Muscle fibre recruitment can respond to the mechanics of the muscle contraction

This study investigates the motor unit recruitment patterns between and within muscles of the triceps surae during cycling on a stationary ergometer at a range of pedal speeds and resistances. Muscle activity was measured from the soleus (SOL), medial gastrocnemius (MG) and lateral gastrocnemius (LG) using surface electromyography (EMG) and quantified using wavelet and principal component analysis. Muscle fascicle strain rates were quantified using ultrasonography, and the muscle–tendon unit lengths were calculated from the segmental kinematics. The EMG intensities showed that the body uses the SOL relatively more for the higher-force, lower-velocity contractions than the MG and LG. The EMG spectra showed a shift to higher frequencies at faster muscle fascicle strain rates for MG: these shifts were independent of the level of muscle activity, the locomotor load and the muscle fascicle strain. These results indicated that a selective recruitment of the faster motor units occurred within the MG muscle in response to the increasing muscle fascicle strain rates. This preferential recruitment of the faster fibres for the faster tasks indicates that in some circumstances motor unit recruitment during locomotion can match the contractile properties of the muscle fibres to the mechanical demands of the contraction

Optimal elbow angle for MMG signal classification of biceps brachii during dynamic fatiguing contraction

Mechanomyography (MMG) activity of the biceps muscle was recorded from thirteen subjects. Data was recorded while subjects performed dynamic contraction until fatigue. The signals were segmented into two parts (Non-Fatigue and Fatigue), An evolutionary algorithm was used to determine the elbow angles that best separate (using DBi) both Non-Fatigue and Fatigue segments of the MMG signal. Establishing the optimal elbow angle for feature extraction used in the evolutionary process was based on 70% of the conducted MMG trials. After completing twenty-six independent evolution runs, the best run containing the best elbow angles for separation (fatigue and non-fatigue) was selected and then tested on the remaining 30% of the data to measure the classification performance. Testing the performance of the optimal angle was undertaken on eight features that where extracted from each of the two classes (non-fatigue and fatigue) to quantify the performance. Results show that the elbow angles produced by the Genetic algorithm can be used for classification showing 80.64% highest correct classification for one of the features and on average of all eight features including worst performing features giving 66.50%