Objective: As metabolic cost is a primary factor influencing humans' gait, we want to deepen our understanding of metabolic energy expenditure models. Therefore, this paper identifies the parameters and input variables, such as muscle or joint states, that contribute to accurate metabolic cost estimations. Methods: We explored the parameters of four metabolic energy expenditure models in a Monte Carlo sensitivity analysis. Then, we analysed the model parameters by their calculated sensitivity indices, physiological context, and the resulting metabolic rates during the gait cycle. The parameter combination with the highest accuracy in the Monte Carlo simulations represented a quasi-optimized model. In the second step, we investigated the importance of input parameters and variables by analysing the accuracy of neural networks trained with different input features. Results: Power-related parameters were most influential in the sensitivity analysis and the neural network-based feature selection. We observed that the quasi-optimized models produced negative metabolic rates, contradicting muscle physiology. Neural network-based models showed promising abilities but have been unable to match the accuracy of traditional metabolic energy expenditure models. Conclusion: We showed that power-related metabolic energy expenditure model parameters and inputs are most influential during gait. Furthermore, our results suggest that neural network-based metabolic energy expenditure models are viable. However, bigger datasets are required to achieve better accuracy. Significance: As there is a need for more accurate metabolic energy expenditure models, we explored which musculoskeletal parameters are essential when developing a model to estimate metabolic energy.

翻译：摘要：目的：由于代谢成本是影响人类步态的主要因素，我们希望深化对代谢能耗模型的理解。因此，本文识别了有助于准确估算代谢成本的参数与输入变量（如肌肉或关节状态）。方法：通过蒙特卡洛敏感性分析，我们探究了四种代谢能耗模型的参数。随后，根据计算所得敏感性指数、生理背景及步态周期中的代谢率对模型参数进行分析。蒙特卡洛模拟中精度最高的参数组合代表了一种准优化模型。第二步，通过分析以不同输入特征训练的神经网络精度，我们研究了输入参数与变量的重要性。结果：在敏感性分析与基于神经网络的特征选择中，功率相关参数最具影响力。我们观察到准优化模型会产生负代谢率，这与肌肉生理学相矛盾。基于神经网络的模型展现出潜力，但尚无法达到传统代谢能耗模型的精度。结论：研究表明，步态中功率相关的代谢能耗模型参数与输入最为关键。此外，我们的结果表明基于神经网络的代谢能耗模型具有可行性，但需更大规模数据集以实现更高精度。意义：鉴于对更精确代谢能耗模型的需求，本文探究了开发代谢能耗估算模型时哪些肌肉骨骼参数至关重要。