Mechanomyographic determination of post-activation potentiation in myopathies

Assessment of Skeletal Muscle Contractile Properties by Radial Displacement: The Case for Tensiomyography

Skeletal muscle operates as a near-constant volume system; as such muscle shortening during contraction is transversely linked to radial deformation. Therefore, to assess contractile properties of skeletal muscle, radial displacement can be evoked and measured. Mechanomyography measures muscle radial displacement and during the last 20 years, tensiomyography has become the most commonly used and widely reported technique among the various methodologies of mechanomyography. Tensiomyography has been demonstrated to reliably measure peak radial displacement during evoked muscle twitch, as well as muscle twitch speed. A number of parameters can be extracted from the tensiomyography displacement/time curve and the most commonly used and reliable appear to be peak radial displacement and contraction time. The latter has been described as a valid non-invasive means of characterising skeletal muscle, based on fibre-type composition. Over recent years, applications of tensiomyography measurement within sport and exercise have appeared, with applications relating to injury, recovery and performance. Within the present review, we evaluate the perceived strengths and weaknesses of tensiomyography with regard to its efficacy within applied sports medicine settings. We also highlight future tensiomyography areas that require further investigation. Therefore, the purpose of this review is to critically examine the existing evidence surrounding tensiomyography as a tool within the field of sports medicine.

Development of multichannel array transducer of displacement mechanical-myogram

The myoelectric signal (EMG) recorded on the skin surface is a time-related and spatial aggregate of the action potentials of motor units of skeletal muscle, and it indicates the input information for muscle contraction. The mechano-myographic signal (MMG) is a vibration of skin/muscle surface caused by muscle contraction and it directly reflects the activity of muscle contraction. The two-dimensional generation mechanism of MMG is still n ot clarified in detail. In this study, the displacement MMGs were recorded at 25 measuring points using 5 × 5 displacement MMG array transducer within 50× 50 mm, while the motor point of the biceps brachii was electro-stimulated. The spatial propagation map of twitch waveform of displacement MMG was drawn.

Stationarity distributions of mechanomyogram signals from isometric contractions of extrinsic hand muscles during functional grasping

This study investigates the stationarity of steady state mechanomyogram signals for the purpose of determining appropriate features for signal classification. Mechanomyography is the superficial recording of low frequency vibrations detected over contracting muscles. Steady state mechanomyogram signals, recorded at the belly of the extensor digitorum, flexor digitorum superficialis and flexor pollicis longus muscles during functional grasps were tested for weak stationarity. Twenty percent of the contractions were found to be non-stationary, indicating that time frequency methods may be appropriate for automatic pattern recognition of functional grasp from the mechanomyogram. The distribution of the stationary test statistic was dependent on the type of muscle contractions, suggesting that the test statistic itself might be a discriminating feature for mechanomyogram pattern recognition in applications such as multifunction prosthetic control. Since the major known source of non-stationarity was decreasing variance, it is suggested that shifts in the distribution of the test statistic may indicate the time course of relative muscle contributions to functional grasp.

Vision-based segmentation of continuous mechanomyographic grasping sequences

In detecting motor related activity from mechanomyographic (MMG) recordings, the acquisition of long, continuous streams of MMG signals is typically preferred over the painstaking collection of individual, isolated contractions. However, a major challenge with continuous collection is the subsequent separation of the MMG data stream into segments representing individual contractions. This paper proposes a method for segmenting continuously recorded MMG data streams using computer vision while providing a highly reduced set of key images for rapid human expert verification. Transverse plane video recordings of functional grasp sequences were synchronized with the acquisition of MMG signals from the forearm. An automatic, vision-based algorithm exploiting skin color detection, motion estimation, and template matching provided segmentation cues for MMG signals arising from multiple grips. The automatic segmentation method tolerated extraneous hand movements, differentiated among multiple grips and estimated grip transition times. Our implementation segmented two grips with an average accuracy of 97.8 -/+ 4%, and up to seven grips with an accuracy of 73 -/+ 20%. The automatically extracted contraction initiation and termination times were within 173 -/+ 133 ms of the times obtained via manual segmentation. It is suggested that the proposed method would be particularly conducive to the assembly of large collections of signals for training MMG-driven prostheses.