Adam W Potter

Acceptability and Usability of an Ambulatory Health Monitoring System for Use by Military Personnel

OCCUPATIONAL APPLICATIONS A physiological status monitoring system was evaluated for use by soldiers in the field. Two different designs were evaluated, with the design based on previous human factors evaluations proven to be more comfortable and acceptable for use. This study demonstrated that the advanced design of the EQ-02 physiological status monitoring system met dismounted soldier needs. Furthermore, this study validated the use of a usability evaluation in the successful design/advancement of a physiological status monitoring system. TECHNICAL ABSTRACT Background: Previous research has shown that the form factor of a physiological status monitoring system, the Equivital™ EQ-01 (Hidalgo Ltd., Cambridge, UK) had problems associated with comfort and usability of the system for soldiers. Previous data gathered was used to guide improvements in the physiological status monitoring system. Purpose: Assess whether the previous feedback from usability evaluations helped guide improvements in comfort, acceptability, and usability of a physiological status monitoring system for dismounted soldiers. Improvements to the EQ-01 system were incorporated into the next-generation EQ-02 (Hidalgo Ltd., Cambridge, UK) system. Methods: Thirty-nine infantry dismounted soldiers were randomly assigned to wear either an EQ-01 or EQ-02 system while performing standard military field training. They filled out a survey on fit, comfort, irritation to the body, impact on military performance, and acceptability. They then wore the other system and filled out the same survey. Results: The Equivital™ EQ-02 system was superior in terms of fit (51% better in overall fit), ease of donning (10% easier), comfort (45% more comfortable), impact on military performance (45% less impact), impact on the body (17% less impact), and acceptability (32% more acceptable). All these measures are subjective self-report ratings. Conclusions: A human factors engineering approach provided an effective means of guiding improvements and the production of a physiological status monitoring system that dismounted soldiers were more likely to accept and wear.

Ebola Response: Modeling the Risk of Heat Stress from Personal Protective Clothing.

Introduction A significant number of healthcare workers have responded to aid in the relief and containment of the 2013 Ebola virus disease (EVD) outbreak in West Africa. Healthcare workers are required to wear personal protective clothing (PPC) to impede the transmission of the virus; however, the impermeable design and the hot humid environment lead to risk of heat stress. Objective Provide healthcare workers quantitative modeling and analysis to aid in the prevention of heat stress while wearing PPC in West Africa. Methods A sweating thermal manikin was used to measure the thermal (Rct) and evaporative resistance (Ret) of the five currently used levels of PPC for healthcare workers in the West Africa EVD response. Mathematical methods of predicting the rise in core body temperature (Tc) in response to clothing, activity, and environment was used to simulate different responses to PPC levels, individual body sizes, and two hot humid conditions: morning/evening (air temperature: 25°C, relative humidity: 40%, mean radiant temperature: 35°C, wind velocity: 1 m/s) and mid-day (30°C, 60%, 70°C, 1 m/s). Results Nearly still air (0.4 m/s) measures of Rct ranged from 0.18 to 0.26 m2 K/W and Ret ranged from 25.53 to 340.26 m2 Pa/W. Conclusion Biophysical assessments and modeling in this study provide quantitative guidance for prevention of heat stress of healthcare workers wearing PPC responding to the EVD outbreak in West Africa.

Mathematical prediction of core body temperature from environment, activity, and clothing: The heat strain decision aid (HSDA)

Physiological models provide useful summaries of complex interrelated regulatory functions. These can often be reduced to simple input requirements and simple predictions for pragmatic applications. This paper demonstrates this modeling efficiency by tracing the development of one such simple model, the Heat Strain Decision Aid (HSDA), originally developed to address Army needs. The HSDA, which derives from the Givoni-Goldman equilibrium body core temperature prediction model, uses 16 inputs from four elements: individual characteristics, physical activity, clothing biophysics, and environmental conditions. These inputs are used to mathematically predict core temperature (Tc) rise over time and can estimate water turnover from sweat loss. Based on a history of military applications such as derivation of training and mission planning tools, we conclude that the HSDA model is a robust integration of physiological rules that can guide a variety of useful predictions. The HSDA model is limited to generalized predictions of thermal strain and does not provide individualized predictions that could be obtained from physiological sensor data-driven predictive models. This fully transparent physiological model should be improved and extended with new findings and new challenging scenarios.

