sEMG-Based End-to-End Continues Prediction of Human Knee Joint Angles Using the Tightly Coupled Convolutional Transformer Model.

Wearable exoskeleton robots can promote the rehabilitation of patients with physical dysfunction. And improving human-computer interaction performance is a significant challenge for exoskeleton robots. The traditional feature extraction process based on surface Electromyography(sEMG) is complex and requires manual intervention, making real-time performance difficult to guarantee. In this study, we propose an end-to-end method to predict human knee joint angles based on sEMG signals using a tightly coupled convolutional transformer (TCCT) model. We first collected sEMG signals from 5 healthy subjects. Then, the envelope was extracted from the noise-removed sEMG signal and used as the input to the model. Finally, we developed the TCCT model to predict the knee joint angle after 100 ms. For the prediction performance, we used the Root Mean Square Error(RMSE), Pearson Correlation Coefficient(CC), and Adjustment R2 as metrics to evaluate the error between the actual knee angle and the predicted knee angle. The results show that the model can predict the human knee angle quickly and accurately. The mean RMSE, Adjustment R2, and (CC) values of the model are 3.79°, 0.96, and 0.98, respectively, which are better than traditional deep learning models such as Informer (4.14, 0.95, 0.98), CNN (5.56, 0.89, 0.96) and CNN-BiLSTM (3.97, 0.95, 0.98). In addition, the prediction time of our proposed model is only 11.67 ± 0.67 ms, which is less than 100 ms. Therefore, the real-time and accuracy of the model can meet the continuous prediction of human knee joint angle in practice.

Continuous Estimation of Human Knee Joint Angles by Fusing Kinematic and Myoelectric Signals.

Exoskeleton robot is an essential tool in active rehabilitation training for patients with lower limb motor dysfunctions. Accurate and real-time recognition in human motion intention is a great challenge in exoskeleton robot, which can be implemented by continues estimation of human joint angles. In this study, we innovatively proposed a novel feature-based convolutional neural network-bi-directional long-short term memory network (CNN-BiLSTM) model to predict the knee joint angles more accurately and in real time. We validated our method on a public dataset, including surface electromyography(sEMG) and inertial measurement unit (IMU) data of 10 healthy subjects during normal walking. Initially, features extraction from each modal data achieved feature-level fusion. Then the importance of each sEMG and IMU signal feature for knee joint angle prediction was quantified by ensemble feature scorer (EFS) and the number of features required for prediction while ensuring accuracy was simplified by profile likelihood maximization (PLM) algorithm. Finally, the CNN-BiLSTM model was created by using the determined simplest features to further fuse the spatio-temporal correlation of signals. The results indicated that the EFS and PLM algorithm could remove the feature redundancy perfectly and estimation performance would become better when bi-modal gait data were fused. For the estimation performance, the average root mean square error (RMSE), adjusted [Formula: see text] and pearson correlation coefficient (CC) of our algorithm were 4.07, 0.95, and 0.98, respectively, which was better than CNN, BiLSTM and other three traditional machine learning methods. In addition, the model test time was 62.47 ± 0.29 ms, which was less than the predicted horizon of 100 ms. The real-time performance and accuracy are satisfactory. Compared with previous works, our method has great advantages in feature selection and model design, which further improves the prediction accuracy. These promising results demonstrate that the proposed method has considerable potential to be applied to exoskeleton robot control.

Large-Scale Brain Functional Network Integration for Discrimination of Autism Using a 3-D Deep Learning Model.

GOAL: Brain functional networks (BFNs) constructed using resting-state functional magnetic resonance imaging (fMRI) have proven to be an effective way to understand aberrant functional connectivity in autism spectrum disorder (ASD) patients. It is still challenging to utilize these features as potential biomarkers for discrimination of ASD. The purpose of this work is to classify ASD and normal controls (NCs) using BFNs derived from rs-fMRI. METHODS: A deep learning framework was proposed that integrated convolutional neural network (CNN) and channel-wise attention mechanism to model both intra- and inter-BFN associations simultaneously for ASD diagnosis. We investigate the effects of each BFN on performance and performed inter-network connectivity analysis between each pair of BFNs. We compared the performance of our CNN model with some state-of-the-art algorithms using functional connectivity features. RESULTS: We collected 79 ASD patients and 105 NCs from the ABIDE-I dataset. The mean accuracy of our classification algorithm was 77.74% for classification of ASD versus NCs. CONCLUSION: The proposed model is able to integrate information from multiple BFNs to improve detection accuracy of ASD. SIGNIFICANCE: These findings suggest that large-scale BFNs is promising to serve as reliable biomarkers for diagnosis of ASD.

Antioxidant activities of anastatin A & B derivatives and compound 38c's protective effect in a mouse model of CCl4-induced acute liver injury.

Anastatins A and B, two flavonoid compounds isolated from desert plant Anastatica hierochuntica, have protective activities for primary rat hepatocytes. Anastatins A and B, and their derivatives, were synthesized by our group previously. In this study, the antioxidant activity and cytotoxicity of these compounds were studied using chemical assessment methods, cell proliferation inhibition experiments, and cell oxidative damage models. The best compound, 38c, was used to study the hepatoprotection activity and mechanism by using a CCl4-induced liver injury model in mice. The results show that most of these flavonoid compounds have good antioxidant activity and low cytotoxicity in vitro. Among them, the most potent compound was 38c, which exhibited a protective effect on CCl4-induced hepatic injury by suppressing the amount of CYP2E1. These findings indicate that anastatin flavonoid derivatives have potential therapeutic utility against oxidative hepatic injury.

Antioxidant properties of flavonoid derivatives and their hepatoprotective effects on CCl4 induced acute liver injury in mice.

Excessive accumulation of free radicals in the body can cause liver damage, aging, cancer, stroke, and myocardial infarction. Anastatin B, a skeletal flavonoid, was reported to have antioxidant and hepatoprotective effects. Anastatin B derivatives, compound 1 and 2, were synthesized by our group previously. In this study, their antioxidant activity and hepatoprotective mechanism were studied using chemical evaluation methods, a cellular model of hydrogen peroxide (H2O2)-induced oxidative damage, and a mouse model of carbon tetrachloride (CCl4)-induced liver injury. Results from the chemical evaluation suggested that both compounds had good antioxidant power and radical scavenging ability in vitro. MTT assay showed that both compounds had cytoprotective activity in H2O2-treated PC12 cells. Moreover, their hepatoprotective activities evaluated using a mouse model of CCl4-induced liver injury that compared with the model group, pretreatment with compound 1 and 2 significantly decreased alanine transaminase (ALT), aspartate transaminase (AST), lactate dehydrogenase (LDH), and malondialdehyde (MDA) levels; reduced the liver tissue damage; and increased glutathione content. However, compound 2 was a more effective hepatoprotectant than compound 1 was. Finally, the amount of TNF-α and cytochrome P450 2E1 (CYP2E1) were significantly downregulated in compound 1 and 2 pretreatment groups. Collectively, our findings demonstrate that both compounds have potential antioxidant activity and hepatoprotective effect in vitro and in vivo. Further chemo-biological study and investigation of the compounds' enzymatic targets are ongoing.