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.

代谢成本和能量消耗模型的参数、输入变量以及神经网络等对代谢成本估计的准确性贡献最大。