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Exosomal ANXA2 based on ovarian most cancers tissues regulates epithelial-mesenchymal plasticity involving individual peritoneal mesothelial tissues.
In this paper, a perception-empathy biofeedback (PEBF) system is proposed that supplements the foot pressure status of a paralyzed foot with a wearable vibrotactile biofeedback (BF) vest to the back. Improvements in the ankle dorsiflexion and push-off movement in the swing phase and pre-swing phase, respectively, can be expected after using the proposed system. However, the results of the 3 week pilot clinical tests suggest that significant improvement is only observed for the push-off movement. It is assumed that the attention required to recognize the BF was beyond the ability of the patients. In this paper, a dual task (40 s walking and performing mental arithmetic at the same time) was conducted with the following conditions no vibrations and providing BF to the lower back and the entire back. According to the results, the ankle joint angle of the paralyzed side at push-off under the entire back condition is statistically significant (p = 0.0780); however, there are no significant changes under the lower back condition (p = 0.4998). Moreover, the ankle joint angle of the paralyzed side at the initial contact is statistically significant with respect to the lower back condition (p = 0.0233) and shows a significant trend for the entire back condition (p = 0.0730). The results suggest that the limited attention capacity of hemiplegic patients fails to improve both dorsiflexion and push-off movements; moreover, ankle motion can be promoted if attention is concentrated on recognizing focalized vibratory feedback patterns.By 2020, over 2.2 million people in the United States will be living with an amputated lower limb. The functional impact of amputations presents significant challenges in daily living activities. While significant work has been done to develop smart prosthetics, for the long-term development of effective and robust myoelectric control systems for transtibial amputees, there is still much that needs to be understood regarding how extrinsic muscles of the lower limb are utilized post-amputation. In this study, we examined muscle activity between the intact and residual limbs of three transtibial amputees with the aim of identifying differences in voluntary recruitment patterns during a bilateral motor task. We report that while there is variability across subjects, there are consistencies in the muscle recruitment patterns for the same functional movement between the intact and the residual limb within each subject. These results provide insights for how symmetric activation in residual muscles can be characterized and used to develop myoelectric control strategies for prosthetic devices in transtibial amputees.Muscle networks represent a series of interactions among muscles in the central nervous system's effort to reduce the redundancy of the musculoskeletal system in motor-control. How this occurs has only been investigated recently in healthy subjects with a novel technique exploring the functional connectivity between muscles through intermuscular coherence (IMC), yet the potential value of this method in characterizing the alteration of muscular networks after stroke remains unknown. In this study, muscle networks were assessed in post-stroke survivors and healthy controls to identify possible alterations in the neural oscillatory drive to muscles after stroke. PF9366 Surface electromyography (sEMG) was collected from eight key upper extremity muscles to non-invasively determine the common neural input to the spinal motor neurons innervating muscle fibers. Coherence was computed between all possible muscle pairs and further decomposed by non-negative matrix factorization (NMF) to identify the common spectral patterns of coherence underlying the muscle networks. Results suggested that the number of identified muscle networks during dynamic force generation decreased after stroke. The findings in this study could provide a new prospective for understanding the motor control recovery during post-stroke rehabilitation.The use of the electrical activity from the muscles may provide a natural way to control exoskeletons or other robotic devices seamlessly. The major challenges to achieve this goal are human motor redundancy and surface electromyography (sEMG) variability. The goal of this work is to find a feature extraction and classification procedures to estimate accurately elbow angular trajectory by means of a NARX Neural Network. The processing time-step should be small enough to make it feasible its further use for online control of an exoskeleton. In order to do so we analysed the Biceps and Triceps Brachii data from an elbow flexo-extension Coincident Timing task performed in the horizontal plane. The sEMG data was pre-processed and its energy was divided in five frequency intervals that were fed to a Nonlinear Auto Regressive with Exogenous inputs (NARX) Neural Network. The estimated angular trajectory was compared with the measured one showing a high correlation between them and a RMSE error maximum of 7 degrees. The procedure presented here shows a reasonably good estimation that, after training, allows real-time implementation. In addition, the results are encouraging to include more complex tasks including the shoulder joint.Rehabilitation level evaluation is an important part of the automatic rehabilitation training system. As a general rule, this process is manually performed by rehabilitation doctors using chart-based ordinal scales which can be both subjective and inefficient. In this paper, a novel approach based on ensemble learning is proposed which automatically evaluates stroke