Abstract
Mechanomyography (MMG) is a technique of recording muscles activity that may be considered a suitable choice for human–machine interfaces (HMI). The design of sensors used for MMG and their spatial distribution are among the deciding factors behind their successful implementation to HMI. We present a new design of a MMG sensor, which consists of two coupled piezoelectric discs in a single housing. The sensor’s functionality was verified in two experimental setups related to typical MMG applications: an estimation of the force/MMG relationship under static conditions and a neural network-based gesture classification. The results showed exponential relationships between acquired MMG and exerted force (for up to 60% of the maximal voluntary contraction) alongside good classification accuracy (94.3%) of eight hand motions based on MMG from a single-site acquisition at the forearm. The simplification of the MMG-based HMI interface in terms of spatial arrangement is rendered possible with the designed sensor.
Highlights
Mechanomyography (MMG) is a measurement technique used to record muscles activity based on vibrations arising as an effect of muscle fibers mechanical contractions [1,2,3].This technique is still less popular, especially in clinical applications, compared to electromyography (EMG)
MMG signals recorded during step isometric measurements. (A) root mean square (RMS) values for both of the sensor discs are plotted against the percentage of maximum voluntary contraction (MVC)
An interesting feature of the presented sensor is that for force levels below 20% maximal voluntary contraction (MVC), the rate of MMG RMS increase is lower for the external disc than the internal one
Summary
Mechanomyography (MMG) is a measurement technique used to record muscles activity based on vibrations arising as an effect of muscle fibers mechanical contractions [1,2,3]. This technique is still less popular, especially in clinical applications, compared to electromyography (EMG). The determinants of proper MMG implementation are actively investigated, among others, in order to eliminate crosstalk from neighboring muscles, improve repeatability and signal-to-noise ratio of acquired signals [17,18,19]
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