Biophysical Assessment and Predicted Thermophysiologic Effects of Body Armor

Introduction Military personnel are often required to wear ballistic protection in order to defend against enemies. However, this added protection increases mass carried and imposes additional thermal burden on the individual. Body armor (BA) is known to reduce combat casualties, but the effects of BA mass and insulation on the physical performance of soldiers are less well documented. Until recently, the emphasis has been increasing personal protection, with little consideration of the adverse impacts on human performance. Objective The purpose of this work was to use sweating thermal manikin and mathematical modeling techniques to quantify the tradeoff between increased BA protection, the accompanying mass, and thermal effects on human performance. Methods Using a sweating thermal manikin, total insulation (IT, clo) and vapor permeability indexes (im) were measured for a baseline clothing ensemble with and without one of seven increasingly protective U.S. Army BA configurations. Using mathematical modeling, predictions were made of thermal impact on humans wearing each configuration while working in hot/dry (desert), hot/humid (jungle), and temperate environmental conditions. Results In nearly still air (0.4 m/s), IT ranged from 1.57 to 1.63 clo and im from 0.35 to 0.42 for the seven BA conditions, compared to IT and im values of 1.37 clo and 0.45 respectively, for the baseline condition (no BA). Conclusion Biophysical assessments and predictive modeling show a quantifiable relationship exists among increased protection and increased thermal burden and decreased work capacity. This approach enables quantitative analysis of the tradeoffs between ballistic protection, thermal-work strain, and physical work performance.

Loaded and unloaded foot movement differentiation using chest mounted accelerometer signatures

Heavy loads often subject foot soldiers and first-responders to increased risk musculoskeletal injury (MSI). Identifying excessive loads in real-time could help identify when soldiers are at greater risk of MSI. Using Principal Component Analysis (PCA) we derived a loaded (>35 kg) versus unloaded Naïve Bayesian classification model from 22 male Soldiers (age 20 ± 3.5 yrs, height 1.76 ± 0.09 m and weight 83 ± 13 kg). Using seven-fold cross validation we demonstrated that using only one feature our model accurately classifies heavily loaded versus unloaded over 90% of the time. This technique lends itself to use in real time accelerometry sensors and shows promise for more complex gait analysis.

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.

Cardiorespiratory responses to heavy military load carriage over complex terrain

This study examined complex terrain march performance and cardiorespiratory responses when carrying different Soldier loads. Nine active duty military personnel (age, 21 ± 3 yr; height, 1.72 ± 0.07 m; body mass (BM), 83.4 ± 12.9 kg) attended two test visits during which they completed consecutive laps around a 2.5-km mixed terrain course with either a fighting load (30% BM) or an approach load (45% BM). Respiratory rate and heart rate data were collected using physiological status monitors. Training impulse (TRIMP) scores were calculated using Banisters formula to provide an integrated measure of both time and cardiorespiratory demands. Completion times were not significantly different between the fighting and approach loads for either Lap 1 (p = 0.38) or Lap 2 (p = 0.09). Respiration rate was not significantly higher with the approach load than the fighting load during Lap 1 (p = 0.17) but was significantly higher for Lap 2 (p = 0.04). However, heart rate was significantly higher with the approach load versus the fighting load during both Lap 1 (p = 0.03) and Lap 2 (p = 0.04). Furthermore, TRIMP was significantly greater with the approach load versus the fighting load during both Lap 1 (p = 0.02) and Lap 2 (p = 0.02). Trained military personnel can maintain similar pacing while carrying either fighting or approach loads during short mixed terrain marches. However, cardiorespiratory demands are greatly elevated with the approach load and will likely continue to rise during longer distance marches.