OBJECTIVE: This study explores the factors, characteristics, and effects of MAP changes caused by KOA, providing a neuromuscular-based causal analysis for the rehabilitation treatment of KOA.
METHODS: Keywords including the association of MAP with KOA will be included. "Knee, Osteoarthritis, Electromyography(EMG), Muscle Activity patterns, activation amplitudes, activation time, Muscle Synergy, Co-contraction/activation" were used to search the databases of Science Direct, PubMed, Scopus, and Wiley. The criteria include studies from the past fifteen years that document changes in muscle contraction characteristics and causality analysis in patients with KOA. we compared MAP changes between individuals with and without KOA, such as the activation amplitudes, activation time, muscle synergy and co-contraction index(CCI). Additionally, we explored the potential relationship between muscle weakness, pain, and lower limb mechanical changes with the variations of MAP.
RESULTS: A total of 832 articles were reviewed, and 44 articles that met the inclusion criteria were selected for analysis. The changes in biomechanical structure, pain, and muscle atrophy may contribute to the formation and progression of the changes in MAP in KOA patients. In moderate KOA patients, the vastus lateralis (VL) and biceps femoris (BF) exhibits larger activation amplitudes, with earlier and longer activation times. The vastus medialis (VM) shows a delayed activation time relative to VL. Gastrocnemius activation time is prolonged during mid-gait, while the soleus exhibits lower activation amplitudes during the late stance phase. There are fewer, merged synergies with prolonged activation coefficients, and a higher percentage of unclassifiable synergies. Additionally, the CCI is positively correlated with task difficulty and symptoms. It is higher in the medial and lateral than hamstrings and quadriceps, and CCI specifically respond to joint stabilisation and load.
CONCLUSION: In patients with moderate KOA, changes in MAP are mainly related to symptoms and the difficulty of tasks. MAP changes primarily result in variations in amplitude, contraction duration, muscle synergy, and CCI. The MAP changes can subsequently affect the intermuscular structure, pain, joint loading, and stiffness.
CLINICAL IMPLICATIONS: These contribute to the progression of KOA and create a vicious cycle that accelerates disease advancement. Clinical rehabilitation treatments can target the MAP changes to break the cycle and help mitigate disease progression.
OBJECTIVE: In order to address this issue, we analyzed how leg muscle activity is related to the variations of the path of movement.
METHOD: Since the electromyography (EMG) signal is a feature of muscle activity and the movement path has complex structures, we used entropy analysis in order to link their structures. The Shannon entropy of EMG signal and walking path are computed to relate their information content.
RESULTS: Based on the obtained results, walking on a path with greater information content causes greater information content in the EMG signal which is supported by statistical analysis results. This allowed us to analyze the relation between muscle activity and walking path.
CONCLUSION: The method of analysis employed in this research can be applied to investigate the relation between brain or heart reactions and walking path.
OBJECTIVE: In this research, for the first time, we investigate how facial muscle reaction is related to the reaction of the human brain.
METHODS: Since both electromyography (EMG) and electroencephalography (EEG) signals, as the features of muscle and brain activities, contain information, we benefited from the information theory and computed the Shannon entropy of EMG and EEG signals when subjects were exposed to different static visual stimuli with different Shannon entropies (information content).
RESULTS: Based on the obtained results, the variations of the information content of the EMG signal are related to the variations of the information content of the EEG signal and the visual stimuli. Statistical analysis also supported the results indicating that the visual stimuli with greater information content have a greater effect on the variation of the information content of both EEG and EMG signals.
CONCLUSION: This investigation can be further continued to analyze the relationship between facial muscle and brain reactions in case of other types of stimuli.
Methods: A total of eighteen (18) malocclusion patients were identified. Malocclusion patients were subdivided into 3 groups based on the bracket selection (conventional, self-ligating, and ceramic bracket) with 6 patients for each group. sEMG of muscles were done using a two-channel electromyography device, where pregelled and self-adhesive electrodes (bilateral) were applied. Chewing and clenching of masseter and temporalis muscle activity were recorded for 20 s pre and 6 months of orthodontic treatment using sEMG (frequency 60 Hz). The data were analysed by using repeated measures ANOVA in IBM SPSS Statistics Version 24.0.
Results: Chewing and clenching for masseter muscle showed no significant difference (P > 0.05) in sEMG activity of three types of the brackets. However, for temporalis muscle, there was a significant difference found in sEMG activity during chewing (P < 0.05) and clenching (P < 0.05) between these three brackets.
Conclusion: The activity of temporalis muscle showed significant changes in chewing and clenching, where the conventional group demonstrated better muscle activity pre and at six months of fixed appliances.
OBJECTIVE: The aim of this study was to compare the activity and relationship between surface EMG and static force from the BB muscle in terms of three sensor placement locations.
METHODS: Twenty-one right hand dominant male subjects (age 25.3±1.2 years) participated in the study. Surface EMG signals were detected from the subject's right BB muscle. The muscle activation during force was determined as the root mean square (RMS) electromyographic signal normalized to the peak RMS EMG signal of isometric contraction for 10 s. The statistical analysis included linear regression to examine the relationship between EMG amplitude and force of contraction [40-100% of maximal voluntary contraction (MVC)], repeated measures ANOVA to assess differences among the sensor placement locations, and coefficient of variation (CoV) for muscle activity variation.
RESULTS: The results demonstrated that when the sensor was placed on the muscle belly, the linear slope coefficient was significantly greater for EMG versus force testing (r2=0.62, P<0.05) than when placed on the lower part (r2=0.31, P>0.05) and upper part of the muscle belly (r2=0.29, P<0.05). In addition, the EMG signal activity on the muscle belly had less variability than the upper and lower parts (8.55% vs. 15.12% and 12.86%, respectively).
CONCLUSION: These findings indicate the importance of applying the surface EMG sensor at the appropriate locations that follow muscle fiber orientation of the BB muscle during static contraction. As a result, EMG signals of three different placements may help to understand the difference in the amplitude of the signals due to placement.
RESULTS: The study included 24 participants and examined three muscles (m. Orbicularis Oris, m. Zygomaticus Major, and m. Mentalis) during five different facial expressions. Prior to thorough statistical analysis, features were extracted from the acquired electromyographs. Finally, classification was done with the use of logistic regression, random forest classifier and linear discriminant analysis. A statistically significant difference in muscle activity amplitudes was demonstrated between muscles, enabling the tracking of individual muscle activity for diagnostic and therapeutic purposes. Additionally other time domain and frequency domain features were analyzed, showing statistical significance in differentiation between muscles as well. Examples of pattern recognition showed promising avenues for further research and development.
CONCLUSION: Surface electromyography is a useful method for assessing the function of facial expression muscles, significantly contributing to the diagnosis and treatment of oral motor function disorders. Results of this study show potential for further research and development in this field of research.