine learning (ML) techniques used for the primary prevention of work-related musculoskeletal disorders (WMSDs), a scoping review was conducted using seven literature databases. Of the 4,639 initial results, 130 primary research studies were deemed relevant for inclusion. Studies were reviewed and classified as a contribution to one of six steps within the primary WMSD prevention research framework by van der Beek et al. (2017). ML techniques provided the greatest contributions to the development of interventions (48 studies), followed by risk factor identification (33 studies), underlying mechanisms (29 studies), incidence of WMSDs (14 studies), evaluation of interventions (6 studies), and implementation of effective interventions (0 studies). Nearly a quarter (23.8%) of all included studies were published in 2020. These findings provide insight into the breadth of ML techniques used for primary WMSD prevention and can help identify areas for future research and development.