Alexander P. Welles

Thermal-Work Strain During Marine Rifle Squad Operations in Afghanistan

The physiological burden created by heat strain and physical exercise, also called thermal-work strain, was quantified for 10 male Marines (age 21.9 ± 2.3 years, height 180.3 ± 5.2 cm, and weight 85.2 ± 10.8 kg) during three dismounted missions in Helmand Province, Afghanistan. Heart rate (HR) and core body temperature (T core) were recorded every 15 seconds (Equivital EQ-01; Hidalgo, Cambridge, United Kingdom) during periods of light, moderate, and heavy work and used to estimate metabolic rate. Meteorological measures, clothing characteristics, anthropometrics, and estimated metabolic rates were used to predict T core for the same missions during March (spring) and July (summer) conditions. Thermal-work strain was quantified from HR and T core values using the Physiological Strain Index (PSI) developed by Moran et al. July PSI and T core values were predicted and not observed due to lack of access to in-theater warfighters at that time. Our methods quantify and compare the predicted and observed thermal-work strain resulting from environment and worn or carried equipment and illustrate that a small increase in ambient temperature and solar load might result in increased thermal-work strain.

Real time gait pattern classification from chest worn accelerometry during a loaded road march

Accelerometers, whether in smart phones or wearable physiological monitoring systems are becoming widely used to identify movement and activities of free living individuals. Although there has been much work in applying computationally intensive methods to this problem, this paper focuses on developing a real-time gait analysis approach that is intuitive, requires no individual calibration, can be extended to complex gait analysis, and can readily be adopted by ambulatory physiological monitors for use in real time. Chest-mounted tri-axial accelerometry data were collected from sixty-one male U.S. Army Ranger candidates engaged in an 8 or 12 mile loaded (35 Kg packs) timed road march. The pace of the road march was such that volunteers needed to both walk and run. To provide intuitive features we examined the periodic patterns generated from 4s periods of movement from the vertical and longitudinal accelerometer axes. Applying the “eigenfaces” face recognition approach we used Principal Components Analysis to find a single basis vector from 10% of the data (n=6) that could distinguish patterns of walk and run with a classification rate of 95% and 90% (n=55) respectively. Because these movement features are based on a gridded frequency count, the method is applicable for use by body-worn microprocessors.

Wearable Physiological Monitoring for Human Thermal-Work Strain Optimization

Safe performance limits of soldiers and athletes have typically relied on predictive work-rest models of ambient conditions, average work intensity, and characteristics of the population. Bioengineering advances in noninvasive sensor technologies, including miniaturization, reduced cost, power requirements, and comfort, now make it possible to produce individual predictions of safe thermal-work limits. These precision medicine assessments depend on the development of thoughtful algorithms based on physics and physiology. Both physiological telemetry and thermal-strain indexes have been available for >50 years, but greater computing power and better wearable sensors now make it possible to provide actionable information at the individual level. Core temperature can be practically estimated from time series heart rate data and, using an adaptive physiological strain index, provides meaningful predictions of safe work limits that cannot be predicted from only core temperature or heart rate measurements. Early adopters of this technology include specialized occupations where individuals operate in complete encapsulation such as chemical protective suits. Emerging technologies that focus on heat flux measurements at the skin show even greater potential for estimating thermal-work strain using a parsimonious sensor set. Applications of these wearable technologies include many sports and military training venues where inexperienced individuals can learn effective work pacing strategies and train to safe personal limits. The same strategies can also provide a technologically based performance edge for experienced workers and athletes faced with novel and nonintuitive physiological challenges, such as health care providers in full protective clothing treating Ebola patients in West Africa in 2014. NEW & NOTEWORTHY This mini-review details how the application of computational techniques borrowed from signal processing and control theory can provide meaningful advances for the applied physiological problem of real-time thermal-work strain monitoring. The work examines the development of practical core body temperature estimation techniques and how these can be used in combination with current and updated thermal-work strain indexes to provide objective state assessments and to optimize work rest schedules for a given task.

