Triple-E Principle: Leveraging Occam’s Razor for Dance Energy Expenditure Estimation

Abstract

Objective: Dance, as a globally practiced physical activity, presents challenges in accurately assessing energy expenditure due to its diverse styles and tempos. Traditional methods, relying on empirical formulas within ActiGraph accelerometers, often result in significant biases. While multiple wearable sensors have been introduced to mitigate these biases, they increase model complexity. Methods: This study proposes the Triple-E principle—Effectiveness, Efficiency, and Extension—as a framework for developing state-of-the-art (SOTA) machine learning models aimed at accurately estimating energy expenditure, while minimizing model complexity and optimizing sensor placement. To validate the proposed approach, we recruited a cohort of 250 participants (mean age: 63.0 ± 6.0 years), each performing ballroom, aerobic, or square dance routines. Participants were fitted with ActiGraph wGT3X-BT accelerometers at five anatomical locations, along with the CORTEX MetaMax 3B gas analyzer for metabolic data collection. We analyzed 311 physiological signal sequences and 1,555 acceleration count sequences. Results: Empirical formulas were proved inaccurate for dance energy expenditure, with Mean Absolute Percentage Error (MAPE) exceeding 50% and Root Mean Squared Error (RMSE) surpassing 3.23. A bidirectional stepwise regression model incorporating heart rate or triaxial motion sequences from accelerometers achieved an average goodness-of-fit of 0.73, identifying optimal accelerometer sites based on Efficiency principle. A random forest regression model minimized errors to 5% (MAPE) and 0.33 (RMSE) with data from all sites. Notably, wrist accelerometers and heart rate alone provided sufficient accuracy (RMSE: 0.35-0.36), highlighting a trade-off between Effectiveness and Efficiency. A deep-learning network pipeline based on the Extension principle automatically extracted features, achieving an average RMSE to 0.15. Conclusion: This study introduces a pioneering quantitative and unified model assessment system. Thoroughly analyzed and validated in the context of dance, the research offers detailed explanations of the most effective, efficient, and extensive models.