Intention recognition of elbow joint based on sEMG using adaptive fuzzy neural network

In this paper, the adaptive fuzzy neural network (AFNN) based on the surface electromyography (sEMG) for estimating the elbow joint angle is established and investigated from the perspective of rapidity and accuracy. In addition, back propagation neural network (BPNN) and artificial neural network of radial basis function (RBFNN), as the classical method for data forecasting, have been applied to estimate the elbow joint angle for comparing with AFNN. Ultimately, the experimental simulation and result analysis demonstrate that the rapidity and accuracy of AFNN is superior to BPNN and RBFNN

An Advanced Adaptive Control of Lower Limb Rehabilitation Robot

Rehabilitation robots play an important role in the rehabilitation field, and effective human-robot interaction contributes to promoting the development of the rehabilitation robots. Though many studies about the human-robot interaction have been carried out, there are still several limitations in the flexibility and stability of the control system. Therefore, we proposed an advanced adaptive control method for lower limb rehabilitation robot. The method was devised with a dual closed loop control strategy based on the surface electromyography (sEMG) and plantar pressure to improve the robustness of the adaptive control for the rehabilitation robots. First, in the outer loop control, an advanced variable impedance controller based on the sEMG and plantar pressure was designed to correct robot's reference trajectory. Then, in the inner loop control, a sliding mode iterative learning controller (SMILC) based on the variable boundary saturation function was designed to achieve the tracking of the reference trajectory. The experiment results showed that, in the designed dual closed loop control strategy, a variable impedance controller can effectively reduce trajectory tracking errors and adaptively modify the reference trajectory synchronizing with the motion intention of patients; the designed sliding mode iterative learning controller can effectively reduce chattering in sliding mode control and excellently achieve the tracking of rehabilitation robot's reference trajectory. This study can improve the performance of the human-robot interaction of the rehabilitation robot system, and expand the application to the rehabilitation field

IISE Trans Occup Ergon Hum Factors

Background:In the literature, efficacy of passive upper limb exoskeletons has been demonstrated in reduced activity of involved muscles during overhead occupational tasks. However, there are fewer studies that have investigated the efficacy of active upper limb exoskeletons or compared them with their passive counterparts.Purpose:We aimed to use an approach simulating human-exoskeleton interactions to compare several passive and active assistance methods in an upper limb exoskeleton and to evaluate how different assistance types affect musculoskeletal loadings during overhead lifting.Methods:An upper-extremity musculoskeletal model was integrated with a five degree-of-freedom exoskeleton for virtual human-in-the-loop evaluation of exoskeleton design and control. Different assistance methods were evaluated, including spring-based activation zones and active control based on EMG, to examine their biomechanical effects on musculoskeletal loadings including interaction forces and moments, muscle activations, and joint moments and reaction forces.Results:Our modeling and simulation results suggest the effectiveness of the proposed passive and active assistance methods in reducing biomechanical loadings\u2014the upper-limb exoskeletons could reduce maximum loading on the shoulder joint by up to 46% compared to the no-exoskeleton situation. Active assistance was found to outperform the passive assistance approach. Specifically, EMG-based active assistance could assist over the whole lifting range and had a larger capability to reduce deltoid muscle activation and shoulder joint reaction force.Conclusions:We used a modeling and simulation approach to virtually evaluate various exoskeleton assistance methods without testing multiple physical prototypes and to investigate the effects of these methods on musculoskeletal loadings that cannot be measured directly or noninvasively. Our findings offer new approaches for testing methods and improving exoskeleton designs with \u201csmart\u201d controls. More research is planned to further optimize the exoskeleton control strategies and validate the simulated results in a real-life implementation.CC999999/ImCDC/Intramural CDC HHSUnited States/2022-01-27T00:00:00Z34254566PMC878993412107vault:4075

Research on Multimodal Fusion Recognition Method of Upper Limb Motion Patterns

In order to solve the problems of single movement pattern recognition information and low recognition accuracy of multijoint upper limb exoskeleton rehabilitation training, a multimodal information fusion method with human surface electromyography (sEMG) and electrocardiogram (ECG) was proposed, and an Inception-Sim model for upper limb motion pattern recognition was designed. Integrating the advantages of multimodal information, inspired by the convolutional neural network processing image classification problem, the original signal was converted into a Gramian angular summation/difference fields-histogram of oriented gradient (GASF/GADF-HOG) image based on the principle of Grameen angle superposition/difference field, and the directional gradient histogram feature of the GASF/GADF image was extracted. The Inception-Sim model was constructed based on the Inception V3 model, and the human motion pattern recognition was completed on the basis of the transfer learning network. VGG16, ResNet-50, and other backbone networks were selected as comparison models. The recognition accuracy of each motion pattern for all participants reaches up to 90%, which is better than that of the control model. The average iteration speed of the proposed Inception-Sim model improved by about 21% compared to the control model. The experimental results show that the proposed multimodal information fusion recognition method can improve the accuracy and iteration speed of the upper limb motion recognition mode and then improve the effect of upper limb rehabilitation training