Extreme health sensing: the challenges, technologies, and strategies for active health sustainment of military personnel during training and combat missions

Military personnel are often asked to accomplish rigorous missions in extremes of climate, terrain, and terrestrial altitude. Personal protective clothing and individual equipment such as body armor or chemical biological suits and excessive equipment loads, exacerbate the physiological strain. Health, over even short mission durations, can easily be compromised. Measuring and acting upon health information can provide a means to dynamically manage both health and mission goals. However, the measurement of health state in austere military environments is challenging; (1) body worn sensors must be of minimal weight and size, consume little power, and be comfortable and unobtrusive enough for prolonged wear; (2) health states are not directly measureable and must be estimated; (3) sensor measurements are prone to noise, artifact, and failure. Given these constraints we examine current successful ambulatory physiological status monitoring technologies, review maturing sensors that may provide key health state insights in the future, and discuss unconventional analytical techniques that optimize health, mission goals, and doctrine from the perspective of thermal work strain assessment and management.

Automated guidance from physiological sensing to reduce thermal-work strain levels on a novel task

This experiment demonstrated that automated pace guidance generated from real-time physiological monitoring allowed less stressful completion of a timed (60 minute limit) 5 mile treadmill exercise. An optimal pacing policy was estimated from a Markov decision process that balanced the goals of the movement task and the thermal-work strain safety constraints. The machine guided pace was based on current physiological strain index (PSI), the time, and the distance already completed. Fourteen healthy and fit young subjects participated in the study (9 men, 5 women). Each participated in an unguided exercise session followed by a guided one. In the unguided session, they were instructed to complete 5 miles in 60 minutes and to try to finish at the lowest body temperature possible; in the guided sessions, participants were instructed to match machine-provided pacing guidance provided every 2 minutes. Continuous real-time measures of heart rate and core body temperature were obtained from a wearable Hidalgo EquivitalTM EQ-02 and the MiniMitter Jonah thermometer pill. Of the fourteen subjects, 13 completed the 5 miles in one hour for the unguided session; at least three different self-pacing strategies were observed, with an alternating speed proving to be most effective. In the guided sessions, 6 subjects were stopped by the machine guidance for exceeding the algorithms PSI “safety” limit. Eight subjects were guided to complete the task with significantly lower PSIs. The results indicate that machine guided advice shows promise for preventing hyperthermia and improving outcomes for performers of an unfamiliar task.

Is normobaric hypoxia an effective treatment for sustaining previously acquired altitude acclimatization

This study examined whether normobaric hypoxia (NH) treatment is more efficacious for sustaining high-altitude (HA) acclimatization-induced improvements in ventilatory and hematologic responses, acute mountain sickness (AMS), and cognitive function during reintroduction to altitude (RA) than no treatment at all. Seventeen sea-level (SL) residents (age = 23 ± 6 yr; means ± SE) completed in the following order: 1) 4 days of SL testing; 2) 12 days of HA acclimatization at 4,300 m; 3) 12 days at SL post-HA acclimatization (Post) where each received either NH (n = 9, [Formula: see text] = 0.122) or Sham (n = 8; [Formula: see text] = 0.207) treatment; and 4) 24-h reintroduction to 4,300-m altitude (RA) in a hypobaric chamber (460 Torr). End-tidal carbon dioxide pressure ([Formula: see text]), hematocrit (Hct), and AMS cerebral factor score were assessed at SL, on HA2 and HA11, and after 20 h of RA. Cognitive function was assessed using the SynWin multitask performance test at SL, on HA1 and HA11, and after 4 h of RA. There was no difference between NH and Sham treatment, so data were combined. [Formula: see text] (mmHg) decreased from SL (37.2 ± 0.5) to HA2 (32.2 ± 0.6), decreased further by HA11 (27.1 ± 0.4), and then increased from HA11 during RA (29.3 ± 0.6). Hct (%) increased from SL (42.3 ± 1.1) to HA2 (45.9 ± 1.0), increased again from HA2 to HA11 (48.5 ± 0.8), and then decreased from HA11 during RA (46.4 ± 1.2). AMS prevalence (%) increased from SL (0 ± 0) to HA2 (76 ± 11) and then decreased at HA11 (0 ± 0) and remained depressed during RA (17 ± 10). SynWin scores decreased from SL (1,615 ± 62) to HA1 (1,306 ± 94), improved from HA1 to HA11 (1,770 ± 82), and remained increased during RA (1,707 ± 75). These results demonstrate that HA acclimatization-induced improvements in ventilatory and hematologic responses, AMS, and cognitive function are partially retained during RA after 12 days at SL whether or not NH treatment is utilized.NEW & NOTEWORTHY This study demonstrates that normobaric hypoxia treatment over a 12-day period at sea level was not more effective for sustaining high-altitude (HA) acclimatization during reintroduction to HA than no treatment at all. The noteworthy aspect is that athletes, mountaineers, and military personnel do not have to go to extraordinary means to retain HA acclimatization to an easily accessible and relevant altitude if reexposure occurs within a 2-wk time period.

