Amputation of the upper limb brings heavy burden to amputees, reduces their quality of life, and limits their performance in activities of daily life. The realization of natural control for prosthetic hands is crucial to improving the quality of life of amputees. Surface electromyography (sEMG) signal is one of the most widely used biological signals for the prediction of upper limb motor intention, which is an essential element of the control systems of prosthetic hands. The conversion of sEMG signals into effective control signals often requires a lot of computational power and complex process. Existing commercial prosthetic hands can only provide natural control for very few active degrees of freedom. Deep learning (DL) has performed surprisingly well in the development of intelligent systems in recent years. The significant improvement of hardware equipment and the continuous emergence of large data sets of sEMG have also boosted the DL research in sEMG signal processing. DL can effectively improve the accuracy of sEMG pattern recognition and reduce the influence of interference factors. This paper analyzes the applicability and efficiency of DL in sEMG-based gesture recognition and reviews the key techniques of DL-based sEMG pattern recognition for the prosthetic hand, including signal acquisition, signal preprocessing, feature extraction, classification of patterns, post-processing, and performance evaluation. Finally, the current challenges and future prospects in clinical application of these techniques are outlined and discussed.

Glioma stem cells (GSCs) have the capacity to repopulate tumors and mediate resistance to radiotherapy and chemotherapy. The Notch signaling pathway is important in proliferation, stem cell maintenance, cell differentiation, and tumorigenesis in GSCs. In this study, we compared CD133, Notch, and VEGF expressions in histological sections of primary and recurrent glioblastomas after radiotherapy and chemotherapy. In vitro study, the γ-secretase inhibitor inhibited NICD, Hes1 and pVEGFR2 expressions in GSCs. GSCs cultured under endothelial conditions undergo endothelial differentiation. Tumor samples were collected from 27 patients at the time of tumor recurrence. We used immunohistochemical techniques to compare expression of CD133, Notch-1 and VEGF. Expressions of CD133-, Notch-1-, and VEGF-positive glioma cells were higher in recurrent glioblastoma after radiotherapy and chemotherapy. To determine the clinical importance of Notch-1 expression in glioblastoma, we analyzed 15 patients who had received bevacizumab therapy followed by a second surgery at recurrence. OS was significantly longer in cases with Notch-1 negativity (8.8 months) than in those with I Notch-1 positivity (6.8 months). We noted that GSCs have the potential for endothelial differentiation with Notch activity. We believe that Notch-1 is a potential target and/or biomarker for antiangiogenic treatments.

The aim of the present study was to identify the response of the autonomic nervous system (ANS) to passive lower limb movement and to determine whether there are gender differences. The experimental sets included 5 cycles per minute (CPM5), 10 cycles per minute (CPM10) and 15 cycles per minute (CPM15) on the passive cycling machine. ANS activity was measured using heart rate variability time domain analysis (RR interval, pNN50, RMSSD and SDNN), frequency domain analysis (TF, LF, HF and LF/HF) and Poincaré plot analysis (SD1, SD2 and SD1/SD2 ratio). The collected signal at rest served as the baseline (rest). Compared with the parameters at rest, the male subjects had decreased pNN50, decreased SDNN, lower TP and LF power (ms(2)), suppressed LF (n.u.), augmented HF (n.u.), suppressed LF/HF, decreased SD2 and increased SD1/SD2 ratios in response to CPM5 or CPM10 (all P < 0.05). Compared with the parameters at rest, decreased LF/HF and increased SD1/SD2 in response to CPM5 or CPM10 (all P < 0.05) were the only changes in the female subjects. LF/HF and SD1/SD2 differed between both groups for the same level of passive lower limb movement (all P < 0.05). These results suggest that passive lower limb movement leads to an ANS response and that male subjects are more sensitive to passive lower limb movements. During passive leg movements, sympathetic nervous activity is largely suppressed, and vagal activity achieves dominance. The response of the ANS to passive leg movement is determined by gender.

Objective The purpose of this study is to investigate the cortical activation during passive and active training modes under different speeds of upper extremity rehabilitation robots. Methods Twelve healthy subjects completed the active and passive training modes at various speeds (0.12, 0.18, and 0.24 m/s) for the right upper limb. The functional near-infrared spectroscopy (fNIRS) was used to measure the neural activities of the sensorimotor cortex (SMC), premotor cortex (PMC), supplementary motor area (SMA), and prefrontal cortex (PFC). Results Both the active and passive training modes can activate SMC, PMC, SMA, and PFC. The activation level of active training is higher than that of passive training. At the speed of 0.12 m/s, there is no significant difference in the intensity of the two modes. However, at the speed of 0.24 m/s, there are significant differences between the two modes in activation levels of each region of interest (ROI) (P < 0.05) (SMC: F = 8.90, P = 0.003; PMC: F = 8.26, P = 0.005; SMA: F = 5.53, P = 0.023; PFC: F = 9.160, P = 0.003). Conclusion This study mainly studied on the neural mechanisms of active and passive training modes at different speeds based on the end-effector upper-limb rehabilitation robot. Slow, active training better facilitated the cortical activation associated with cognition and motor control. See Video Abstract, http://links.lww.com/WNR/A621.

