Assessing the behavior of a hybrid model of the knee with contact surrogate under parameter uncertainties.

Modeling the knee is an important factor in increasing the quality of life of both healthy individuals and patients. Nevertheless, the intricate nature of the knee makes this problem complicated. In this study, an extension to an established planar knee joint model with Hertzian contact pairs is proposed with contact mechanics based on polynomial chaos expansion surrogate. Firstly, the finite element (FE) model is made representing a contact pair of sphere-to-plane type with two layers on both bodies, corresponding to the cartilage and the bone. Five variables corresponding to both geometry and material parameters are used to parametrize this model. Then, 128 distinct variants of the FE model are created based on a quasi-Monte Carlo sequence. This dataset is used to train and validate the surrogate. The trained surrogate is proven to have predictive capabilities with an average nRMSE of 0.2% in randomized test/train splits. When included in a model of the knee and tested under parameter uncertainties in Monte Carlo simulations, it results in nRMSE of 58% for angular coordinate compared to the original model with Hertzian pair. This signifies the high influence of contact formulation on the model output and the need for more physically based models in knee contact modeling.

Quantitative assessment of muscle fatigue during rowing ergometer exercise using wavelet analysis of surface electromyography (sEMG).

In this paper, we present a quantitative assessment of muscle fatigue using surface electromyography (sEMG), a widely recognized method that is conducted through various analytical approaches, including analysis of spectral and time-frequency distributions. Existing research in this field has demonstrated considerable variability in the computational methods used. Although some studies highlight the efficacy of wavelet analysis in dynamic motion, few offer a comprehensive method for determining fatigue and applying it to specific movements. Previous research has focused primarily on discerning differences based on sport type or gender, with a notable absence of studies that presented results for quantifying fatigue during exercise with rowing ergometers. Developing on our previous work, where we introduced a method for determining muscle fatigue through wavelet analysis, considering biomechanical aspects of limb position changes, this current article serves as a continuation. Our study refines the research approach for a selected group, focusing on fatigue determination using the previously established method. The results obtained confirm the effectiveness of DWT analysis in assessing muscle fatigue, as evidenced by the achievement of negative values of the regression coefficients of Median Frequency (MDF) during exercises performed to maximal fatigue. Furthermore, it has been confirmed that the homogeneity of the group and, in the case of the examined group, the results previously achieved or lower limb strength do not have an impact on the results. Finally, we discuss the main limitations of our study and outline the subsequent steps of our investigation, providing valuable information for future investigations in this field.

Wavelet analysis of the EMG signal to assess muscle fatigue in the lower extremities during symmetric movement on a rowing ergometer.

PURPOSE: The aim of this publication was to propose a method to determine changes in fatigue in selected muscle groups of the lower extremity during dynamic and cyclical motion performed on a rowing ergometer. The study aimed to use the discrete wavelet transform (DWT) to analyze electromyographic signals (EMG) recorded during diagnostic assessment of muscle and peripheral nerve electrical activity (electroneurography) using an electromyography device (EMG). METHODS: The analysis involved implementing calculations such as mean frequency (MNF) and median frequency (MDF) using the reconstructed EMG signal through DWT. The study examined the efficacy of DWT analysis in assessing muscle fatigue after physical exertion. RESULTS: The study obtained a negative regression coefficient for DWT analysis in all muscles except for the right gastrocnemius (GAS). The results suggest that DWT analysis can be an effective tool for evaluating muscle fatigue after physical exertion. CONCLUSIONS: The use of DWT in the analysis of EMG signals during rowing ergometer exercises has shown promising results in assessing muscle fatigue. However, additional investigations are necessary to confirm and expand these findings. This publication addresses the literature gap on the determination of muscle fatigue considering motion analysis on a rowing ergometer using the discrete wavelet transform. Previous studies have extensively compared and analyzed methods such as the Fourier transform (FFT), short-time Fourier transform (STFT), and wavelet transform (WT) for muscle fatigue analysis. However, no previous work has specifically examined the assessment of muscle fatigue by incorporating DWT analysis with motion analysis on a rowing ergometer.

Identification of muscle movements and activity by experimental methods for selected cases - stage#1.

PURPOSE: The aim of this study was to determine the position where the most activated and isolated individual muscles were. In the next steps, the selected limb positions will be used to determine the maximum values of isometric forces of the individual muscle heads based on the Hill model. METHODS: In order to determine the sought muscle activation, an electromyograph was used. Isometric contraction measurements were carried out for seven series of tests. Isometric contraction was performed as 100% MVC. RESULTS: For the long head of the biceps muscle, in the case of bending in the shoulder joint, angle of 75° was selected and for abduction in the shoulder joint - 90°. Internal rotation in the shoulder joint was omitted because of lower activation values. For the short head of the biceps muscle, the angle characterized by the greatest activity of the head was the angle of 115° in flexion at the elbow joint. The selected angle was 30° for shoulder extension and 110° for shoulder adduction. For the lateral head of the triceps brachial muscle, measurements showed that the angle at which the lateral head was most activated is 115°. CONCLUSIONS: The aim of this study was to determine the positions of the arm muscles that activate and isolate individual heads the most. The research presented and achieved results concern one specific person for whom a personalized numerical model was developed to represent the flexion-extension movement at the elbow joint. The performed tests can also be a preliminary assessment of the upper limb positions, for which wider conclusions could be drawn in the case of measurements on a larger number of participants.