Three dimensional models of human thermoregulation: A review

Accurate prediction of three-dimensional humanoid avatars for anthropometric modeling

OBJECTIVE

To evaluate the hypothesis that anthropometric dimensions derived from a person's manifold-regression predicted three-dimensional (3D) humanoid avatar are accurate when compared to their actual circumference, volume, and surface area measurements acquired with a ground-truth 3D optical imaging method. Avatars predicted using this approach, if accurate with respect to anthropometric dimensions, can serve multiple purposes including patient body composition analysis and metabolic disease risk stratification in clinical settings.

METHODS

Manifold regression 3D avatar prediction equations were developed on a sample of 570 adults who completed 3D optical scans, dual-energy X-ray absorptiometry (DXA), and bioimpedance analysis (BIA) evaluations. A new prospective sample of 84 adults had ground-truth measurements of 6 body circumferences, 7 volumes, and 7 surface areas with a 20-camera 3D reference scanner. 3D humanoid avatars were generated on these participants with manifold regression including age, weight, height, DXA %fat, and BIA impedances as potential predictor variables. Ground-truth and predicted avatar anthropometric dimensions were quantified with the same software.

RESULTS

Following exploratory studies, one manifold prediction model was moved forward for presentation that included age, weight, height, and %fat as covariates. Predicted and ground-truth avatars had similar visual appearances; correlations between predicted and ground-truth anthropometric estimates were all high (R2s, 0.75-0.99; all p < 0.001) with non-significant mean differences except for arm circumferences (%Δ ~ 5%; p < 0.05). Concordance correlation coefficients ranged from 0.80-0.99 and small but significant bias (p < 0.05-0.01) was present with Bland-Altman plots in 13 of 20 total anthropometric measurements. The mean waist to hip circumference ratio predicted by manifold regression was non-significantly different from ground-truth scanner measurements.

CONCLUSIONS

3D avatars predicted from demographic, physical, and other accessible characteristics can produce body representations with accurate anthropometric dimensions without a 3D scanner. Combining manifold regression algorithms into established body composition methods such as DXA, BIA, and other accessible methods provides new research and clinical opportunities.

Accurate Prediction of Three-Dimensional Humanoid Avatars for Anthropometric Modeling

Objective

To evaluate the hypothesis that anthropometric dimensions derived from a person's manifold-regression predicted three-dimensional (3D) humanoid avatar are accurate when compared to their actual circumference, volume, and surface area measurements acquired with a ground-truth 3D optical imaging method. Avatars predicted using this approach, if accurate with respect to anthropometric dimensions, can serve multiple purposes including patient metabolic disease risk stratification in clinical settings.

Methods

Manifold regression 3D avatar prediction equations were developed on a sample of 570 adults who completed 3D optical scans, dual-energy X-ray absorptiometry (DXA), and bioimpedance analysis (BIA) evaluations. A new prospective sample of 84 adults had ground-truth measurements of 6 body circumferences, 7 volumes, and 7 surface areas with a 20-camera 3D reference scanner. 3D humanoid avatars were generated on these participants with manifold regression including age, weight, height, DXA %fat, and BIA impedances as potential predictor variables. Ground-truth and predicted avatar anthropometric dimensions were quantified with the same software.

Results

Following exploratory studies, one manifold prediction model was moved forward for presentation that included age, weight, height, and %fat as covariates. Predicted and ground-truth avatars had similar visual appearances; correlations between predicted and ground-truth anthropometric estimates were all high (R2s, 0.75-0.99; all p < 0.001) with non-significant mean differences except for arm circumferences (%D ~ 5%; p < 0.05). Concordance correlation coefficients ranged from 0.80-0.99 and small but significant bias (p < 0.05 - 0.01) was present with Bland-Altman plots in 13 of 20 total anthropometric measurements. The mean waist to hip circumference ratio predicted by manifold regression was non-significantly different from ground-truth scanner measurements.

Conclusions

3D avatars predicted from demographic, physical, and other accessible characteristics can produce body representations with accurate anthropometric dimensions without a 3D scanner. Combining manifold regression algorithms into established body composition methods such as DXA, BIA, and other accessible methods provides new research and clinical opportunities.