Estimation of metabolic energy expenditure from core temperature using a human thermoregulatory model

Human metabolic energy expenditure is critical to many scientific disciplines but can only be measured using expensive and/or restrictive equipment. The aim of this work is to determine whether the SCENARIO thermoregulatory model can be adapted to estimate metabolic rate (M) from core body temperature (TC). To validate this method of M estimation, data were collected from fifteen test volunteers (age = 23 ± 3yr, height = 1.73 ± 0.07m, mass = 68.6 ± 8.7kg, body fat = 16.7 ± 7.3%; mean ± SD) who wore long sleeved nylon jackets and pants (Itot,clo = 1.22, Im = 0.41) during treadmill exercise tasks (32 trials; 7.8 ± 0.5km in 1h; air temp. = 22°C, 50% RH, wind speed = 0.35ms-1). Core body temperatures were recorded by ingested thermometer pill and M data were measured via whole room indirect calorimetry. Metabolic rate was estimated for 5min epochs in a two-step process. First, for a given epoch, a range of M values were input to the SCENARIO model and a corresponding range of TC values were output. Second, the output TC range value with the lowest absolute error relative to the observed TC for the given epoch was identified and its corresponding M range input was selected as the estimated M for that epoch. This process was then repeated for each subsequent remaining epoch. Root mean square error (RMSE), mean absolute error (MAE), and bias between observed and estimated M were 186W, 130 ± 174W, and 33 ± 183W, respectively. The RMSE for total energy expenditure by exercise period was 0.30 MJ. These results indicate that the SCENARIO model is useful for estimating M from TC when measurement is otherwise impractical.

Estimation of core body temperature from skin temperature, heat flux, and heart rate using a Kalman filter

Core body temperature (TC) is a key physiological metric of thermal heat-strain yet it remains difficult to measure non-invasively in the field. This work used combinations of observations of skin temperature (TS), heat flux (HF), and heart rate (HR) to accurately estimate TC using a Kalman Filter (KF). Data were collected from eight volunteers (age 22 ± 4 yr, height 1.75 ± 0.10 m, body mass 76.4 ± 10.7 kg, and body fat 23.4 ± 5.8%, mean ± standard deviation) while walking at two different metabolic rates (∼350 and ∼550 W) under three conditions (warm: 25 °C, 50% relative humidity (RH); hot-humid: 35 °C, 70% RH; and hot-dry: 40 °C, 20% RH). Skin temperature and HF data were collected from six locations: pectoralis, inner thigh, scapula, sternum, rib cage, and forehead. Kalman filter variables were learned via linear regression and covariance calculations between TC and TS, HF, and HR. Root mean square error (RMSE) and bias were calculated to identify the best performing models. The pectoralis (RMSE 0.18 ± 0.04 °C; bias -0.01 ± 0.09 °C), rib (RMSE 0.18 ± 0.09 °C; bias -0.03 ± 0.09 °C), and sternum (RMSE 0.20 ± 0.10 °C; bias -0.04 ± 0.13 °C) were found to have the lowest error values when using TS, HF, and HR but, using only two of these measures provided similar accuracy.

Estimating human metabolic energy expenditure using a bootstrap particle filter

Metabolic energy expenditure is a physiological measure of importance to multiple scientific fields including nutrition, athletic performance, and thermoregulatory modeling. However, measuring metabolic rate in non-laboratory settings is difficult due to the restrictions imposed by laboratory grade measurement methods. The use of probabilistic graphical models, a type of machine learning model, may provide a means to estimate hidden variables such as metabolic rate from more easily observed variables such as heart rate and core body temperature. Using a probabilistic graphical model approach, a particle filter was applied to estimate metabolic rate from continuous heart rate and core body temperature observations. This paper examines which set of observations allows the particle filter to make more accurate estimations of metabolic rate and whether or not the addition of change in metabolic rate as a state variable improves accuracy. Observation and state parameters were learned by linear regression from continuous heart rate, core temperature, and metabolic rate collected from 15 volunteers (age: 23 ± 3 yr, ± SD) over N = 24, 3-hour periods during which 1 hour was spent running up to 8 km distance. State segmentations were learned using k-means clustering with up to 10 states. Observations of heart rate alone and with core temperature were used to predict metabolic rate with a root mean square error ± standard deviation of 166 ± 27 W and 133 ± 26 W.