دورية أكاديمية
Contributing Components of Metabolic Energy Models to Metabolic Cost Estimations in Gait.
| العنوان: | Contributing Components of Metabolic Energy Models to Metabolic Cost Estimations in Gait. |
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| المؤلفون: | Gambietz M, Nitschke M, Miehling J, Koelewijn AD |
| المصدر: | IEEE transactions on bio-medical engineering [IEEE Trans Biomed Eng] 2024 Apr; Vol. 71 (4), pp. 1228-1236. Date of Electronic Publication: 2024 Mar 20. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Institute Of Electrical And Electronics Engineers Country of Publication: United States NLM ID: 0012737 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1558-2531 (Electronic) Linking ISSN: 00189294 NLM ISO Abbreviation: IEEE Trans Biomed Eng Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: New York, NY : Institute Of Electrical And Electronics Engineers Original Publication: New York, IEEE Professional Technical Group on Bio-Medical Engineering. |
| مواضيع طبية MeSH: | Gait*/physiology , Neural Networks, Computer*, Humans ; Biomechanical Phenomena ; Energy Metabolism/physiology ; Muscles ; Walking/physiology |
| مستخلص: | 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. |
| تواريخ الأحداث: | Date Created: 20231108 Date Completed: 20240321 Latest Revision: 20240321 |
| رمز التحديث: | 20240321 |
| DOI: | 10.1109/TBME.2023.3331271 |
| PMID: | 37938950 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1558-2531 |
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| DOI: | 10.1109/TBME.2023.3331271 |