Modeling thermoregulatory responses during high-intensity exercise in warm environments

The six cylinder thermoregulatory model (SCTM) has been validated thoroughly for resting humans. This type of modeling is helpful to predict and develop guidance for safe performance of work and recreational activities. In the context of a warming global climate, updating the accuracy of the model for intense exercise in warm environments will help a wide range of individuals in athletic, recreational, and military settings. Three sets of previously collected data were used to determine SCTM accuracy. Dataset 1: two groups [large (LG) 91.5 kg and small (SM) 67.7 kg] of individuals performed 60 min of semirecumbent cycling in temperate conditions (25.1°C) at metabolic rates of 570-700 W. Dataset 2: two LG (100 kg) and SM (65.8 kg) groups performed 60 min of semirecumbent cycling in warm/hot environmental conditions (36.2°C) at metabolic rates of 590-680 W. Dataset 3: seven volunteers completed 8-km track trials (∼30 min) in cool (17°C) and warm (30°C) environments. The volunteers' metabolic rates were estimated to be 1,268 W and 1,166 W, respectively. For all datasets, SCTM-predicted core temperatures were found to be similar to the observed core temperatures. The root mean square deviations (RMSDs) ranged from 0.06 to 0.46°C with an average of 0.2°C deviation, which is less than the acceptance threshold of 0.5°C. Thus, the present validation shows that SCTM predicts core temperatures with acceptable accuracy during intense exercise in warm environments and successfully captures core temperature differences between large and small individuals.NEW & NOTEWORTHY The SCTM has been validated thoroughly for resting humans in warm and cold environments and during water immersion. The present study further demonstrated that SCTM predicts core temperatures with acceptable accuracy during intense exercise up to 1,300 W in temperate and warm environments and captures core temperature differences between large and small individuals. SCTM is potentially useful to develop guidance for safe operation in athletic, military, and occupational settings during exposure to warm or hot environments.

A Validated Methodological Approach to Prove the Safety of Clinical Electromagnetic Induction Systems in Magnetic Hyperthermia

The present study focuses on the development of a methodology for evaluating the safety of MNH systems, through the numerical prediction of the induced temperature rise in superficial skin layers due to eddy currents heating under an alternating magnetic field (AMF). The methodology is supported and validated through experimental measurements of the AMF's distribution, as well as temperature data from the torsos of six patients who participated in a clinical trial study. The simulations involved a computational model of the actual coil, a computational model of the cooling system used for the cooling of the patients during treatment, and a detailed human anatomical model from the Virtual Population family. The numerical predictions exhibit strong agreement with the experimental measurements, and the deviations are below the estimated combined uncertainties, confirming the accuracy of computational modeling. This study highlights the crucial role of simulations for translational medicine and paves the way for personalized treatment planning.

Hot brain: practical climate change advice for neurologists

'We are called to be architects of the future, not its victims'-Buckminster Fuller People with chronic neurological conditions may be vulnerable to change and less able to manage its demands: neurological diseases are among the most burdensome. Whether climate change has particular effects on specific neurological diseases or not, the known impaired resilience to change affecting people with neurological diseases requires neurologists to have awareness of potential climate impacts and their management. Preparedness should include understanding of general national and local alerts and action systems, and the ability to advise patients about managing extreme weather events, particularly heatwaves, but also floods and cold snaps. At the same time, we need more research into the particular consequences of climate change on specific neurological diseases. Climate change is a serious healthcare issue, requiring the neurological community to respond as it would, or did, to other serious challenges, such as COVID-19. As disease experts, we all have a role to play.

Human body numerical simulation: An accurate model for a thigh subjected to a cold treatment

This article presents the development of a digital twin model of a thigh portion subjected to various thermal treatments. Two scenarios are investigated: cold water immersion (CWI) and whole body cryotherapy (WBC), for which the comparison of numerical results with experimental measurements validates the consistency of the developed model. The use of real geometry on a first subject demonstrates the high heterogeneity of the temperature field and the need for accurate geometry. A second subject with thicker adipose tissue highlights the impact of the subject's actual morphology on the validity of the treatment and the necessity to work with real geometry in order to optimize cold modalities and develop personalized treatments.

Validation of a human thermoregulatory model during prolonged immersion in warm water

