The muscle force is the sum of forces of multiple motor units (MUs), which have different contractile properties. During movements, MUs develop unfused tetani, which result from summation of twitch-shape responses to individual stimuli, which are variable in amplitude and duration. The aim of the study was to develop a realistic muscle model that would integrate previously developed models of MU contractions and an algorithm for the prediction of tetanic forces. The proposed model of rat medial gastrocnemius muscle is based on physiological data: excitability and tiring frequencies of motoneurons, contractile properties, and the number and proportion of MUs in the muscle. The MU twitches were modeled by a six-parameter analytical function. The excitability of motoneurons was modeled according to a distribution of their rheo-base currents measured experimentally. Processes of muscle force regulation were modeled according to a common drive hypothesis. The excitation signal to motoneurons was modeled by two form types: triangular and trapezoid. The discharge frequencies of MUs, calculated individually for each MU, corresponded to those recorded for rhythmic firing of motoneurons. The force of the muscle was calculated as the sum of all recruited MUs. Participation of the three types of MUs in the developed muscle force was presented at different levels of the excitation signal to motoneurons. The model appears highly realistic and open for input data from various skeletal muscles with different compositions of MU types. The results were compared with three other models with different distribution of the input parameters. NEW & NOTEWORTHY The proposed mathematical model of rat medial gastrocnemius muscle is highly realistic because it is based strictly on experimentally determined motor unit contractile parameters and motoneuron properties. It contains the actual number and proportion of motor units and takes into consideration their different contributions to the whole muscle force, depending on the level of the excitation signal. The model is open for input data from other muscles, and additional physiological parameters can also be included.

Trunk muscle electromyographic (EMG) signals are often contaminated by the electrical activity of the heart. During low or moderate muscle force, these electrocardiographic (ECG) signals disturb the estimation of muscle activity. Butterworth high-pass filters with cut-off frequency of up to 60 Hz are often used to suppress the ECG signal. Such filters disturb the EMG signal in both frequency and time domain. A new method based on the dynamic application of Savitzky-Golay filter is proposed. EMG signals of three left trunk muscles and pure ECG signal were recorded during different motor tasks. The efficiency of the method was tested and verified both with the experimental EMG signals and with modeled signals obtained by summing the pure ECG signal with EMG signals at different levels of signal-to-noise ratio. The results were compared with those obtained by application of high-pass, 4th order Butterworth filter with cut-off frequency of 30 Hz. The suggested method is separating the EMG signal from the ECG signal without EMG signal distortion across its entire frequency range regardless of amplitudes. Butterworth filter suppresses the signals in the 0-30 Hz range thus preventing the low-frequency analysis of the EMG signal. An additional disadvantage is that it passes high-frequency ECG signal components which is apparent at equal and higher amplitudes of the ECG signal as compared to the EMG signal. The new method was also successfully verified with abnormal ECG signals.

Electromyography (EMG) is a widely used method for estimating muscle activity and could help in understanding how muscles interact with each other and affect human movement control. To detect muscle interactions during elbow flexion and extension, a recently developed InterCriteria Analysis (ICrA) based on the mathematical formalisms of index matrices and intuitionistic fuzzy sets is applied. ICrA has had numerous implementations in different fields, including biomedicine and quality of life; however, this is the first time the approach has been used for establishing muscle interactions. Six human upper arm large surface muscles or parts of muscles responsible for flexion and extension in shoulder and elbow joints were selected. Surface EMG signals were recorded from four one-joint (pars clavicularis and pars spinata of m. deltoideus [DELcla and DELspi, respectively], m. brachialis [BRA], and m. anconeus [ANC]) and two two-joint (m. biceps brachii [BIC] and m. triceps brachii-caput longum [TRI]) muscles. The outcomes from ten healthy subjects performing flexion and extension movements in the sagittal plane at four speeds with and without additional load are implemented in this study. When ICrA was applied to examine the two different movements, the BIC–BRA muscle interaction was distinguished during flexion. On the other hand, when the ten subjects were observed, four interacting muscle pairs, namely DELcla-DELspi, BIC-TRI, BIC-BRA, and TRI-BRA, were detected. The results obtained after the ICrA application confirmed the expectations that the investigated muscles contribute differently to the human upper arm movements when the flexion and extension velocities are changed, or a load is added.

The aim of this paper was to investigate in detail the biomechanics of the knee during different static loadings on the spine using electromyographic (EMG) signals from six main surface muscles acting in the knee joint; three components of the ground reaction force measured by a force plate; knee flexion joint angle measured by a flexible goniometer; and the distances between the bones (femur and tibia) forming the knee joint measured by an echograph. The measurements were taken without weight (reference straight position) and with a weight of 2, 5, 10, 15, 17, and 20 kg placed in a rucksack on the spine. The results showed that the forces in the horizontal and sagittal planes were negligible, and the reaction in the frontal plane increased and was linearly dependent on the carrying weight. The distance between bones decreased linearly with increasing weight for all participants from 3.94% to 53.92% from the referent position. The knee angle varied and in many cases decreased with increasing weight. The calculated correlation coefficients between mean EMG signals and loading weight showed that the adjustment of different subjects’ musculature to increasing load is individual. In general, knee joint balance is a dynamic individual process.

The InterCriteria decision-making approach, known as InterCriteria analysis (ICrA), was applied here to optimize the experimental protocol when the surface electromyography (EMG) signals of upper arm human muscles are recorded. Ten healthy subjects performed cycling movements in the sagittal plane with and without added weight for ten, six, two, and one second, respectively, for each active phase. The EMG signals from six muscles or parts of muscles, namely m. deltoideus pars clavicularis and pars spinata, m. brachialis, m. anconeus, m. biceps brachii, and m. triceps brachii caput longum, were recorded. ICrA was used on the obtained data to find correlations between the sixteen different phases, eight for elbow flexion and eight for elbow extension. Based on the obtained results, we proposed an optimized experimental protocol (OEP) that omits slower and more difficult tasks while saving crucial data. The optimized protocol consists of seven, instead of ten, tasks and takes three minutes less than the time taken for the full experimental protocol (FEP). The lower number of movements in the OEP could prevent physical and psychical fatigue, discomfort, or even pain in the investigated subjects. In addition, the time to train subjects, as well as the time to process the surface EMG data, can be significantly reduced.