Objective: This study aimed to verify and compare the accuracy of energy expenditure (EE) prediction models using shoe-based motion detectors with embedded accelerometers.
Methods: Three physical activity (PA) datasets (unclassified, recognition, and intensity segmentation) were used to develop three prediction models. A multiple classification flow and these models were used to estimate EE. The "unclassified" dataset was defined as the data without PA recognition, the "recognition" as the data classified with PA recognition, and the "intensity segmentation" as the data with intensity segmentation. The three datasets contained accelerometer signals (quantified as signal magnitude area (SMA)) and net heart rate (HRnet). The accuracy of these models was assessed according to the deviation between physically measured EE and model-estimated EE.
Results: The variance between physically measured EE and model-estimated EE expressed by simple linear regressions was increased by 63% and 13% using SMA and HRnet, respectively. The accuracy of the EE predicted from accelerometer signals is influenced by the different activities that exhibit different count-EE relationships within the same prediction model.
Conclusion: The recognition model provides a better estimation and lower variability of EE compared with the unclassified and intensity segmentation models.
Significance: The proposed shoe-based motion detectors can improve the accuracy of EE estimation and has great potential to be used to manage everyday exercise in real time.