A time-efficient continuous ramp protocol for data-driven walking energy expenditure estimation across multiple speeds.

BACKGROUND

Recent research has sought to use data-driven models to estimate walking energy expenditure across multiple speeds via wearable devices. Many studies employ a discrete step protocol-repeatedly walking at a constant speed for several minutes-because indirect calorimetry depends on time-delayed respiratory responses. However, this approach becomes time-inefficient when constructing sufficiently diverse datasets for deep learning, which requires large amounts of distinctive data. To address this issue, we integrated a data-driven approach with a previously proposed continuous protocol wherein walking speeds are gradually increased within a single trial. The purpose of this study is to compare the effectiveness of such a continuous dataset for energy expenditure estimation against a conventional discrete approach.

METHODS

Fourteen subjects walked on a treadmill wearing four IMUs, while energy expenditure was measured using an indirect calorimetry. In the continuous ramp protocol, subjects walked for 10 mins at speeds linearly increasing from 1.0 to 1.75 m/s. The discrete step protocol involved five speeds within the same range, each maintained for 6 mins. In the continuous ramp protocol, energy expenditure was mapped to each speed after compensating for respiratory delay, whereas in the discrete step protocol, we used averaged breath-by-breath measurements of the final 3 minutes. We compared the kinematics, kinetics, and energy expenditure between the two protocols. Subsequently, 13 additional subjects were recruited to compare a commercial smartwatch with linear and deep learning models trained on datasets from each protocol.

RESULTS

After compensating for respiratory delays, no differences in energy expenditure were observed between the two protocols, although kinematic differences appeared at speeds above 1.5 m/s. These differences did not impair estimation accuracy: deep learning models trained on the discrete and continuous datasets showed comparable performance (13.1% vs. 10.7% mean error, respectively), both significantly outperforming the smartwatch. Furthermore, when trained on the more diverse data from the continuous ramp protocol, a deep learning model achieved uniformly low error across a broad speed range with only a single IMU.

CONCLUSION

The continuous ramp protocol can generate a valid walking motion-energy expenditure dataset in a time-efficient manner, improving model performance by providing richer data diversity. This approach is not limited to walking speed but can be applied to other continuously changing exercise intensities across various forms of locomotion, thus promoting efforts to replace indirect calorimetry, traditionally requires extensive laboratory experiments.