University of Twente Student Theses
Quantification and Prediction of History-Dependent Muscle Properties
Sproates, N.T.D. (2023) Quantification and Prediction of History-Dependent Muscle Properties.
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| Abstract: | History-dependent muscle properties, i.e. residual force enhancement (rFE) and residual force depression (rFD), are phenomena that are dependent on the previous state of the muscle, specifically the muscle’s length. rFE is characterized by an increase of steady-state force following active eccentric contraction compared to steady-state isometric force at the corresponding length, whereas rFD is characterized by a decrease in steady-state force following active concentric contraction. These history-dependent muscle properties have been extensively investigated. However, in vivo measurements yielded conflicting outcomes, leading to uncertainties regarding the characteristics of these muscle properties. This study aimed to quantify the influence of the operation region of the force-length relationship on rFE and rFD, as well as to quantify the influence of the muscle fiber-type composition of the muscle on rFE and rFD. Additionally, the occurrence of rFE on the tibialis anterior (TA) is attempted to be predicted with machine learning classifiers based on the acquired datasets. Five subjects performed isometric, lengthening, and shortening contractions at 15.0-20.0 %MVC of the TA at two different ankle angles for the assessment of the influence of the operating region. The chosen ankle angles were subject-specific and ensured that the TA was operating at both the ascending limb and plateau region. For the assessment of the muscle fiber-type composition, six subjects performed isometric, lengthening, and shortening contractions of the TA, primarily composed of fiber type II, and the soleus (SOL), primarily composed of fiber type I, at 7.5-12.5 %MVC. The obtained data measured on the TA are all used as input for six different machine learning classifiers to predict the occurrence of rFE. To assess the influence of the input parameters, one parameter was excluded from the training data one by one. No statistical differences were found regarding the influence of both the operating muscle’s region and the fiber-type composition on the obtained rFE and rFD. However, on average, rFE was 3.98 ± 1.20 % higher on the plateau region and 4.73 ± 4.67 % higher on the TA, respectively. Additionally, more subjects were categorized as responders measuring on the plateau and on the TA. Regarding the machine learning classifiers, three classifiers, i.e. Linear Discriminant Analysis (LDA), Logistic Regression (LR), and Support Vector Machine (SVM), exhibited the highest overall performance, each achieving an f1 score greater than 0.8. Excluding muscle activation parameters from the training data, resulted in a decrease in performance. When excluding the normalized ankle angle, only LR showed a decrease in performance. These results indicated no discernible relation between rFD and the operating muscle’s region or the muscle fiber-type distribution. However, the findings suggested that rFE may be better captured when measured on the TA and on the plateau region, although further confirmation is required through future research. Understanding these history-dependent muscle properties contributes to a better overall understanding of the biomechanics of human movements and could improve biomechanical models and rehabilitation programs. |
| Item Type: | Essay (Master) |
| Faculty: | TNW: Science and Technology |
| Programme: | Biomedical Engineering MSc (66226) |
| Link to this item: | https://purl.utwente.nl/essays/97394 |
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