This research has proved that mechanomyographic (MMG) signals can be used for evaluating muscle performance. Stimulation of the lost physiological functions of a muscle using an electrical signal has been determined crucial in clinical and experimental settings in which voluntary contraction fails in stimulating specific muscles. Previous studies have already indicated that characterizing contractile properties of muscles using MMG through neuromuscular electrical stimulation (NMES) showed excellent reliability. Thus, this review highlights the use of MMG signals on evaluating skeletal muscles under electrical stimulation. In total, 336 original articles were identified from the Scopus and SpringerLink electronic databases using search keywords for studies published between 2000 and 2020, and their eligibility for inclusion in this review has been screened using various inclusion criteria. After screening, 62 studies remained for analysis, with two additional articles from the bibliography, were categorized into the following: (1) fatigue, (2) torque, (3) force, (4) stiffness, (5) electrode development, (6) reliability of MMG and NMES approaches, and (7) validation of these techniques in clinical monitoring. This review has found that MMG through NMES provides feature factors for muscle activity assessment, highlighting standardized electromyostimulation and MMG parameters from different experimental protocols. Despite the evidence of mathematical computations in quantifying MMG along with NMES, the requirement of the processing speed, and fluctuation of MMG signals influence the technique to be prone to errors. Interestingly, although this review does not focus on machine learning, there are only few studies that have adopted it as an alternative to statistical analysis in the assessment of muscle fatigue, torque, and force. The results confirm the need for further investigation on the use of sophisticated computations of features of MMG signals from electrically stimulated muscles in muscle function assessment and assistive technology such as prosthetics control.

The purpose of this study was to quantify and analyze the muscle activity of the biceps brachii (BB) muscle from three different male age groups and varying the electrode placement on their muscles. Six subjects in three different age groups (adolescents, vicenarian and tricenarian) participated in this study. The electrodes were placed on one of three locations on the upper arm BB: muscle belly (M), upper muscle of the belly (U) and lower muscle of the belly (L). The current study shows some significant differences of muscle activeness among the three age groups and the electrode placement location. It indicates that there is difference in BB activity between adolescents and vicenarians, and between vicenarians and tricenarians (p0.05). According to statistical analysis, vicenarians’ BBs are the most active, and then adolescents, and then tricenarians. The majority of the EMG results show that the muscle activity is highest in the lower portion of the muscle and decreases continuously up to the upper portion. Therefore, the present findings suggest that, EMG activity varies due to electrode placement and results are not similar for all age groups. Results are helpful for biceps rehabilitation, muscle coordination and other neuromuscular activities of the upper arms.

Image processing has been used widely in many areas but limited in facial rehabilitation area. Facial paralysis has been concerned recently due to the importance of face-to-face communication in our daily life. Patient with facial paralysis will have asymmetry of their face, difficult in doing their daily activities such as eating and drinking, and having physical and psychosocial distress due to their physical appearance. The objective of this paper is to show the importance of image processing methods on facial paralysis cases which have been less considerate among researchers and has provided overview on its application for facial measurement in facial rehabilitation area. A search of online databases such as PubMed, Elsevier, IEEE, Springer, ACM digital library and Medline was conducted in the period from July, 2011 to Jan, 2012 by using some keywords related to facial paralysis and image processing. 23 articles were satisfying the criteria which were chosen for the studies. The results of review shows many promising image processing methods are available in extracting 2D or 3D information acquired from images or videos which can be improved and also shows the developing fully automated rehabilitation system is possible in future.