Recently, the use of portable equipment has attracted much attention in the medical ultrasound field. Handheld ultrasound devices have great potential for improving the convenience of diagnosis, but noise-induced artifacts and low resolution limit their application. To enhance the video quality of handheld ultrasound devices, we propose a low-rank representation multipathway generative adversarial network (LRR MPGAN) with a cascade training strategy. This method can directly generate sequential, high-quality ultrasound video with clear tissue structures and details. In the cascade training process, the network is first trained with plane wave (PW) single-/multiangle video pairs to capture dynamic information and then fine-tuned with handheld/high-end image pairs to extract high-quality single-frame information. In the proposed GAN structure, a multipathway generator is applied to implement the cascade training strategy, which can simultaneously extract dynamic information and synthesize multiframe features. The LRR decomposition channel approach guarantees the fine reconstruction of both global features and local details. In addition, a novel ultrasound loss is added to the conventional mean square error (MSE) loss to acquire ultrasound-specific perceptual features. A comprehensive evaluation is conducted in the experiments, and the results confirm that the proposed method can effectively reconstruct high-quality ultrasound videos for handheld devices. With the aid of the proposed method, handheld ultrasound devices can be used to obtain convincing and convenient diagnoses.Automatic diagnosing of Cerebral Palsy (CP) gait is crucial in quantitative evaluation of a therapeutic intervention. Existing systems for such gait assessment are expensive and require user intervention. This study proposes a low-cost gait assessment system equipped with multiple Kinect sensors. Forty subjects (20 CP patients and 20 normal) were recruited for the experiment. To remove outlier frames from the combined gait signal of multiple sensors a data driven algorithm was proposed. Different supervised classifiers along with extreme learning machine were investigated to diagnose CP gait. In addition, a feature level analysis was also performed. Several spatio-temporal features (i.e. step length, stride length, stride time, etc.) were extracted. The strength of walking ratio, a speed invariant feature, to detect CP gait was thoroughly analyzed. The proposed system outperformed state-of-the-art with ≈98% of accuracy (sensitivity 100%, and specificity 96.87%). Results indicate a substantial improvement in abnormality detection performance after outlier removal. Based on ReliefF feature ranking algorithm, walking ratio ranked the best among other classical gait features.  https://www.selleckchem.com/products/itacnosertib.html  Performance of all classifiers increased substantially using walking ratio as a feature. Extreme learning machine demonstrated a competing performance in all cases. The higher classification accuracy of this low-cost system using only a single feature makes it attractive for CP gait detection.An important challenge in the study of functional corticomuscular coupling (FCMC) is an accurate capture of the coupling relationship between the cerebral cortex and the effector muscle. The coherence method is a linear analysis method, which has certain limitations in further revealing the nonlinear coupling between neural signals. Although mutual information (MI) and transfer entropy (TE) based on information theory can capture both linear and nonlinear correlations, the equitability of these algorithms is ignored and the nonlinear components of the correlation cannot be separated. The maximal information coefficient (MIC) is a suitable method to measure the coupling between neurophysiological signals. This study extends the MIC to the time-frequency domain, named time-frequency maximal information coefficient (TFMIC), to explore the FCMC in a specific frequency band. The effectiveness, equitability, and robustness of the algorithm on the simulation data was verified and compared with coherence, TE- and MI- based methods. Simulation results showed that the TFMIC could accurately detect the coupling for different functional relationships at low noise levels. The dorsiflexion experimental results revealed that the beta-band (14-30 Hz) significant coupling was observed at channels Cz, C4, FC4, and FCz. Additionally, the results showed that the coupling was higher in the alpha-band (8-13 Hz) and beta-band (14-30 Hz) than in the gamma-band (31-45 Hz). This might be related to a transition between sensorimotor states. Specifically, the nonlinear component of FCMC was also observed at channels Cz, C4, FC4, and FCz. This study expanded the research on nonlinear coupling components in FCMC.Estimation of muscle excitations from a reduced sensor array could greatly improve current techniques in remote patient monitoring. Such an approach could allow continuous monitoring of clinically relevant biomechanical variables that are ideal for personalizing rehabilitation. In this paper, we introduce the notion of a muscle synergy function which describes the synergistic relationship between a subset of muscles. We develop from first principles an approximation to their behavior using Gaussian process regression and demonstrate the utility of the technique for estimating the excitation time-series of leg muscles during normal walking for nine healthy subjects. Specifically, excitations for six muscles were estimated using surface electromyography (sEMG) data during a finite time interval (called the input window) from four different muscles (called the input muscles) with mean absolute error (MAE) less than 5.0% of the maximum voluntary contraction (MVC) and that accounts for 82-88% of the variance (VAF) in the true excitations. Further, these estimated excitations informed muscle activations with less than 4.0% MAE and 89-93% VAF. We also present a detailed analysis of a number of different modeling choices, including every possible combination of four-, three- and two-muscle input sets, the size and structure of the input window, and the stationarity of the Gaussian process covariance functions. Further, application specific modifications for future use are discussed. The proposed technique lays a foundation to explore the use of reduced wearable sensor arrays and muscle synergy functions for monitoring clinically relevant biomechanics during daily life.