Gesture recognition via surface electromyography (sEMG) has drawn significant attention in the field of human–computer interaction. An important factor limiting the performance of sEMG-based pattern recognition (PR) is the generalization ability which sEMG changes for the identical movements when conducted at various positions or by different persons. Thus, this study aims to explore the generalization of classifier to develop a stable classification model that does not require relearning, even if it is used by other people. We propose a new feature extraction method to diminish the influence of limb position on sEMG-based PR. Specifically, the sEMG features are extracted directly from time domain. This condition is accomplished by using Fourier transform properties, difference, and the sum of squares differences. The best offline cross-validation accuracy (CVA) results are 88.775% training data from the tenth subject and testing data from the fifth subject in the NinaPro dataset. The best online CVA is 99%, and the movement selection time is 47.036 ± 1.028 ms. In comparison with the well-known sEMG feature, the CVA and the generalization of the proposed features improved substantially. These improvements aim to facilitate the practical implementation of myoelectric interfaces.

Rehabilitation technologies have great potentials in assisted motion training for stroke patients. Considering that wrist motion plays an important role in arm dexterous manipulation of activities of daily living, this paper focuses on developing a cable-driven wrist robotic rehabilitator (CDWRR) for motion training or assistance to subjects with motor disabilities. The CDWRR utilizes the wrist skeletal joints and arm segments as the supporting structure and takes advantage of cable-driven parallel design to build the system, which brings the properties of flexibility, low-cost, and low-weight. The controller of the CDWRR is designed typically based on a virtual torque-field, which is to plan “assist-as-needed” torques for the spherical motion of wrist responding to the orientation deviation in wrist motion training. The torque-field controller can be customized to different levels of rehabilitation training requirements by tuning the field parameters. Additionally, a rapidly convergent parameter self-identification algorithm is developed to obtain the uncertain parameters automatically for the floating wearable structure of the CDWRR. Finally, experiments on a healthy subject are carried out to demonstrate the performance of the controller and the feasibility of the CDWRR on wrist motion training or assistance.

Compared with conventional exoskeletons with rigid links, cable-driven upper-limb exoskeletons are light weight and have simple structures. However, cable-driven exoskeletons rely heavily on the human skeletal system for support. Kinematic modeling and control thus becomes very challenging due to inaccurate anthropomorphic parameters and flexible attachments. In this paper, the mechanical design of a cable-driven arm rehabilitation exoskeleton is proposed to accommodate human limbs of different sizes and shapes. A novel arm cuff able to adapt to the contours of human upper limbs is designed. This has given rise to an exoskeleton which reduces the uncertainties caused by instabilities between the exoskeleton and the human arm. A kinematic model of the exoskeleton is further developed by considering the inaccuracies of human-arm skeleton kinematics and attachment errors of the exoskeleton. A parameter identification method is used to improve the accuracy of the kinematic model. The developed kinematic model is finally tested with a primary experiment with an exoskeleton prototype.

Human arm exhibits superb maneuverability in performing various tasks by utilizing its ability to actively adjust impedance parameters and interaction forces. Therefore, how to identify the joint stiffness of the arm during motion and transmit them to the exoskeleton robots is the key to achieving flexible rehabilitation motion. In this paper, we propose a stiffness estimation method for elbow joint, termed stiffness and torque mapping index (STMI), which establishes the relationship between joint torque, joint angle and joint stiffness based on surface electromyography (sEMG). To improve joint angle identification accuracy, we propose a regression algorithm (RA), termed improved ant colony optimization generalized regression neural network (IACO-GRNN), and propose a time-domain descriptor (TDD) sEMG feature. Feature comparison experiment show that the TDD features (RMSE: 7.9792±0.0721; R2: 0.9257±0.0020) are superior among the popular features such as mean absolute value (MAV), root mean square (RMS) and waveform length (WL). The combination of TDD and IACO-GRNN achieves more impressive regression performance (RMSE: 8.6839 ± 0.0084, R2: 0.9109 ± 0.0002) than RA in joint angle recognition experiments, such as support vector regression (SVR), random forest regression (RFR), Particle swarm optimization-GRNN (PSO-GRNN) and Genetic algorithm-GRNN (GA-GRNN). The average RMSE of the joint stiffness estimated using STMI was 1.0146 Nm/rad. The proposed method can offer satisfactory dynamic elbow joint stiffness estimation only using sEMG signals, avoiding complex calibration of physiological data and additional sensing devices.

Based on the current research results of anthropomorphic-arm bionics and parallel manipulators, a motion control approach for a 7-DOF cable-driven manipulator is proposed. This paper introduces mechanical structure design of this anthropomorphic-arm first. For the inverse kinematics of the 7-DOF manipulator, a hybrid algorithm, based on gradient projection method and Paden-Kahan sub problems, is proposed to make the trajectory tracing of the manipulator in task space with high accuracy; a joint rate redistribution approach is also employed to optimize kinematic performance criterion and improve the fault-tolerance of the manipulator. The coupling cable lengths among the different joint modules, i.e., shoulder, elbow, and wrist joints, are analyzed in detail. Finally, to demonstrate the proposed algorithm, by utilizing ADAMS software, a straight-line trajectory motion simulation for the 7-DOF cable-driven manipulator is realized

A mechanism with lumped-compliance can be constructed by mounting springs at joints of an inverted slider crank mechanism. Different mounting schemes bring change in the stiffness performance. In this paper, a unified stiffness model is developed for a comprehensive analysis of the stiffness performance for mechanisms constructed with different spring mounting schemes. With the model, stiffness behaviors of spring-loaded inverted slider crank mechanisms are analyzed. Influences of each individual spring on the overall performance are characterized. The unified stiffness model allows designing mechanisms for a desired stiffness performance, such as constant-torque mechanism and variable stiffness mechanism, both being illustrated with a design example and experiments.