patients' rehabilitation level using multi-channel sEMG signals to this problem. The correlation between rehabilitation levels and rehabilitation training actions is investigated and actions suitable for rehabilitation assessment are selected. Then, features are extracted from the selected actions. Finally, the features are used to train the stacking classification model. Experiments using sEMG data collected from 24 stroke patients have been carried out to examine the validity and feasibility of the proposed method. The experiment results show that the algorithm proposed in this paper can improve the classification accuracy of 6 Brunnstrom stages to 94.36%, which can promote the application of home-based rehabilitation training in practice.A surface Electromyography (sEMG) contaminant type detector has been developed by using a Recurrent Neural Network (RNN) with Long Short-Term (LSMT) units in its hidden layer. This setup may reduce the contamination detection processing time since there is no need for feature extraction so that the classification occurs directly from the sEMG signal. The publicly available NINAPro (Non-Invasive Adaptive Prosthetics) database sEMG signals was used to train and test the network. Signals were contaminated with White Gaussian Noise, Movement Artifact, ECG and Power Line Interference. Two out of the 40 healthy subjects' data were considered to train the network and the other 38 to test it. Twelve models were trained under a -20dB contamination, one for each channel. ANOVA results showed that the training channel could affect the classification accuracy if SNR = -20dB and 0dB. An overall accuracy of 97.72% has been achieved by one of the models.Despite recent advancements in the field of pattern recognition-based myoelectric control, the collection of a high quality training set remains a challenge limiting its adoption. This paper proposes a framework for a possible solution by augmenting short training protocols with subject-specific synthetic electromyography (EMG) data generated using a deep generative network, known as SinGAN. The aim of this work is to produce high quality synthetic data that could improve classification accuracy when combined with a limited training protocol. SinGAN was used to generate 1000 synthetic windows of EMG data from a single window of six different motions, and results were evaluated qualitatively, quantitatively, and in a classification task. Qualitative assessment of synthetic data was conducted via visual inspection of principal component analysis projections of real and synthetic feature space. Quantitative assessment of synthetic data revealed 11 of 32 synthetic features had similar location and scale to real features (using univariate two-sample Lepage tests); whereas multivariate distributions were found to be statistically different (p less then 0.05). Finally, the addition of these synthetic data to a brief training set of real data significantly improved classification accuracy in a cross-validation testing scheme by 5.4% (p less then 0.001).The aging process, as well as neurological disorders, causes a decline in sensorimotor functions, which can often bring degraded motor output. As a means of compensation for such sensorimotor deficiencies, sensorimotor augmentation has been actively investigated. Consequently, exoskeleton devices or functional electrical stimulation could augment the muscle activity, while textured surfaces or electrical nerve stimulations could augment the sensory feedback. However, it is not easy to precisely anticipate the effects of specific augmentation because sensory feedback and motor output interact with each other as a closed-loop operation via the central and peripheral nervous systems. A computational internal model can play a crucial role in anticipating such an effect of augmentation therapy on the motor outcome. Still, no existing internal sensorimotor loop model has been represented in a complete computational form facilitating the anticipation. This paper presents such a computational internal model, including numerical values representing the effect of sensorimotor augmentation. With the existing experimental results, the model performance was evaluated indirectly. The change of sensory gain affects motor output inversely, while the change of motor gain did not change or minimally affects the motor output.Clinical Relevance- The presented computational internal model will provide a simple and easy tool for clinicians to design therapeutic intervention using sensorimotor augmentation.There is a growing body of literature that recognizes the importance of Skin Conductance (SC) for assessing changes in emotional states, such as engagement to learning tasks, and its importance to estimate possible drawbacks affecting overall performance. To date, most of the commonly used methods for SC signal analysis, i.e. detecting its phasic and tonic components and thus extracting informative features, are either too simple and unreliable or too complex and thus inaccessible and inflexible, as well as unable to perform online analyses. The current work proposes a simplified but clear and effective algorithm based on a Machine State to search for expected behaviors in the well-defined morphology of the signal. Eleven (11) features were correctly extracted from 79 healthy subjects during an experimental setup for immersive virtual rehabilitation (balance study case). The method was also successfully applied as a tool to identify significant changes in the subjective psychophysiological response to different experimental conditions. These results point toward a potential role in virtual rehabilitation applications by getting real-time feedback in human-in-the-loop approaches.
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