sEMG-based impedance control for lower-limb rehabilitation robot

Stretchable, Flexible, Breathable, Self-Adhesive Epidermal Hand sEMG Sensor System

Hand function rehabilitation training typically requires monitoring the activation status of muscles directly related to hand function. However, due to factors such as the small surface area for hand-back electrode placement and significant skin deformation, the continuous real-time monitoring of high-quality surface electromyographic (sEMG) signals on the hand-back skin still poses significant challenges. We report a stretchable, flexible, breathable, and self-adhesive epidermal sEMG sensor system. The optimized serpentine structure exhibits a sufficient stretchability and filling ratio, enabling the high-quality monitoring of signals. The carving design minimizes the distribution of connecting wires, providing more space for electrode reservation. The low-cost fabrication design, combined with the cauterization design, facilitates large-scale production. Integrated with customized wireless data acquisition hardware, it demonstrates the real-time multi-channel sEMG monitoring capability for muscle activation during hand function rehabilitation actions. The sensor provides a new tool for monitoring hand function rehabilitation treatments, assessing rehabilitation outcomes, and researching areas such as prosthetic control.

Force/position-based velocity control strategy for the lower limb rehabilitation robot during active training: design and validation

Aiming at the shortcomings of most existing control strategies for lower limb rehabilitation robots that are difficult to guarantee trajectory tracking effect and active participation of the patient, this paper proposes a force/position-based velocity control (FPVC) strategy for the hybrid end-effector lower limb rehabilitation robot (HE-LRR) during active training. The configuration of HE-LRR is described and the inverse Jacobian analysis is carried out. Then, the FPVC strategy design is introduced in detail, including normal velocity planning and tangential velocity planning. The experimental platform for the HE-LRR system is presented. A series of experiments are conducted to validate the FPVC strategy's performance, including trajectory measurement experiments, force and velocity measurement experiments, and active participation experiments. Experimental studies show that the end effector possesses good following performance with the reference trajectory and the desired velocity, and the active participation of subjects can be adjusted by the control strategy parameters. The experiments have verified the rationality of the FPVC strategy, which can meet the requirements of trajectory tracking effect and active participation, indicating its good application prospects in the patient's robot-assisted active training.

Lower limb exoskeleton robot and its cooperative control: A review, trends, and challenges for future research

Effective control of an exoskeleton robot (ER) using a human-robot interface is crucial for assessing the robot's movements and the force they produce to generate efficient control signals. Interestingly, certain surveys were done to show off cutting-edge exoskeleton robots. The review papers that were previously published have not thoroughly examined the control strategy, which is a crucial component of automating exoskeleton systems. As a result, this review focuses on examining the most recent developments and problems associated with exoskeleton control systems, particularly during the last few years (2017-2022). In addition, the trends and challenges of cooperative control, particularly multi-information fusion, are discussed.

sEMG-Based Continuous Hand Action Prediction by Using Key State Transition and Model Pruning

Conventional classification of hand motions and continuous joint angle estimation based on sEMG have been widely studied in recent years. The classification task focuses on discrete motion recognition and shows poor real-time performance, while continuous joint angle estimation evaluates the real-time joint angles by the continuity of the limb. Few researchers have investigated continuous hand action prediction based on hand motion continuity. In our study, we propose the key state transition as a condition for continuous hand action prediction and simulate the prediction process using a sliding window with long-term memory. Firstly, the key state modeled by GMM-HMMs is set as the condition. Then, the sliding window is used to dynamically look for the key state transition. The prediction results are given while finding the key state transition. To extend continuous multigesture action prediction, we use model pruning to improve reusability. Eight subjects participated in the experiment, and the results show that the average accuracy of continuous two-hand actions is 97% with a 70 ms time delay, which is better than LSTM (94.15%, 308 ms) and GRU (93.83%, 300 ms). In supplementary experiments with continuous four-hand actions, over 85% prediction accuracy is achieved with an average time delay of 90 ms.

sEMG-Based Gain-Tuned Compliance Control for the Lower Limb Rehabilitation Robot during Passive Training

