An in-depth understanding of the mechanism of lower extremity muscle coordination during walking is the key to improving the efficacy of gait rehabilitation in patients with neuromuscular dysfunction. This paper investigates the effect of changes in walking speed on lower extremity muscle synergy patterns and muscle functional networks. Eight healthy subjects were recruited to perform walking tasks on a treadmill at three different speeds, and the surface electromyographic signals (sEMG) of eight muscles of the right lower limb were collected synchronously. The non-negative matrix factorization (NNMF) method was used to extract muscle synergy patterns, the mutual information (MI) method was used to construct the alpha frequency band (8–13 Hz), beta frequency band (14–30 Hz) and gamma frequency band (31–60 Hz) muscle functional network, and complex network analysis methods were introduced to quantify the differences between different networks. Muscle synergy analysis extracted 5 muscle synergy patterns, and changes in walking speed did not change the number of muscle synergy, but resulted in changes in muscle weights. Muscle network analysis found that at the same speed, high-frequency bands have lower global efficiency and clustering coefficients. As walking speed increased, the strength of connections between local muscles also increased. The results show that there are different muscle synergy patterns and muscle function networks in different walking speeds. This study provides a new perspective for exploring the mechanism of muscle coordination at different walking speeds, and is expected to provide theoretical support for the evaluation of gait function in patients with neuromuscular dysfunction.
Objective To evaluate the effectiveness and safety of postoperative intraperitoneal hyperthermic perfusion chemotherapy (IHPC) for advanced gastric cancer, so as to provide references for clinical practice and study. Methods The following databases including The Cochrane Library, PubMed, EMbase, CBM, CNKI, VIP and WanFang were searched on computer, and other searches were also performed to collect all relevant randomized controlled trials (RCTs) on postoperative IHPC versus intravenous chemotherapy alone (IC) for advanced gastric cancer. The quality of the included studies was assessed according to Cochrane Handbook 5.1 for Systematic Review, and Meta-analysis was conducted by using RevMan 5.1 software. Results A total of 18 RCTs involving 2299 patients were included. The results of meta-analyses showed that: a) Efficacy evaluation: There were significant differences between the IHPC group and the IC group in 1-, 2-, 3-, and 5-year survival rate, 3- and 5-year recurrence rate, and 3- and 5-year distant metastasis rate; the OR value and 95%CI were 1.88 (1.49, 2.39), 2.45 (1.64, 3.67), 2.29 (1.92, 2.73), 2.17 (1.70, 2.76), 0.39 (0.29, 0.52), 0.54 (0.40, 0.72), 0.55 (0.38, 0.78), 0.58 (0.42, 0.81), respectively; b) Safety evaluation: There were significant differences between the IHPC group and the IC group in the incidence of abdominal pain, abdominal distension, nausea and vomiting; the OR value and 95%CI were 2.20 (1.58, 3.07), 7.00 (2.67, 18.36), 0.65 (0.45, 0.95), respectively. But there were no significant differences between the IHPC group and the IC group in the incidence of alopecia, ileus, bone marrow inhibition, and hepatic lesion. Conclusion Compared with IC, postoperative IHPC+IC can improve survival rate and reduce the recurrence and distant metastasis rate; additionally, it is safe and feasible, so it is recommended that the detailed condition of patients should be taken into consideration when the postoperative IHPC+IC therapy is applied to clinic.
