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.

The pattern recognition (PR) based on surface electromyography (sEMG) could improve the quality of daily life of amputees. However, the lack of robustness and adaptability hinders its practical application. To realize the long-term reliability and user adaptability simultaneously, a novel multi-task dual-stream supervised domain adaptation (MDSDA) network based on convolutional neural network (CNN) was proposed. A long-term multi-subject sEMG signal acquisition was conducted to validate the performance of MDSDA, recruiting 12 able-bodied subjects. A total of thirty gestures were used for the acquisition, including one set of static gestures and two sets of dynamic gestures. The longterm multi-subject sEMG dataset is publicly available at the website (https://github.com/tinker1017/LONG-TERM-ANDMULTI-SUBJECT-SEMG-DATASET). Four train-test estimations were designed to evaluate the robustness and adaptability of MDSDA. The results showed that MDSDA outperformed CNN and fune-tuning. Furthermore, we studied the divisibility between static and dynamic gestures that performed similar actions. The outcomes demonstrated that there existed high separability between them. This may be helpful to reduce the signal collection burden. Experimental results proved MDSDA has the potential to provide a robust and generalized PR system for the clinic applications.

Transcutaneous electrical nerve stimulation (TENS) has been used to treat chronic pain. However, the potential efficacy and mechanism of the effect of applying TENS for a short time in chronic pain patients remains unclear. To identify the effect of short-term TENS on chronic pain patients and to clarify the mechanism of the effect, we investigated abnormalities of functional connectivity (FC) within the prefrontal cortex (PFC) using resting-state functional near-infrared spectroscopy (rs-fNIRS). Fifteen patients (56.8 ± 17.4 years, nine females) with chronic pain participated in this rs-fNIRS study. The fNIRS scans included two parts: a 5-minute resting-state scan followed by a 5-minute scan during TENS (150 Hz) application. The pain intensity was measured using a Visual Analog Scale (VAS) and Pittsburgh Sleep Quality Index (PSQI). The spontaneous brain activity of the PFC and resting-state functional connectivity (rsFC) in the PFC were examined during TENS and compared to before TENS. The results showed that Pain intensity significantly decreased after TENS (p < 0.001). During TENS, fALFF values were significantly lower in BA46 (**p = 0.0025) and BA45 (**p = 0.0056). rsFC strength increased during TENS compared to before, with significant group-level increases in BA10, BA9, BA46, and BA44/45 (p < 0.05). Notably, the variation between BA10 and BA44/45 was highly significant (***p < 0.001). These findings suggest that FC between BA10 and BA44/45 was associated with analgesia of TENS in patients with chronic pain, indicating the potential role of FC as a novel objective parameter to predict the outcome of clinical use of TENS for pain relief in chronic pain patients.

Background: At present, the effects of upper limb movement are generally evaluated from the level of motor performance. The purpose of this study is to evaluate the response of the cerebral cortex to different upper limb movement patterns from the perspective of neurophysiology.Method: Thirty healthy adults (12 females, 18 males, mean age 23.9 ± 0.9 years) took resistance and non-resistance exercises under four trajectories (T1: left and right straight-line movement; T2: front and back straight-line movement; T3: clockwise and anticlockwise drawing circle movement; and T4: clockwise and anticlockwise character ⁕ movement). Each movement included a set of periodic motions composed of a 30-s task and a 30-s rest. Functional near-infrared spectroscopy (fNIRS) was used to measure cerebral blood flow dynamics. Primary somatosensory cortex (S1), supplementary motor area (SMA), pre-motor area (PMA), primary motor cortex (M1), and dorsolateral prefrontal cortex (DLPFC) were chosen as regions of interests (ROIs). Activation maps and symmetric heat maps were applied to assess the response of the cerebral cortex to different motion patterns.Result: The activation of the brain cortex was significantly increased during resistance movement for each participant. Specifically, S1, SMA, PMA, and M1 had higher participation during both non-resistance movement and resistance movement. Compared to non-resistance movement, the resistance movement caused an obvious response in the cerebral cortex. The task state and the resting state were distinguished more obviously in the resistance movement. Four trajectories can be distinguished under non-resistance movement.Conclusion: This study confirmed that the response of the cerebral motor cortex to different motion patterns was different from that of the neurophysiological level. It may provide a reference for the evaluation of resistance training effects in the future.

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.