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 firing 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 rheobase 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.
Many people have problems with elbow joint driving because of different diseases, sport trauma, road traffic injuries, etc. A good way for restoring elbow motions is a self-rehabilitation using an active orthosis. It has to be light, convenient for daily use, active, easy to control by patients, and safe. The paper presents a prototype of an active elbow orthosis. The plastic details were designed using a CAD system and were 3D printed. The joint was driven by a Dynamixel actuator. To increase the joint moment, a reduction gear-belt drive was constructed and applied. Experiments with six healthy subjects were performed using a Noraxon measuring system, aiming to investigate elbow joint angles in natural movements with four different velocities of elbow flexion and extension without and with a load of 0.5 kg in the hand. The four velocities (from very slow to very fast) were controlled by a specialized PC application. In order to achieve similar motions of the orthosis, the angle profiles were approximated so that the motion was between 10° and 120° because of electro/mechanical and software safety stoppers. Experiments were performed with the orthosis following the given angle profile without load and with a load of 0.5 kg. The results show that the orthosis’ forearm performs the given angle and angular speed profiles with enough precision.
Aim of the study was to determine physical activity's part of daily energy expenditure by heart rate monitoring and using data to control athletes' nutritional intake. Group of 10 male and 4 female wrestlers (age = 21 ± 1.8) at national level, who train 15 hours per week served as subjects in this investigation. The 72-hour HR recording was performed with a TEMEO cardiotelemetric system (made in Bulgaria). The energy expenditure during physical activity is determined by Method 1 of Hiilloskorpi et al. (2003). The determined daily energy expenditure is compared to the theoretically calculated. The deference is less than 100 kcal, but if subjects change the intensity, volume or duration of the workouts or increase their number, the difference will be much more evident.
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