Walking is a fundamental component of daily human activity, in which orderly execution of key gait events [such as heel strike (HS) and toe-off (TO) ] is essential for maintaining gait coordination and stability. However, the underlying brain–muscle neural coordination mechanisms associated with these events remain unclear. In this study, electroencephalography (EEG) signals from 19 channels and surface electromyography (sEMG) signals from 14 lower-limb muscles were synchronously recorded from 18 healthy participants during steady-state walking at a constant speed of 3.2 km/h. Brain–muscle connectivity networks were constructed using the adaptive directed transfer function (ADTF), and the dynamic connectivity characteristics associated with HS and TO events were quantitatively analyzed. The results showed that brain–muscle connectivity was strongest in the β frequency band. Intra-brain (EEG-EEG), brain-to-muscle (EEG-sEMG), and intra-muscle (sEMG-sEMG) connections were significantly stronger than muscle-to-brain (sEMG-EEG) connections. At both HS and TO events, information exchange between frontal-central cortical regions and the muscles of the supporting leg was markedly enhanced. Furthermore, compared with TO, the brain-muscle network at HS exhibited higher clustering coefficient, global efficiency, and betweenness centrality. These findings suggest that brain-muscle interactions during walking are predominantly mediated in the β band and dynamically modulated by gait events. Accurate characterization of gait event-related brain-muscle connectivity may provide important technical support for clarifying the neuromuscular coordination mechanisms underlying fine gait control.
Muscle fatigue has widespread application in the field of rehabilitation medicine. The paper studies the muscle fatigue using surface electromyogram (sEMG) in the background of rehabilitation training system. The sEMG and ventilatory threshold of vastus lateralis, rectus femoris and erector spinae are collected synchronously and the electromyogram fatigue threshold (EMGFT) of different sEMG was analyzed by increasing load cycling experiments of 10 healthy subjects. This paper also analyzes the effect of isotonic and isometric contraction on EMGFT. Results showed that the appeared time of EMGFT was earlier than that of ventilatory threshold in the incremental load cycling. While the differences were subtle and EMGFT was verified to be effective. EMGFT has been proven effective for different muscle contraction by comparing the EMGFT of vastus lateralis and erector spinae. EMGFT could be used to keep muscle injuries from overtraining in the process of rehabilitation. Therefore, EMGFT has a great significance for femoral shaft fractures’s fatigue monitoring in rehabilitation training.
Gesture imitation is a common rehabilitation strategy in limb rehabilitation training. In traditional rehabilitation training, patients need to complete training actions under the guidance of rehabilitation physicians. However, due to the limited resources of the hospital, it cannot meet the training and guidance needs of all patients. In this paper, we proposed a following control method based on Kinect and NAO robot for the gesture imitation task in rehabilitation training. The method realized the joint angles mapping from Kinect coordination to NAO robot coordination through inverse kinematics algorithm. Aiming at the deflection angle estimation problem of the elbow joint, a virtual space plane was constructed and realized the accurate estimation of deflection angle. Finally, a comparative experiment for deflection angle of the elbow joint angle was conducted. The experimental results showed that the root mean square error of the angle estimation value of this method in right elbow transverse deflection and vertical deflection directions was 2.734° and 2.159°, respectively. It demonstrates that the method can follow the human movement in real time and stably using the NAO robot to show the rehabilitation training program for patients.
The functional coupling between motor cortex and effector muscles during autonomic movement can be quantified by calculating the coupling between electroencephalogram (EEG) signal and surface electromyography (sEMG) signal. The maximal information coefficient (MIC) algorithm has been proved to be effective in quantifying the coupling relationship between neural signals, but it also has the problem of time-consuming calculations in actual use. To solve this problem, an improved MIC algorithm was proposed based on the efficient clustering characteristics of K-means ++ algorithm to accurately detect the coupling strength between nonlinear time series. Simulation results showed that the improved MIC algorithm proposed in this paper can capture the coupling relationship between nonlinear time series quickly and accurately under different noise levels. The results of right dorsiflexion experiments in stroke patients showed that the improved method could accurately capture the coupling strength of EEG signal and sEMG signal in the specific frequency band. Compared with the healthy controls, the functional corticomuscular coupling (FCMC) in beta (14~30 Hz) and gamma band (31~45 Hz) were significantly weaker in stroke patients, and the beta-band MIC values were positively correlated with the Fugl-Meyers assessment (FMA) scale scores. The method proposed in this study is hopeful to be a new method for quantitative assessment of motor function for stroke patients.