This study validates the Six Cylinder Thermoregulatory Model (SCTM) during prolonged warm water immersion, which underpins the Probability of Survival Decision Aid (PSDA) currently in use by the United States Coast Guard (USCG). PSDA predicts survival time for hypothermia and dehydration. USCG has been using PSDA for search and rescue operation since 2010. In 2019, USCG organized a workshop to review PSDA performance and concluded that PSDA is an essential tool for operation, although it occasionally overestimates survival times in warm waters above 16 °C. Forty-six human subjects were immersed from the neck down in 18, 22, and 26 °C water for 45 min up to 10 h. Rectal temperature (Tcore), 10-site mean skin temperature (Tsk), and water loss were measured. At the end of immersion, Tcore ranged from 35.2 to 38.0 °C, and Tsk ranged from 19.7 to 27.4 °C. The SCTM-predicted Tcore, Tsk and water loss were compared to the measured values. Root mean squared deviation (RMSD) was used to test for acceptable predictions. Tcore RMSDs were 0.2, 0.14, and 0.3 °C in 18, 22, and 26 °C water respectively. Tsk RMSDs were 1.44, 0.76, and 1.1 °C in 18, 22, and 26 °C water respectively. SCTM underpredicted water loss by 84%. Overall, SCTM predicted Tcore and Tsk with acceptable accuracy in 18 and 22 °C water for up to 10 h, but overpredicted in 26 °C water. Future studies and algorithm development are required to improve water loss prediction as well as Tcore and Tsk prediction in 26 °C water.

Reduced CPU Workload for Human Pose Detection with the Aid of a Low-Resolution Infrared Array Sensor on Embedded Systems

Modern embedded systems have achieved relatively high processing power. They can be used for edge computing and computer vision, where data are collected and processed locally, without the need for network communication for decision-making and data analysis purposes. Face detection, face recognition, and pose detection algorithms can be executed with acceptable performance on embedded systems and are used for home security and monitoring. However, popular machine learning frameworks, such as MediaPipe, require relatively high usage of CPU while running, even when idle with no subject in the scene. Combined with the still present false detections, this wastes CPU time, elevates the power consumption and overall system temperature, and generates unnecessary data. In this study, a low-cost low-resolution infrared thermal sensor array was used to control the execution of MediaPipe's pose detection algorithm using single-board computers, which only runs when the thermal camera detects a possible subject in its field of view. A lightweight algorithm with several filtering layers was developed, which allowed the effective detection and isolation of a person in the thermal image. The resulting hybrid computer vision proved effective in reducing the average CPU workload, especially in environments with low activity, almost eliminating MediaPipe's false detections, and reaching up to 30% power saving in the best-case scenario.

An individualized and multi-segmental bioheat model for predicting local conditions of the human body under various thermal environments

Thermoregulatory bioheat models have attracted the attention of researchers due to their conformity with the basis of human thermal perception. For this reason, various models have been presented, such as simplified thermoregulatory bioheat (STB), individualized thermoregulatory bioheat (ITB), and multi-segmental thermoregulatory bioheat (MSTB). In the present study and based upon previous models, an individual multi-segment thermoregulatory bioheat (IMTB) model has been introduced. In this model, the body is subdivided into 17 segments and 3 layers, with the blood circulatory system consisting of arteries, veins, and superficial veins. Also, IMTB can evaluate the individual parameters effects (such as height, weight, gender, and age) on physiological parameters and active/passive systems. Finally, this new model was evaluated in human thermal response predictions over a wide range of transient and steady-state environmental conditions (5.0< Tair(°C) <50.0, 31.0 < RH (%)<70.0) and various individual characteristics (male and female, 20 < age (years) < 69, 50 Collapse

Key Words

• Blood circulatory system
• Individual characteristics
• Local skin temperature
• Multi-segment
• Thermoregulatory bioheat model
• Transient thermal environment

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Affiliation(s)

Mahdi Afzalian Mechanical Engineering, University of Birjand, Iran.
Seyed Alireza Zolfaghari Mechanical Engineering, University of Birjand, Iran.

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Beating the heat: military training and operations in the era of global warming

Global climate change has resulted in an increase in the number and intensity of environmental heat waves, both in areas traditionally associated with hot temperatures and in areas where heat waves did not previously occur. For military communities around the world, these changes pose progressively increasing risks of heat-related illnesses and interference with training sessions. This is a significant and persistent "noncombat threat" to both training and operational activities of military personnel. In addition to these important health and safety concerns, there are broader implications in terms of the ability of worldwide security forces to effectively do their job (particularly in areas that historically already have high ambient temperatures). In the present review, we attempt to quantify the impact of climate change on various aspects of military training and performance. We also summarize ongoing research efforts designed to minimize and/or prevent heat injuries and illness. In terms of future approaches, we propose the need to "think outside the box" for a more effective training/schedule paradigm. One approach may be to investigate potential impacts of a reversal of sleep-wake cycles during basic training during the hot months of the year, to minimize the usual increase in heat-related injuries, and to enhance the capacity for physical training and combat performance. Regardless of which approaches are taken, a central feature of successful present and future interventions will be that they are rigorously tested using integrative physiological approaches.