Reed W. Hoyt

A Real-Time Algorithm for Predicting Core Temperature in Humans

In this paper, we present a real-time implementation of a previously developed offline algorithm for predicting core temperature in humans. The real-time algorithm uses a zero-phase Butterworth digital filter to smooth the data and an autoregressive (AR) model to predict core temperature. The performance of the algorithm is assessed in terms of its prediction accuracy, quantified by the root mean squared error (RMSE), and in terms of prediction uncertainty, quantified by statistically based prediction intervals (Pis). To evaluate the performance of the algorithm, we simulated real-time implementation using core-temperature data collected during two different field studies, involving ten different individuals. One of the studies includes a case of heat illness suffered by one of the participants. The results indicate that although the real-time predictions yielded RMSEs that are larger than those of the offline algorithm, the real-time algorithm does produce sufficiently accurate predictions for practically meaningful prediction horizons (~20 min). The algorithm reached alert (39°C) and alarm (39.5°C) thresholds for the heat-ill individual but did not even attain the alert threshold for the other individuals, demonstrating the algorithms good sensitivity and specificity. The Pis reflected, in an intuitively expected manner, the uncertainty associated with real-time forecast as a function of prediction horizon and core-temperature variability. The results also corroborate the feasibility of universal AR models, where an offline-developed model based on one individuals data could be used to predict any other individual in real time. We conclude that the real-time implementation of the algorithm confirms the attributes observed in the offline version and, hence, could be considered as a warning tool for impending heat illnesses.

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.

Comparative Analysis of Metabolic Cost Equations: A Review

Military personnel often engage in multi-day missions in harsh environments that require physical strength and endurance.xa0 Predicting the metabolic costs of dismounted military movements is of critical importance for mission planning and ensuring Soldier safety.xa0 The ability to accurately predict individualized thermo-physiological responses specific to variables such as clothing, equipment, weather, terrain, and environment is of significant concern.xa0 While there are multiple equations published that predict metabolic cost, only a few account for all of these variables.xa0 This paper compares several well-recognized equations that address the needs of the military: 1) Givoni & Goldman (1971), 2) Pandolf et al. (1977), 3) American College of Sports Medicine (ACSM) (2000), 4) Minetti et al. (2002), and 5) Santee et al. (2003b). This review shows that existing equations generally lack some of the required elements for estimating military activities and, with the exception of the Pandolf Equation, others do not account for an external load, resting conditions and terrain or surface characteristics.xa0 Furthermore, this review outlines the need for continued refinement of existing equations or development of improved estimation equations.

ALTERNATIVE APPROACHES TO IMPROVE PHYSIOLOGICAL PREDICTIONS

Abstract : Recent advancements in technology have resulted in new biosensors and information processing capabilities that permit on-line, real-time measurement of physiological variables. This has, in turn, given rise to the possibility of developing soldier-specific, data-driven predictive models for assessing physiological status in the battlefield. This paper explores how the accuracy of a predictive model based on first principles physiology can be enhanced by data-driven black box techniques of modeling and predicting human physiological variables. Such hybrid techniques are employed here in the prediction of core temperature. Preliminary results show that the mean square error of prediction can be reduced by up to fifty percent for prediction horizons of up to 30 minutes.

Subcutaneous Glucose Concentration as a Predictor Variable for Energy Expenditure during Resistance Exercise in Humans

The paper describes concurrent, minute-by-minute dynamics of subcutaneous glucose concentration and energy expenditure in young male subjects performing 40-min resistance exercise in a whole room calorimeter. The observed negative correlation between subcutaneous glucose concentration, as measured by continuous glucose monitoring (CGM) sensor and energy expenditure is exploited to propose and validate a simple linear model, which is used to estimate minute-by-minute energy expenditure from CGM sensor readings. The data were collected from seven young adult male subjects during their 48-hour stay in calorimeter room. Each subject had two 48-hour calorimeter sessions, except one subject who only performed one session. The minute-by-minute CGM data were regressed on energy expenditure (EE) data thus obtaining a linear model connecting these two quantities. This model was subsequently used to estimate EE from CGM readings for the data that were not used in the training dataset. The performance of the linear regression models was analyzed using Bland-Altman plots and it is demonstrated that the CGM sensor can provide a valid predictor variable which can be combined with other physiological parameters to estimate energy expenditure in field conditions.