Mechanomyography is more sensitive than EMG in detecting age-related sarcopenia.

The aim of this work was to investigate the effects of age-related sarcopenia on the time and frequency domain properties of lower extremity muscles' electromyographic and mechanomyographic activities. Healthy elderly (n=10, 64.5+/-4.5yr) and young (n=10, 22.6+/-2.8yr) were recruited as participants. Participants' lean thigh volumes (LTV) and 1 RM (one repetition maximum) leg strength of quadriceps and maximum speed knee extension with different load levels (45%, 60% and 75% 1 RM) were recorded. The root mean square (RMS) and the mean frequency (MF) of the surface electromyography (EMG(RMS), EMG(MF)) and mechanomyography (MMG(RMS), MMG(MF)) signals were collected at vastus lateralis during concentric contraction with different intensity levels. Compared to the young, the elderly had significantly less LTV, absolute and relative maximal force, as well as absolute and relative maximal power (p<.05). EMG(MF) of the elderly and the young increased monotonically from 45% to 75% 1 RM testing conditions. While the MMG(RMS) of the young increased with testing intensities, the MMG(RMS) of the elderly increased only from 45% to 60% but leveled off from 60% to 75% 1 RM testing conditions. The results indicate the declines of muscle mass, force and power production capacity with aging. The observations could be explained by neuromuscular performance and change of MU activation patterns may result from age-related sarcopenia. Aging affected muscle power more than muscle strength, which could be due to fast fiber reduction. This is supported by our observations that the MMG(RMS) differences between the young and the elderly across all three intensity level where EMG(RMS) was only different at the greatest intensity. We suggest that MMG could be used as an important measurement in studying muscle contraction in age-related sarcopenia.

Lower extremity joint torque predicted by using artificial neural network during vertical jump.

The purpose of this study was to develop an artificial neural network (ANN) for predicting lower extremity joint torques using the ground reaction force (GRF) and related parameters derived by the GRF during counter-movement jump (CMJ) and squat jump (SJ). Ten student athletes performed CMJ and SJ. Force plate and kinematic data were recorded. Joint torques were calculated using inverse dynamics and ANN. We used a fully connected, feed-forward network. The network comprised of one input layer, one hidden layer and one output layer. It was trained by error back-propagation algorithm using Steepest Descent Method. Input parameters of the ANN were GRF measurements and related parameters. Output parameters were three lower extremity joint torques. ANN model fitted well with the results of the inverse dynamics output. Our observations indicate that the model developed in this study can be used to estimate three lower extremity joint torques for CMJ and SJ based on ground reaction force data and related parameters.