Individually optimized estimation of energy expenditure in rescue workers using a tri-axial accelerometer and heart rate monitor

TL;DR

This study improves energy expenditure estimation for rescue workers using wearable devices, helping manage food and health during disaster relief.

Contribution

A new individually optimized method for estimating energy expenditure using tri-axial accelerometers and heart rate data in heavy-load physical labor.

Findings

Combining accelerometer and HR data increased energy expenditure estimates by 133-143% compared to using accelerometers alone.

The method was tested on 27 rescue workers during simulated operations over two days.

The approach can improve nutrition and healthcare management in disaster relief scenarios.

Abstract

Objectives: This study aimed to provide an improved energy expenditure estimation for heavy-load physical labor using accelerometer data and heart rate (HR) measured by wearables and to support food preparation and supply management for disaster relief and rescue operations as an expedition team.

Methods: To achieve an individually optimized estimation for energy expenditure, a model equation parameter was determined based on the measurements of physical activity and HR during simulated rescue operations. The metabolic equivalent of task (MET), which was measured by using a tri-axial accelerometer and individual HR, was used, where two (minimum and maximum) or three (minimum, intermediate, and maximum) representative reference points were selected for each individual model fitting. In demonstrating the applicability of our approach in a realistic situation, accelerometer-based METs and HR of 30 males were measured using the tri-axial accelerometer and wearable HR during simulated rescue operations over 2 days.

Results: Data sets of 27 rescue operations (age:34.2 ± 7.5 years; body mass index (BMI):22.9 ± 1.5 kg/m2) were used for the energy expenditure estimation after excluding three rescue workers due to their activity type and insufficient HR measurement. Using the combined approach with a tri-axial accelerometer and HR, the total energy expenditure increased by 143% for two points and 133% for three points, compared with the estimated total energy expenditure using only the accelerometer-based method.

Conclusion: The use of wearables provided a reasonable estimation of energy expenditure for physical workers with heavy equipment. The application of our approach to disaster relief and rescue operations can provide important insights into nutrition and healthcare management.

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