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In recent years, musculoskeletal computation has become a widely used tool to investigate joint and muscle forces within the human body. However, the issue of muscle fatigue is not considered adequately in most models and is a challenging task. One aspect that needs to be examined is the interaction of muscles during an exhausting task. Therefore, an experimental study was designed to analyze the changes of back muscle recruitment pattern during such exercises.
In this study 38 subjects (27 male, 11 female, height = 177±8.5 cm, weight = 74.0±13.6 kg) participated. Each subject had to perform three static and three dynamic exhausting exercises where the back muscles were loaded with subject specific forces using a dynamometer adapter especially designed for the trunk muscles. To collect the muscle activity, twelve surface electromyography sensors were applied on the back, and four on the abdominal muscles. Muscle activity and fatigue were analyzed by calculating the maximum voluntary contraction normalized signal and the median frequency. At first the fatigue of m. erector spinae and m. multifidi was analyzed, since these muscles carry the main load during the exercises. Subsequently the activity of the m. trapezius, m. rectus abdominis and m. obliquus externus were investigated to determine recruitment patterns. To gain more detailed information of these patterns a numerical model was built using the AnyBody Modeling System™. Analyzing the measurements, we can observe an increasing muscle activity during isokinetic exercises while the force is constant. Since the activity in the simulation is defined as the current force output divided by the strength of the muscle, the strength parameter was scaled down based on the measured data, assuming a linear force – activity correlation, and using a numerical algorithm considering the influence of cross talk.
The results show, that changes in recruitment pattern can be divided into three major subgroups. Prior to total exhaustion, some of the subjects show additional activation of muscles in the trapezius region, while other subjects show an additional activation of abdominal muscles, increasing the intra-abdominal pressure which supports the spine. In the third group an activation in both regions can be observed.
The numerical simulations show an increasing activity of abdominal muscles as well as muscles in the upper back. Especially the m. latissimus dorsi shows a significantly higher activity.
The results lead to the conclusion that prior to total exhaustion, additional muscles are recruited to support the main muscles. It was shown that abdominal muscles are activated to support back muscles by pressurizing the trunk cavity to delay total exhaustion as long as possible. In conclusion, the results show that changes in muscle recruitment pattern need to be considered when introducing muscle fatigue to musculoskeletal models.
BACKGROUND:
Validation and verification of multibody musculoskeletal models sEMG is a difficult process because of the reliability of sEMG data and the complex relationship of muscle force and sEMG.
OBJECTIVE:
This work aims at comparing experimentally recorded and simulated muscle activities considering a numerical model for crosstalk. METHODS:
For providing an experimentally derived reference data set, subjects were performing elevations of the arm, where the activities of the contemplated muscle groups were measured by sEMG sensors. Computed muscle activities were further processed and transformed into an artificial electromyographical signal, which includes a numerical crosstalk model. In order to determine whether the crosstalk model provides a better agreement with the measured muscle activities, the Pearson correlation coefficient has been computed as a qualitative way of assessing the curve progression of the data sets.
RESULTS:
The results show an improvement in the correlation coefficient between the experimental data and the simulated muscle activities when taking crosstalk into account.
CONCLUSIONS:
Although the correlation coefficient increased when the crosstalk model was utilized, it is questionable if the discretization of both, the crosstalk and the musculoskeletal model, is accurate enough.
BACKGROUND: Validation and verification of multibody musculoskeletal models sEMG is a difficult process because of the reliability of sEMG data and the complex relationship of muscle force and sEMG. OBJECTIVE: This work aims at comparing experimentally recorded and simulated muscle activities considering a numerical model for crosstalk. METHODS: For providing an experimentally derived reference data set, subjects were performing elevations of the arm, where the activities of the contemplated muscle groups were measured by sEMG sensors. Computed muscle activities were further processed and transformed into an artificial electromyographical signal, which includes a numerical crosstalk model. In order to determine whether the crosstalk model provides a better agreement with the measured muscle activities, the Pearson correlation coefficient has been computed as a qualitative way of assessing the curve progression of the data sets. RESULTS: The results show an improvement in the correlation coefficient between the experimental data and the simulated muscle activities when taking crosstalk into account. CONCLUSIONS: Although the correlation coefficient increased when the crosstalk model was utilized, it is questionable if the discretization of both, the crosstalk and the musculoskeletal model, is accurate enough.