Evaluation of physiological workload assessment methods using heart rate and accelerometry for a smart wearable...

Area of Science:

• Occupational Health
• Sports Science
• Biomedical Engineering

Background:

• Work metabolism (WM) estimation is crucial for assessing occupational demands and employee well-being.
• Oxygen consumption (VO2) is a direct measure of WM, often estimated using heart rate (HR) in field settings.
• The accuracy of HR-based VO2 estimation is limited by individual variability and activity-specific factors.

Purpose of the Study:

• To evaluate and compare the accuracy of three distinct models for estimating work metabolism (WM).
• To assess WM against indirect calorimetry during simulated occupational tasks.
• To determine the optimal wearable sensor combination for reliable WM assessment.

Main Methods:

• Three models were tested: HR-Flex, HR branched (HR + hip accelerometers), and HR + arm-leg accelerometers.
• Twelve participants engaged in five simulated work activities and three submaximal tests.
• Work metabolism was measured using indirect calorimetry as the criterion standard.

Main Results:

• The HR + arm-leg ACC model demonstrated the highest accuracy, with limits of agreement (LoA) of -3.94 and 2.00 mL/min/kg.
• The HR-Flex model showed LoA of -5.01 and 5.36 mL/min/kg.
• The HR branched model yielded LoA of -6.71 and 1.52 mL/min/kg.

Conclusions:

• The HR + arm-leg ACC model is recommended for wearable systems estimating work metabolism due to its superior accuracy.
• Accurate WM estimation using wearable technology can help mitigate health risks associated with high-demand work.
• This approach offers a feasible method for monitoring and managing employee energy expenditure in occupational settings.