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To help hemiplegic patients with stroke to restore impaired or lost upper extremity functionalities efficiently, the design of upper limb rehabilitation robotics which can substitute human practice becomes more important. The aim of this work is to propose a powered exoskeleton for upper limb rehabilitation based on a wheelchair in order to increase the frequency of training and reduce the preparing time per training. This paper firstly analyzes the range of motion (ROM) of the flexion/extension, adduction/abduction, and internal/external of the shoulder joint, the flexion/extension of the elbow joint, the pronation/supination of the forearm, the flexion/extension and ulnar/radial of the wrist joint by measuring the normal people who are sitting on a wheelchair. Then, a six-degree-of-freedom exoskeleton based on a wheelchair is designed according to the defined range of motion. The kinematics model and workspace are analyzed to understand the position of the exoskeleton. In the end, the test of ROM of each joint has been done. The maximum error of measured and desired shoulder flexion and extension joint angle is 14.98%. The maximum error of measured and desired elbow flexion and extension joint angle is 14.56%. It is acceptable for rehabilitation training. Meanwhile, the movement of drinking water can be realized in accordance with the range of motion. It demonstrates that the proposed upper limb exoskeleton can also assist people with upper limb disorder to deal with activities of daily living. The feasibility of the proposed powered exoskeleton for upper limb rehabilitation training and function compensating based on a wheelchair is proved.

Continuous blood pressure (BP) monitoring has a significant meaning for the prevention and early diagnosis of cardiovascular disease. However, under different calibration methods, it is difficult to determine which model is better for estimating BP. This study was firstly designed to reveal a better BP estimation model by evaluating and optimizing different BP models under a justified and uniform criterion, i.e., the advanced point-to-point pairing method (PTP). Here, the physical trial in this study caused the BP increase largely. In addition, the PPG and ECG signals were collected while the cuff bps were measured for each subject. The validation was conducted on four popular vascular elasticity (VE) models (MK-EE, L-MK, MK-BH, and dMK-BH) and one representative elastic tube (ET) model, i.e., M-M. The results revealed that the VE models except for L-MK outperformed the ET model. The linear L-MK as a VE model had the largest estimated error, and the nonlinear M-M model had a weaker correlation between the estimated BP and the cuff BP than MK-EE, MK-BH, and dMK-BH models. Further, in contrast to L-MK, the dMK-BH model had the strongest correlation and the smallest difference between the estimated BP and the cuff BP including systolic blood pressure (SBP) and diastolic blood pressure (DBP) than others. In this study, the simple MK-EE model showed the best similarity to the dMK-BH model. There were no significant changes between MK-EE and dMK-BH models. These findings indicated that the nonlinear MK-EE model with low estimated error and simple mathematical expression was a good choice for application in wearable sensor devices for cuff-less BP monitoring compared to others.

The purpose of this study is to establish the human-exoskeleton coupling (HEC) dynamic model of the upper limb exoskeleton, overcome the difficulties of dynamic modeling caused by the differences of individual and disease conditions and the complexity of musculoskeletal system, to achieve early intervention and optimal assistance for stroke patients. This paper proposes a method of HEC dynamics modeling, and analyzes the HEC dynamics in the patient-active training (PAT) and patient-passive training (PPT) mode, and designs a step-by-step dynamic parameter identification method suitable for the PAT and PPT modes. Comparing the HEC torques obtained by the dynamic model with the real torques measured by torque sensors, the root mean square error (RMSE) can be kept within 13% in both PAT and PPT modes. A calibration experiment was intended to further verify the accuracy of dynamic parameter identification. The theoretical torque of the load calculated by the dynamic model, is compared with the difference calculated by parameter identification. The trends and peaks of the two curves are similar, and there are also errors caused by experimental measurements. Furthermore, this paper proposes a prediction model of the patient's height and weight and HEC dynamics parameters in the PPT mode. The RMSE of the elbow and shoulder joints of the prediction model is 9.5% and 13.3%. The proposed HEC dynamic model is helpful to provide different training effects in the PAT and PPT mode and optimal training and assistance for stroke patients.INDEX TERMS Human-exoskeleton coupling dynamics, parameter identification, rehabilitation training.