Continuous online prediction of lower limb joints angles based on sEMG signals by deep learning approach.

Continuous online prediction of human joints angles is a key point to improve the performance of man-machine cooperative control. In this study, a framework of online prediction method of joints angles by long short-term memory (LSTM) neural network only based on surface electromyography (sEMG) signals was proposed. The sEMG signals from eight muscles of five subjects' right leg and three joints angles and plantar pressure signals of subjects were collected simultaneously. Different inputs (only sEMG (unimodal), sEMG combined with plantar pressure (multimodal)) after online feature extraction and standardization were used for training the angle online prediction model by LSTM. The results indicate that there is no significant difference between the two kinds of inputs for LSTM model and the proposed method can make up for the shortage of using a single type of sensor. The range of mean values of root square mean error, mean absolute error and Pearson correlation coefficient of the three joints angles achieved by the proposed model only with the input of sEMG under four kinds of predicted time (50, 100, 150, and 200 ms) are [1.63°,3.20°],[1.27°, 2.36°] and [0.9747, 0.9935]. Three popular machine learning algorithms with different inputs were compared to the proposed model only based on sEMG. Experiment results demonstrate that the proposed method has the best prediction performance and there are highly significant differences between it and other methods. The difference of prediction results under different gait phases by the proposed method was also analyzed. The results indicate that the prediction effect of support phases is generally better than that of swing phases. Above experimental results show that the proposed method can realize accurate online joint angle prediction and has better performance to promote man-machine cooperation.

Research on the Application of Multi-Source Information Fusion in Multiple Gait Pattern Transition Recognition.

Multi-source information fusion technology is a kind of information processing technology which comprehensively processes and utilizes multi-source uncertain information. It is an effective scheme to solve complex pattern recognition and improve classification performance. This study aims to improve the accuracy and robustness of exoskeleton gait pattern transition recognition in complex environments. Based on the theory of multi-source information fusion, this paper explored a multi-source information fusion model for exoskeleton gait pattern transition recognition in terms of two aspects of multi-source information fusion strategy and multi-classifier fusion. For eight common gait pattern transitions (between level and stair walking and between level and ramp walking), we proposed a hybrid fusion strategy of multi-source information at the feature level and decision level. We first selected an optimal feature subset through correlation feature extraction and feature selection algorithm, followed by the feature fusion through the classifier. We then studied the construction of a multi-classifier fusion model with a focus on the selection of base classifier and multi-classifier fusion algorithm. By analyzing the classification performance and robustness of the multi-classifier fusion model integrating multiple classifier combinations with a number of multi-classifier fusion algorithms, we finally constructed a multi-classifier fusion model based on D-S evidence theory and the combination of three SVM classifiers with different kernel functions (linear, RBF, polynomial). Such multi-source information fusion model improved the anti-interference and fault tolerance of the model through the hybrid fusion strategy of feature level and decision level and had higher accuracy and robustness in the gait pattern transition recognition, whose average recognition accuracy for eight gait pattern transitions reached 99.70%, which increased by 0.15% compared with the highest average recognition accuracy of the single classifier. Moreover, the average recognition accuracy in the absence of different feature data reached 97.47% with good robustness.

Preparation and Performance of Mo/Cu/Fe Multi-Layer Composite Coating with Staggered Spatial Structure by Electro-Explosive Spraying Technology.

In the present study, electro-explosive spraying technology was used to prepare a multi-layer composite coating with a staggered spatial structure on a 45 steel substrate, and the mechanical properties and wear behavior of the coating were studied. The composite coating was prepared by spraying Mo as the bonding layer, then spraying high-carbon steel and aluminum bronze alternately as a functional coating. The cross-sectional morphology, surface morphology and the properties of the coating were analyzed with a scanning electron microscope (SEM), energy dispersive spectrometer (EDS), electron backscattered diffraction (EBSD) and a 3D profilometer. The bonding strength, friction and wear resistance of the coating were studied by the bonding strength experiment and by the friction and wear experiment. The results showed that it is feasible to prepare a composite coating with a sponge-like spatial structure with electro-explosive technology. There was metallurgical bonding as well as mechanical bonding between the adjacent coating layers. The composite coating had the advantages of uniform thickness, high compactness, high bonding strength and good wear resistance.

Continuous Estimation of Knee Joint Angle Based on Surface Electromyography Using a Long Short-Term Memory Neural Network and Time-Advanced Feature.