Variable Damping Control of a Robotic Arm to Improve Trade-off Between Performance and Stability

abstract: Admittance control with fixed damping has been a successful control strategy in previous human-robotic interaction research. This research implements a variable damping admittance controller in a 7-DOF robotic arm coupled with a human subject’s arm at the end effector to study the trade-off of agility and stability and aims to produce a control scheme which displays both fast rise time and stability. The variable damping controller uses a measure of intent of movement to vary damping to aid the user’s movement to a target. The range of damping values is bounded by incorporating knowledge of a human arm to ensure the stability of the coupled human-robot system. Human subjects completed experiments with fixed positive, fixed negative, and variable damping controllers to evaluate the variable damping controller’s ability to increase agility and stability. Comparisons of the two fixed damping controllers showed as fixed damping increased, the coupled human-robot system reacted with less overshoot at the expense of rise time, which is used as a measure of agility. The inverse was also true; as damping became increasingly negative, the overshoot and stability of the system was compromised, while the rise time became faster. Analysis of the variable damping controller demonstrated humans could extract the benefits of the variable damping controller in its ability to increase agility in comparison to a positive damping controller and increase stability in comparison to a negative damping controller.Dissertation/ThesisMasters Thesis Mechanical Engineering 201

Reviewing high-level control techniques on robot-assisted upper-limb rehabilitation

This paper presents a comprehensive review of high-level control techniques for upper-limb robotic training. It aims to compare and discuss the potentials of these different control algorithms, and specify future research direction. Included studies mainly come from selected papers in four review articles. To make selected studies complete and comprehensive, especially some recently-developed upper-limb robotic devices, a search was further conducted in IEEE Xplore, Google Scholar, Scopus and Web of Science using keywords (‘upper limb*’ or ‘upper body*’) and (‘rehabilitation*’ or ‘treatment*’) and (‘robot*’ or ‘device*’ or ‘exoskeleton*’). The search is limited to English-language articles published between January 2013 and December 2017. Valuable references in related publications were also screened. Comparative analysis shows that high-level interaction control strategies can be implemented in a range of methods, mainly including impedance/admittance based strategies, adaptive control techniques, and physiological signal control. Even though the potentials of existing interactive control strategies have been demonstrated, it is hard to identify the one leading to maximum encouragement from human users. However, it is reasonable to suggest that future studies should combine different control strategies to be application specific, and deliver appropriate robotic assistance based on physical disability levels of human users

Robust Impedance Control of a Four Degree of Freedom Exercise Robot

The CSU 4OptimX exercise robot provides a platform for future research into advanced exercise and rehabilitation. The robot and its control system will autonomously modify reference trajectories and impedances on the basis of an optimization criterion and physiological feedback. To achieve this goal, a robust impedance control system with trajectory tracking must be implemented as the foundational control scheme. Two control laws will be compared, sliding mode and H-infinity control. The above robust control laws are combined with underlying impedance control laws to overcome uncertain plant model parameters and disturbance anomalies affecting the input signal. The sliding mode control law is synthesized based on a nominal plant model due to its inherent nature of overcoming unspecified, un-modeled dynamics and disturbances. Implementation of the H-infinity control law uses weights as well as the nominal plant, a structured parametric uncertainty model of the plant, and a model with multiplicative uncertainty. The performance and practicality of each controller is discussed as well as the challenges associated with attempts to implement controllers successfully onto the robot. The findings of this thesis indicate that the closed loop controller with sliding mode is the superior control scheme due to its abilities to counter non-linearities. It is chosen as the platform control scheme. The 2 out of 3 H-infinity controllers performed well in simulation but only one was able to successfully control the robot. Challenges associated with H-infinity control implementation toward impedance control include determining proper weight shapes that balance performance and practicality. This challenge is a starting point for future research into general weight shape determination for H-infinity robust impedance control