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Contributing Components of Metabolic Energy Models to Metabolic Cost Estimations in Gait
Machine Learning · Submitted 18 Oct 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Conclusion: We showed that power-related metabolic energy expenditure model parameters and inputs are most influential during gait.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Objective: As metabolic cost is a primary factor influencing humans' gait, we want to deepen our understanding of metabolic energy expenditure models. | background 0.97 | 0.13 | 0.01 | asserted 0.95 |
| s1 | Therefore, this paper identifies the parameters and input variables, such as muscle or joint states, that contribute to accurate metabolic cost estimations. | contribution 0.96 | 0.66 | 0.03 | asserted 0.98 |
| s2 | Methods: We explored the parameters of four metabolic energy expenditure models in a Monte Carlo sensitivity analysis. | method 1.00 | 0.96 | 0.01 | asserted 0.99 |
| s3 | Then, we analysed the model parameters by their calculated sensitivity indices, physiological context, and the resulting metabolic rates during the gait cycle. | method 1.00 | 0.95 | 0.02 | asserted 0.88 |
| s4 | The parameter combination with the highest accuracy in the Monte Carlo simulations represented a quasi-optimized model. | method 0.82 | 0.93 | 0.03 | asserted 1.00 |
| s5 | 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. | method 0.99 | 0.95 | 0.02 | asserted 0.94 |
| s6 | Results: Power-related parameters were most influential in the sensitivity analysis and the neural network-based feature selection. | result 1.00 | 0.94 | 0.15 | asserted 1.00 |
| s7 | We observed that the quasi-optimized models produced negative metabolic rates, contradicting muscle physiology. | result 0.98 | 0.95 | 0.03 | asserted 1.00 |
| s8 | Neural network-based models showed promising abilities but have been unable to match the accuracy of traditional metabolic energy expenditure models. | result 0.93 | 0.72 | 0.06 | asserted 0.99 |
| s9 | Conclusion: We showed that power-related metabolic energy expenditure model parameters and inputs are most influential during gait. | result 0.86 | 0.94 | 0.86 | asserted 1.00 |
| s10 | Furthermore, our results suggest that neural network-based metabolic energy expenditure models are viable. | interpretation 0.93 | 0.76 | 0.10 | hedged possibility 0.93 |
| s11 | However, bigger datasets are required to achieve better accuracy. | future work 0.84 | 0.42 | 0.04 | asserted 0.74 |
| s12 | 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. | contribution 0.47 | 0.75 | 0.04 | asserted 0.82 |
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The paper
Original abstract
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.
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