Advances and perspectives of mechanomyography

Abstract

Introduction: The evaluation of muscular tissue condition can be accomplished with mechanomyography (MMG), a technique that registers intramuscular mechanical waves produced during a fi ber's contraction and stretching that are sensed or interfaced on the skin surface. Objective: Considering the scope of MMG measurements and recent advances involving the technique, the goal of this paper is to discuss mechanomyography updates and discuss its applications and potential future applications. Methods: Forty-three MMG studies were published between the years of 1987 and 2013. Results: MMG sensors are developed with different technologies such as condenser microphones, accelerometers, laser-based instruments, etc. Experimental protocols that are described in scientifi c publications typically investigated the condition of the vastus lateralis muscle and used sensors built with accelerometers, third and fourth order Butterworth fi lters, 5-100Hz frequency bandpass, signal analysis using Root Mean Square (RMS) (temporal), Median Frequency (MDF) and Mean Power Frequency (MPF) (spectral) features, with epochs of 1 s. Conclusion: Mechanomyographic responses obtained in isometric contractions differ from those observed during dynamic contractions in both passive and functional electrical stimulation evoked movements. In the near future, MMG features applied to biofeedback closed-loop systems will help people with disabilities, such as spinal cord injury or limb amputation because they may improve both neural and myoelectric prosthetic control. Muscular tissue assessment is a new application area enabled by MMG; it can be useful in evaluating the muscular tonus in anesthetic blockade or in pathologies such as myotonic dystrophy, chronic obstructive pulmonary disease, and disorders including dysphagia, myalgia and spastic hypertonia. New research becomes necessary to improve the effi ciency of MMG systems and increase their application in rehabilitation, clinical and other health areas.

Exploratory Outlier Detection for Acceleromyographic Neuromuscular Monitoring: Machine Learning Approach

“…The acceleration and angulation signals were collected through the open-source Cordova Plugin Device-motion library [ 20 ], and measure the movement of the muscle contractions in the 3 orthogonal directions of movement (X, Y, and Z). Similar to studies in mechanomyography [ 21 ], root-mean square analysis was performed on these signals to indicate the range of muscle displacement represented by its acceleration (expressed with units of m/s 2 ; Figure 1 ). The features for the model were then directly derived from the continuous monitoring signal.…”

Sensing Hand Interactions with Everyday Objects by Profiling Wrist Topography

“…With MMG, an accelerometer, microphone, or high-accuracy laser near a muscle can "hear" its motion, and even infer fatigue [69]. MMG has been deeply explored in medical literature (for a review see [38] or [34]), but has seen little adoption in HCI, in spite of the fact that it has been shown to have a higher signalto-noise ratio than electromyography (EMG) [22] and improved sensing in locations away from the muscle belly [4]. Earlier approaches measured skin deformation [15,20,27,60,70], but our band is tuned for fast-changing and highly-localized sub-millimeter wrist changes associated with grasping and interacting with objects, rather than global freehand gesture classification or on-body tapping.…”

“…We use the skin itself as a "plate": movement and deformation of the skin causes a change in d (and thus C). Unlike accelerometers used in MMG [34,38], capacitors can measure changes in d (e.g., vibration) without directly contacting the skin or dampening its motion. Laser-based range sensors can also measure displacement contact-free, but from a form factor perspective capacitive sensors are more compact than lasers.…”

Evaluation of Simple Microphone-based Mechanomyography (MMG) Probe Sets for Hand Stiffness Classification

“…As there are several challenges regarding how mechanomyographic signals could be acquired, there is no one standard method for signal acquisition and analysis. A review of mechanomyography sensor development is presented in [15,20]. Signals were read using accelerometers, piezometers or microphones, or combinations of such.…”

“…We use either Discrete Fourier Transform or Wavelet Packet Decomposition to form feature vectors for classi�ication. As the interesting signal is in the range between 5 -50 Hz [20] and to reduce the computational cost we can downsample the signal. The signal is twice downsampled using the decimate SciPy function [2] which consists of low-pass �iltering with FI� �ilter with Hamming window, and decimating (i.e., keeping every 8th sample) to selected frequency (256 samples per second).…”