Purpose : The purpose of this study was to explore whether incorporating gyroscopic and accelerometer data will improve the prediction of energy expenditure (EE) of preschool children. Three model configurations were developed and compared using (1) accelerometer, (2) gyroscope, and (3) accelerometer + gyroscope data (dual sensor). Method : Participants (n = 39; aged 3 to <6 years) were equipped with OPAL, GT9X, and GENEActiv devices, worn on the right hip, right wrist, and left wrist, while EE was simultaneously measured using a portable metabolic unit. The protocol consisted of semistructured activities spanning a range of intensities from low to high. A total of 54 machine learning models were developed to predict EE (2 EE measures metabolic equivalents, kilojoules per minute × 3 wear locations × 3 model types random forest, linear regression, and fully connected neural network × 3 sensor configurations). Model performance was evaluated using root mean squared error. Results : Our findings reveal that, across the various configurations, the random forest model utilizing dual-sensor data achieved marginally lower mean root mean squared error in the majority of cases. Conclusion : Given the minimal improvements observed and the challenges associated with data acquisition, we recommend that researchers utilize accelerometer-based models moving forward.
Coyle-Asbil et al. (Thu,) studied this question.