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The purpose of this paper is to propose a seamless active interaction control method integrating electromyography (EMG)-triggered assistance and the adaptive impedance control scheme for parallel robot-assisted lower limb rehabilitation and training.

An active interaction control strategy based on EMG motion recognition and adaptive impedance model is implemented on a six-degrees of freedom parallel robot for lower limb rehabilitation. The autoregressive coefficients of EMG signals integrating with a support vector machine classifier are utilized to predict the movement intention and trigger the robot assistance. An adaptive impedance controller is adopted to influence the robot velocity during the exercise, and in the meantime, the user’s muscle activity level is evaluated online and the robot impedance is adapted in accordance with the recovery conditions.

Experiments on healthy subjects demonstrated that the proposed method was able to drive the robot according to the user’s intention, and the robot impedance can be updated with the muscle conditions. Within the movement sessions, there was a distinct increase in the muscle activity levels for all subjects with the active mode in comparison to the EMG-triggered mode.

Both users’ movement intention and voluntary participation are considered, not only triggering the robot when people attempt to move but also changing the robot movement in accordance with user’s efforts. The impedance model here responds directly to velocity changes, and thus allows the exercise along a physiological trajectory. Moreover, the muscle activity level depends on both the normalized EMG signals and the weight coefficients of involved muscles.

Human movement system is a Multi-DOF, redundant, complex and nonlinear system formed by coordinating combination of neural system, bones, muscles and joints, which is robust and has fast response and learning ability. Imitating human movement system can improve robustness, fast response and learning ability of the robots.

In this paper, we propose a new motion model based on the human motion pathway, especially the information propagation mechanism between the cerebellum and spinal cord.

The proposed motion model proves to have fast response and learning ability through experiments, which matches the features of human motion.

The proposed model in this paper introduces the habitual theory in kinesiology and neuroscience into robot control, and improves robustness, fast response and learning ability of the robots. This paper proves that introduction of neuroscience has an important guiding significance for precise and adaptive robot control, such as assembly automation.

The purpose of this paper is to improve the problem of kinematics incompatibility of human–exoskeleton in the existing rigid lower-limb exoskeleton (LLE).

In this paper, following an introduction, the motion characteristics of the human knee joint and the design method of the exoskeleton were introduced. A kinematics model of the LLE based on cross-four-bar linkage was obtained. The structural parameters of the LLE mechanism were optimized by the particle swarm optimization algorithm. The predefined trajectories used in the optimization process were derived from the ankle joint, not the instantaneous center of rotation of the knee joint. Finally, the motion deviation of the optimization result was simulated, and the human–exoskeleton coordination experiment was designed to compare with the traditional single-axis knee joint in terms of comfort and coordination.

The lower limb exoskeleton mechanism obtained in this paper has a good tracking effect on human movement and has been improved in terms of comfort and coordination compared with the traditional single-axis knee joint.

The customized exoskeleton design method introduced in this paper is relatively simple, and the obtained exoskeleton has better movement coordination than the traditional exoskeleton. It can provide a reference for the design of lower limb exoskeleton and lower limb orthosis.

Based on the weak seismic performance and low ductility of coupled shear walls, engineered cementitious composites (ECC) is utilized to strengthen it to solve the deformation problem in tall buildings more effectively and study its mechanical properties more deeply.

The properties of reinforced concrete coupled shear wall (RCCSW) and reinforced ECC coupled shear wall (RECSW) have been studied by numerical simulation, which is in good agreement with the experimental results. The reliability of the finite element model is verified. On this basis, a detailed parameter study is carried out, including the strength and reinforcement ratio of longitudinal rebar, the placement height of ECC in the wall limb and the position of ECC connecting beams. The study indexes include failure mode and the skeleton curve.

The results suggest that the bearing capacity of RECSW is significantly affected by the ratio of longitudinal rebar. When the ratio of longitudinal rebar increases from 0.47% to 3.35%, the bearing capacity of RECSW increases from 250 kN to 303 kN, an increase of 21%. The strength of longitudinal rebar has little influence on the bearing capacity of RECSW. When the strength of the longitudinal rebar increases, the bearing capacity of RECSW increases little. The failure mode of RECSW can be improved by lowering the casting height of the ECC beam in a certain range.

In this paper, ECC is used to strengthen the coupled shear wall, and the accuracy of the finite element model is verified from the failure mode and skeleton curve. On this basis, the casting height of the ECC casting wall limb, the strength and reinforcement ratio of longitudinal rebar and the position of the ECC beam are studied in detail.

This paper aims to propose a novel system design and control algorithm of lower limb exoskeleton, which provides walking assistance and load sharing for the wearer.

In this paper, the valve-controlled asymmetrical hydraulic cylinder is selected for driving the hip and knee joint of exoskeleton. Pressure shoe is developed that purpose on detecting changes in plantar force, and a fuzzy recognition algorithm using plantar pressure is proposed. Dynamic model of the exoskeleton is established, and the sliding mode control is developed to implement the position tracking of exoskeleton. A series of prototype experiments including benchtop test, full assistance, partial assistance and loaded walking experiments are set up to verify the tracking performance and power-assisted effect of the proposed exoskeleton.