Continuous joint angle estimation based on a surface electromyography (sEMG) signal can be used to improve the man-machine coordination performance of the exoskeleton. In this study, we proposed a time-advanced feature and utilized long short-term memory (LSTM) with a root mean square (RMS) feature and its time-advanced feature (RMSTAF; collectively referred to as RRTAF) of sEMG to estimate the knee joint angle. To evaluate the effect of joint angle estimation, we used root mean square error (RMSE) and cross-correlation coefficient ρ between the estimated angle and actual angle. We also compared three methods (i.e., LSTM using RMS, BPNN (back propagation neural network) using RRTAF, and BPNN using RMS) with LSTM using RRTAF to highlight its good performance. Five healthy subjects participated in the experiment and their eight muscle (i.e., rectus femoris (RF), biceps femoris (BF), semitendinosus (ST), gracilis (GC), semimembranosus (SM), sartorius (SR), medial gastrocnemius (MG), and tibialis anterior (TA)) sEMG signals were taken as algorithm inputs. Moreover, the knee joint angles were used as target values. The experimental results showed that, compared with LSTM using RMS, BPNN using RRTAF, and BPNN using RMS, the average RMSE values of LSTM using RRTAF were respectively reduced by 8.57%, 46.62%, and 68.69%, whereas the average ρ values were respectively increased by 0.31%, 4.15%, and 18.35%. The results demonstrated that LSTM using RRTAF, which contained the time-advanced feature, had better performance for estimating the knee joint motion.

Quantitative Assessment of Motor Function for Patients with a Stroke by an End-Effector Upper Limb Rehabilitation Robot.

With the popularization of rehabilitation robots, it is necessary to develop quantitative motor function assessment methods for patients with a stroke. To make the assessment equipment easier to use in clinics and combine the assessment methods with the rehabilitation training process, this paper proposes an anthropomorphic rehabilitation robot based on the basic movement patterns of the upper limb, point-to-point reaching and circle drawing movement. This paper analyzes patients' movement characteristics in aspects of movement range, movement accuracy, and movement smoothness and the output force characteristics by involving 8 patients. Besides, a quantitative assessment method is also proposed based on multivariate fitting methods. It can be concluded that the area of the real trajectory and movement accuracy during circle drawing movement as well as the ratio of force along the sagittal axis in backward point-to-point movement are the unique parameters that are different remarkably between stroke patients and healthy subjects. The fitting function has a high goodness of fit with the Fugl-Meyer scores for the upper limb (R 2 = 0.91, p = 0.015), which demonstrates that the fitting function can be used to assess patients' upper limb movement function. The indicators are recorded during training movement, and the fitting function can calculate the scores immediately, which makes the functional assessment quantitative and timely. Combining the training process and assessment, the quantitative assessment method will farther expand the application of rehabilitation robots.

Self-Tuning Threshold Method for Real-Time Gait Phase Detection Based on Ground Contact Forces Using FSRs.

This paper presents a novel methodology for detecting the gait phase of human walking on level ground. The previous threshold method (TM) sets a threshold to divide the ground contact forces (GCFs) into on-ground and off-ground states. However, the previous methods for gait phase detection demonstrate no adaptability to different people and different walking speeds. Therefore, this paper presents a self-tuning triple threshold algorithm (STTTA) that calculates adjustable thresholds to adapt to human walking. Two force sensitive resistors (FSRs) were placed on the ball and heel to measure GCFs. Three thresholds (i.e., high-threshold, middle-threshold andlow-threshold) were used to search out the maximum and minimum GCFs for the self-adjustments of thresholds. The high-threshold was the main threshold used to divide the GCFs into on-ground and off-ground statuses. Then, the gait phases were obtained through the gait phase detection algorithm (GPDA), which provides the rules that determine calculations for STTTA. Finally, the STTTA reliability is determined by comparing the results between STTTA and Mariani method referenced as the timing analysis module (TAM) and Lopez-Meyer methods. Experimental results show that the proposed method can be used to detect gait phases in real time and obtain high reliability when compared with the previous methods in the literature. In addition, the proposed method exhibits strong adaptability to different wearers walking at different walking speeds.

Preparation of graphene by electrical explosion of graphite sticks.

Graphene nanosheets were produced by electrical explosion of high-purity graphite sticks in distilled water at room temperature. The as-prepared samples were characterized by various techniques to find different forms of carbon phases, including graphite nanosheets, few-layer graphene, and especially, mono-layer graphene with good crystallinity. Delicate control of energy injection is critical for graphene nanosheet formation, whereas mono-layer graphene was produced under the charging voltage of 22.5-23.5 kV. On the basis of electrical wire explosion and our experimental results, the underlying mechanism that governs the graphene generation was carefully illustrated. This work provides a simple but innovative route for producing graphene nanosheets.