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Mechanomyography (MMG) is extensively used in the research of sensor development, signal processing, characterization of muscle activity, development of prosthesis and/or switch control, diagnosis of neuromuscular disorders, and as a medical rehabilitation tool. Despite much existing MMG research, there has been no systematic review of these. This paper aims to determine the current status of MMG in sensor development, related signal processing, and applications. Six electronic databases were extensively searched for potentially eligible studies published between 2003 and 2012. From a total of 175 citations, 119 were selected for full-text evaluation and 86 potential studies were identified for further analysis. This systematic review initially reveals that the development of accelerometers for MMG is still in the initial stage. Another important finding of this paper is that sensor placement location on muscles may influence the MMG signal. In addition, we observe that the majority of research processes MMG signals using wavelet transform. Time/frequency domain analysis of MMG signals provides useful information to examine muscle. In addition, we find that MMG may be applied to diagnose muscle conditions, to control prosthesis and/or switch devices, to assess muscle activities during exercises, to study motor unit activity, and to identify the type of muscle fiber. Finally, we find that the majority of the studies use accelerometers as sensors for MMG measurements. We also observe that currently MMG-based rehabilitation is still in a nascent stage. In conclusion, we recommend further improvements of MMG in the areas of sensor development, particularly on accelerometers, and signal processing aspects, as well as increasing future applications of the technique in prosthesis and/or switch control, clinical practices, and rehabilitation.

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Abstract-The need to find ways to effectively utilize the large quantities of agricultural waste that are produced is indicative of the huge potential associated with producing an alternative pyramidal microwave absorber for anechoic chamber-testing applications. We propose the development of a pyramidal microwave absorber that can use sugar cane bagasse (SCB), a byproduct from the production and processing of sugar cane, as the absorbent. In this paper, we report the results of our use of dielectric probe measurement to determine the dielectric constant and loss tangent of SCB. These values were used to model and simulate an SCB pyramidal microwave absorber in Computer Simulation Technology's (CST's) Microwave Studio. This absorber was operated in the microwave frequency range between 0.1 GHz and 20.0 GHz.

PurposeThis study aimed: i) to examine the relationship between the magnitude of cross-talk in mechanomyographic (MMG) signals generated by the extensor digitorum (ED), extensor carpi ulnaris (ECU), and flexor carpi ulnaris (FCU) muscles with the sub-maximal to maximal isometric grip force, and with the anthropometric parameters of the forearm, and ii) to quantify the distribution of the cross-talk in the MMG signal to determine if it appears due to the signal component of intramuscular pressure waves produced by the muscle fibers geometrical changes or due to the limb tremor.MethodsTwenty, right-handed healthy men (mean ± SD: age  = 26.7±3.83 y; height  = 174.47±6.3 cm; mass  = 72.79±14.36 kg) performed isometric muscle actions in 20% increment from 20% to 100% of the maximum voluntary isometric contraction (MVIC). During each muscle action, MMG signals generated by each muscle were detected using three separate accelerometers. The peak cross-correlations were used to quantify the cross-talk between two muscles.ResultsThe magnitude of cross-talk in the MMG signals among the muscle groups ranged from, R2x, y = 2.45–62.28%. Linear regression analysis showed that the magnitude of cross-talk increased linearly (r2 = 0.857–0.90) with the levels of grip force for all the muscle groups. The amount of cross-talk showed weak positive and negative correlations (r2 = 0.016–0.216) with the circumference and length of the forearm respectively, between the muscles at 100% MVIC. The cross-talk values significantly differed among the MMG signals due to: limb tremor (MMGTF), slow firing motor unit fibers (MMGSF) and fast firing motor unit fibers (MMGFF) between the muscles at 100% MVIC (p<0.05, η 2 = 0.47–0.80).SignificanceThe results of this study may be used to improve our understanding of the mechanics of the forearm muscles during different levels of the grip force.

Even though the amount of rehabilitation guidelines has never been greater, uncertainty continues to arise regarding the effi ciency and eff ectiveness of the rehabilitation of gait disorders. Th is question has been hindered by the lack of information on accurate measurements of gait disorders. Th us, this article reviews the rehabilitation systems for gait disorder using vision and non-vision sensor technologies, as well as the combination of these. All papers published in the English language between  and June,  that had the phrases "gait disorder" , "rehabilitation" , "vision sensor" , or "non vision sensor" in the title, abstract, or keywords were identifi ed from the SpringerLink, ELSEVIER, PubMed, and IEEE databases. Some synonyms of these phrases and the logical words "and" , "or" , and "not" were also used in the article searching procedure. Out of the  published articles found, this review identifi ed  articles that described the rehabilitation of gait disorders using diff erent types of sensor technologies. Th is literature set presented strong evidence for the development of rehabilitation systems using a markerless visionbased sensor technology. We therefore believe that the information contained in this review paper will assist the progress of the development of rehabilitation systems for human gait disorders.

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The objective of the present study was to investigate the time to fatigue and compare the fatiguing condition among the three heads of the triceps brachii muscle using surface electromyography during an isometric contraction of a controlled forceful hand grip task with full elbow extension. Eighteen healthy subjects concurrently performed a single 90 s isometric contraction of a controlled forceful hand grip task and full elbow extension. Surface electromyographic signals from the lateral, long and medial heads of the triceps brachii muscle were recorded during the task for each subject. The changes in muscle activity among the three heads of triceps brachii were measured by the root mean square values for every 5 s period throughout the total contraction period. The root mean square values were then analysed to determine the fatiguing condition for the heads of triceps brachii muscle. Muscle fatigue in the long, lateral, and medial heads of the triceps brachii started at 40 s, 50 s, and 65 s during the prolonged contraction, respectively. The highest fatiguing rate was observed in the long head (slope = −2.863), followed by the medial head (slope = −2.412) and the lateral head (slope = −1.877) of the triceps brachii muscle. The results of the present study concurs with previous findings that the three heads of the triceps brachii muscle do not work as a single unit, and the fiber type/composition is different among the three heads.