The control performance of PID control and sliding mode control are compared. The experimental data shows the tracking trajectories and tracking errors of sliding mode control and demonstrate its good robustness to nonlinearities. sEMG of the gastrocnemius muscle tends to be significantly weakened during assisted walking.

In this paper, a structure that the knee joint and hip joint driven by the valve-controlled asymmetrical cylinder is used to provide walking assistance for the wearer. The sliding mode control is proposed to deal with the nonlinearities during joint rotation and fluids. It shows great robustness and frequency adaptability through experiments under different motion frequencies and assistance modes. The design and control method of exoskeleton is a good attempt, which takes positive impacts on the productivity or quality of the life of wearers.

To identify environmental and physiological factors that may interact to bring about accidental discharges of firearms; and to make suggestions regarding the training of police officers with the aim of reducing such incidents.

A pistol was fitted with sensors to register the pressure exerted on both the hand‐stock and the trigger of the weapon. Participants then performed 13 different tasks, each of which required the use of different limbs while holding the prepared weapon.

Results suggest that motor activity in different limbs can lead to a significant increase in grip force exerted on a firearm, and that the amount of force exerted on the weapon is dependent on the intensity and type of movement and the limb involved in the movement. For movements of the contralateral arm a tendency for higher forces to be exerted on a weapon during pulling than during pushing movements was found, whereas the force with which the movement was performed did not seem to have any influence. In contrast, for movements involving the legs, findings indicate that increasingly more forceful leg‐movements lead to a progressively higher risk of unintentionally discharging a firearm due to unintended muscle activity, whereas the type of movement does not seem to influence the amount of force produced in the hand carrying the weapon. Generally, the use of the lower limbs appears to offer a greater danger for involuntary discharges resulting from unintended muscle activity than movements involving the contralateral arm.

Results point towards evidence‐based methods for training police officers that may reduce the danger of involuntary discharges of a firearm in a real life situation.

This paper provides the first empirical research to identify which movements of the human body are more or less likely to induce unintended muscle activity in the hand carrying a firearm which may evoke involuntary discharges.

The purpose of this paper is the control of lower limb orthosis acting at the knee joint level for a passive rehabilitation purpose.

A control law, based on a saturated proportional derivative controller, is proposed in order to drive the shank-foot-orthosis system along a desired trajectory.

The proposed control law is tested in real time using the orthosis EICOSI of the LISSI-Laboratory. The experiments show that the proposed control law is capable of providing satisfactory trajectory tracking performance given only the knee joint angle measurement. Moreover, the control law is robust with respect to external disturbances.

Robust control of an actuated lower limb orthosis.

The purpose of this paper is to model and predict suitable gait trajectories of lower-limb exoskeleton for wearer during rehabilitation walking. Lower-limb exoskeleton is widely used for assisting walk in rehabilitation field. One key problem for exoskeleton control is to model and predict suitable gait trajectories for wearer.

In this paper, the authors propose a Deep Spatial-Temporal Model (DSTM) for generating knee joint trajectory of lower-limb exoskeleton, which first leverages Long-Short Term Memory framework to learn the inherent spatial-temporal correlations of gait features.

With DSTM, the pathological knee joint trajectories can be predicted based on subject’s other joints. The energy expenditure is adopted for verifying the effectiveness of new recovery gait pattern by monitoring dynamic heart rate. The experimental results demonstrate that the subjects have less energy expenditure in new recovery gait pattern than in others’ normal gait patterns, which also means the new recovery gait is more suitable for subject.

Long-Short Term Memory framework is first used for modeling rehabilitation gait, and the deep spatial–temporal relationships between joints of gait data can obtained successfully.

This paper seeks to present an inertial motion tracking system for monitoring movements of human upper limbs in order to support a home‐based rehabilitation scheme in which the recovery of stroke patients' motor function through repetitive exercises needs to be continuously monitored and appropriately evaluated.

Two inertial sensors are placed on the upper and lower arms in order to obtain acceleration and turning rates. Then the position of the upper limbs can be deduced by using the kinematical model of the upper limbs that was designed in the previous paper. The tracking system starts from inertial data acquisition and pre‐filtering, followed by a number of processes such as transformation of coordinate systems of sensor data, and kinematical modelling and optimization of position estimation.

The motion detector using the proposed kinematic model only has drifts in the measurements. Fusion of acceleration and orientation data can effectively solve the drift problem without the involvement of a Kalman filter.

The image rendering is not undertaken when the data sampling is performed. This non‐synchronization is applied in order to avoid the breaks in the continuous sampling.

This new motion detector can work in different environments without significant drifts. Also, this system only deploys two inertial sensors but is able to estimate the position of the wrist, elbow and shoulder joints.