Tao Kuan, Meng Kun, Gao Bingcan, Yang Junchao, Qiu Junqiang
IEEE Trans Neural Syst Rehabil Eng. 2025;33:2088-2096. doi: 10.1109/TNSRE.2025.3574739.
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.
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.
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.
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.
舞蹈作为一项全球范围内开展的体育活动，因其风格和节奏多样，在准确评估能量消耗方面存在挑战。传统方法依赖于ActiGraph加速度计中的经验公式，往往会导致显著偏差。虽然已经引入了多个可穿戴传感器来减轻这些偏差，但它们增加了模型的复杂性。
本研究提出了三E原则——有效性、效率和扩展性——作为开发最先进（SOTA）机器学习模型的框架，旨在准确估计能量消耗，同时最小化模型复杂性并优化传感器放置。为了验证所提出的方法，我们招募了250名参与者（平均年龄：63.0±6.0岁），每人进行交际舞、有氧舞蹈或广场舞套路。参与者在五个解剖位置佩戴ActiGraph wGT3X-BT加速度计，同时使用CORTEX MetaMax 3B气体分析仪收集代谢数据。我们分析了311个生理信号序列和1555个加速度计数序列。
事实证明，经验公式对于舞蹈能量消耗并不准确，平均绝对百分比误差（MAPE）超过50%，均方根误差（RMSE）超过3.23。一个结合了来自加速度计的心率或三轴运动序列的双向逐步回归模型实现了0.73的平均拟合优度，根据效率原则确定了最佳加速度计位置。一个随机森林回归模型使用所有位置的数据将误差最小化至5%（MAPE）和0.33（RMSE）。值得注意的是，仅手腕加速度计和心率就提供了足够的准确性（RMSE：0.35 - 0.36），突出了有效性和效率之间的权衡。一个基于扩展性原则的深度学习网络管道自动提取特征，平均RMSE达到0.15。
本研究引入了一个开创性的定量和统一模型评估系统。在舞蹈背景下进行了全面分析和验证，该研究对最有效、高效和广泛的模型进行了详细解释。
