Novel body fat estimation using machine learning and 3-dimensional optical imaging

Harty, Patrick S.; Sieglinger, Breck A.; Heymsfield, Steven B.; Shepherd, John; Bruner, David M.; Stratton, Matthew T.; Tinsley, Grant M.

doi:10.1038/s41430-020-0603-x

Cited by 34 publications

(33 citation statements)

References 10 publications

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“…The reference method used in these studies for BFP estimation was DXA. Recently, Harty et al (2020) used a rearrangement of the four-component model of Wang et al (2002) as a reference to propose a new formula for predicting BFP from a set body metrics derived from an optical scanner (body surface area, and upper arm, calf, thigh, and upper and lower abdomen circumferences) and machine learning. This new formula is presented in two branches, one for leaner subjects and another one for larger subjects, to provide a better prediction for the leaner group.…”

Section: Methodsmentioning

confidence: 99%

Anthropometric Indicators as a Tool for Diagnosis of Obesity and Other Health Risk Factors: A Literature Review

et al. 2021

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Obesity is characterized by the accumulation of an excessive amount of fat mass (FM) in the adipose tissue, subcutaneous, or inside certain organs. The risk does not lie so much in the amount of fat accumulated as in its distribution. Abdominal obesity (central or visceral) is an important risk factor for cardiovascular diseases, diabetes, and cancer, having an important role in the so-called metabolic syndrome. Therefore, it is necessary to prevent, detect, and appropriately treat obesity. The diagnosis is based on anthropometric indices that have been associated with adiposity and its distribution. Indices themselves, or a combination of some of them, conform to a big picture with different values to establish risk. Anthropometric indices can be used for risk identification, intervention, or impact evaluation on nutritional status or health; therefore, they will be called anthropometric health indicators (AHIs). We have found 17 AHIs that can be obtained or estimated from 3D human shapes, being a noninvasive alternative compared to X-ray-based systems, and more accessible than high-cost equipment. A literature review has been conducted to analyze the following information for each indicator: definition; main calculation or obtaining methods used; health aspects associated with the indicator (among others, obesity, metabolic syndrome, or diabetes); criteria to classify the population by means of percentiles or cutoff points, and based on variables such as sex, age, ethnicity, or geographic area, and limitations.

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Section: Methodsmentioning

confidence: 99%

Anthropometric Indicators as a Tool for Diagnosis of Obesity and Other Health Risk Factors: A Literature Review

et al. 2021

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“…Body size and shape information provides valuable insights into a wide range of topics related to human obesity. [1][2][3][4][5] Anthropometric measurements, such as circumferences that define body size and shape, are inexpensive and safely acquired for evaluating the health and nutritional status of patients with overweight and obesity across the full lifespan. 2,3 The use of these estimates, applied worldwide in highly varied settings, are advocated by numerous scientific organizations and health-related associations as a means of weight trajectories, gauging the risk of developing chronic diseases, and many other topics of clinical and research interest.…”

Section: Introductionmentioning

confidence: 99%

“…An important application of conventional anthropometry (CA) is to quantify and monitor somatic features, such as adiposity level and adipose tissue distribution, as part of multicenter trials and survey protocols. 1,[6][7][8] An ambitious goal would be to create large cloudbased anthropometric databases by pooling the information collected in these studies and using them for numerous clinical and investigative purposes. Although building these global databases is a laudable objective, several roadblocks now limit the practicality of this approach.…”

Section: Introductionmentioning

confidence: 99%

“…Body size and shape information provides valuable insights into a wide range of topics related to human obesity 1–5 . Anthropometric measurements, such as circumferences that define body size and shape, are inexpensive and safely acquired for evaluating the health and nutritional status of patients with overweight and obesity across the full lifespan 2,3 .…”

Section: Introductionmentioning

confidence: 99%

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Digital anthropometry for body circumference measurements: Toward the development of universal three‐dimensional optical system analysis software

Sobhiyeh

Kennedy

Dunkel

et al. 2020

Obesity Science & Practice

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Background/Objective: Digital anthropometric (DA) assessments are increasingly being administered with three-dimensional (3D) optical devices in clinical settings that manage patients with obesity and related metabolic disorders. However, anatomic measurement sites are not standardized across manufacturers, precluding use of published reference values and pooling of data across research centers. Subjects/Methods: This study aimed to develop universal 3D analysis software by applying novel programming strategies capable of producing device-independent DA estimates that agree with conventional anthropometric (CA) measurements made at well-defined anatomic sites. A series of technical issues related to proprietary methods of 3D geometrical reconstruction and image analysis were addressed in developing major software components. To evaluate software accuracy, comparisons were made to CA circumference measurements made with a flexible tape at eleven standard anatomic sites in up to 35 adults scanned with three different commercial 3D optical devices. Results: Overall, group mean CA and DA values across the three systems were in good agreement, with ∼2 cm systematic differences; CA and DA estimates were highly correlated (all p-values <0.01); root-mean square errors were low (0.51-3.27 cm); and CA-DA bias tended to be small, but significant depending on anatomic site and device. Conclusions: Availability of this software, with future refinements, has the potential to facilitate clinical applications and creation of large pooled uniform anthropometric databases.

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“…SportRχiv preprint, doi: 10.31236/osf.io/w734a ing (done infrequently) vs monitoring (done often) variables used by some S&C coaches [667]. Some of the measures such as blood sampling and dual-energy X-ray absorptiometry (DXA) currently seen as testing measures might become monitoring measures with the introduction of lowcost and accurate future alternatives [252,303,333,456,462,633,814]. Some functional test measurements such as isometric mid-thigh pull (IMTP), might be replaced with something less physically demanding to make their use more frequent as a monitoring measure.…”

Section: Precision Strength Trainingmentioning

confidence: 99%

Precision strength training: Data-driven artificial intelligence approach to strength and conditioning

Teikari¹,

Pietrusz²

2021

Preprint

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In strength training, personalised strength training (autoregulation) approaches have been used to individualise exercise programs with monitoring an for dynamic adjustment based on their responses to training. While this transition from tradition-based training to evidence-based training framework has been an improvement in training practices, we argue that the future of strength training will also incorporate deep learning models powered by data. We refer to this data-driven framework as precision strength training inspired by the similar modeling frameworks used in precision medicine. In contrast to current personalised training in which the acquired athlete data is often subject to human expert decision-making, we are anticipating the rise of human-in-the-loop systems with an augmented coach who will be doing decisions collaboratively with the machine. Similar to other precision frameworks, such as precision health, we envision such a future to take decades to be realised and we focus here on practical short-term targets on a way to long-term realisation. In this chapter, we will review the measurement technology needed for continuous data acquisition from an individual during training/physical activity, how to acquire these datasets for the development of such systems and, how a proof-of-concept system could be developed for powerlifting training with applicability to general strength and conditioning (S&C) and physical rehabilitation purposes. Additionally, we will evaluate how the user experience (UX) of the system feedback and visualisation could be designed.

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Novel body fat estimation using machine learning and 3-dimensional optical imaging

Cited by 34 publications

References 10 publications

Anthropometric Indicators as a Tool for Diagnosis of Obesity and Other Health Risk Factors: A Literature Review

Anthropometric Indicators as a Tool for Diagnosis of Obesity and Other Health Risk Factors: A Literature Review

Digital anthropometry for body circumference measurements: Toward the development of universal three‐dimensional optical system analysis software

Precision strength training: Data-driven artificial intelligence approach to strength and conditioning

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