The lower limb rehabilitation robot is a typical man-machine coupling system. Aiming at the problems of insufficient physiological information and unsatisfactory safety performance in the compliance control strategy for the lower limb rehabilitation robot during passive training, this study developed a surface electromyography-based gain-tuned compliance control (EGCC) strategy for the lower limb rehabilitation robot. First, the mapping function relationship between the normalized surface electromyography (sEMG) signal and the gain parameter was established and an overall EGCC strategy proposed. Next, the EGCC strategy without sEMG information was simulated and analyzed. The effects of the impedance control parameters on the position correction amount were studied, and the change rules of the robot end trajectory, man-machine contact force, and position correction amount analyzed in different training modes. Then, the sEMG signal acquisition and feature analysis of target muscle groups under different training modes were carried out. Finally, based on the lower limb rehabilitation robot control system, the influence of normalized sEMG threshold on the robot end trajectory and gain parameters under different training modes was experimentally studied. The simulation and experimental results show that the adoption of the EGCC strategy can significantly enhance the compliance of the robot end-effector by detecting the sEMG signal and improve the safety of the robot in different training modes, indicating the EGCC strategy has good application prospects in the rehabilitation robot field.

Model Analysis and Experimental Study of Lower Limb Rehabilitation Training Device Based on Gravity Balance

More hemiplegia patients tend to use equipment for rehabilitation training due to the lack of physical therapists and the low effect of manual training. Nowadays, lower limb rehabilitation training devices for patients in grade 2 of the Medical Research Council (MRC-2) scale are still scarce and have some issues of poor autonomy and cannot relieve the muscle weakness of patients. To address these problems, a prototype based on gravity balance was designed with the combination of springs and linkages to enable patients to passively experience the rehabilitation training in the state of balancing the gravity of lower limbs. The motion of the mechanism was analyzed to obtain the functional relation between the motor rotation angle and the joints’ angle. Based on the principle of constant potential energy, a gravity balance mathematical model of the device was established, analyzed, and simulated. Moreover, through the training experiment, the results show that when subjects in three different weights were trained under the rehabilitation device with and without gravity balance, the required torques of the motor and EMG signal strength of the knee and hip joints decreased by a degree of significance, which verified the effectiveness of the device’s gravity balancing characteristics for MRC-2 patients.

Brain emotional learning impedance control of uncertain nonlinear systems with time delay: Experiments on a hybrid elastic joint robot in telesurgery

Telesurgical robot control is a significant example of an uncertain nonlinear system, as it involves various complexities, including unknown master/slave dynamics, environmental uncertainties, joint elasticities, and communication time delays. This problem becomes even more complicated when desirable properties such as stability, transparency, rigidity, accuracy, and fine manipulability are considered. We consider an elastic joint telesurgical robot architecture that combines two parallel and serial manipulators to achieve the desired rigidity, accuracy, and fine manipulability. For this purpose, we propose using Brain Emotional Learning (BEL) to estimate the robot's uncertain nonlinear dynamics. In contrast to recent stability analyses of BEL-based systems, we employ Lyapunov theory to achieve the closed-loop system's general stability independent of robot dynamics and chattering. Furthermore, the proposed control architecture implements two reference impedance models for the master and slave robots' trajectory generation and makes a trade-off between transparency and stability by simultaneously considering optimal position synchronization and transparency conditions. In this regard, we extend these two conditions in absolute stability theory and Llewellyn's criterion to obtain the allowable bound of communication time delay. The proposed robot is designed and experimentally implemented at the Robotics Laboratories at FUM and SUT Universities. Along with confirming the theoretical results, simulations and laboratory experiments demonstrate that a reasonable trade-off between stability and transparency is made in four realistic case studies with and without communication time delays.

A safe human–robot interactive control structure with human arm movement detection for an upper-limb wearable robot used during lifting tasks

Manual lifting tasks involve repetitive raising, holding and stacking movements with heavy objects. These arm movements are notable risk factors for muscle pain, fatigue, and musculoskeletal disorders in workers. An upper-limb wearable robot, as a 6-DOF dual-arm exoskeleton, which was designed to augment workers’ strength and minimize muscular activation in the arm during repetitive lifting tasks. To adjust the robot joint trajectory, the user needs to apply an interactive torque to operate the robot during lifting tasks when a standard virtual mechanical impedance control structure is used. To reduce overshooting of the interactive torque on the user’s joint, a three-tier hierarchical control structure was developed for the robot in this study. At the highest level, a human arm movement detection module is used to detect the user’s arm motion according to the surface electromyography signals. Then, a Hammerstein adaptive virtual mechanical impedance controller is used at the middle level to reduce overshooting and yield an acceptable value of torque for the user’s elbow joint in actual lifting tasks. At the lowest level, the actuator controller on each joint of the robot controls the robot to complete lifting tasks. Several experiments were conducted, and the results showed that the interactive torque on the user’s elbow was limited and the muscular activations of erector spinae and biceps brachii muscles were effectively decreased. The proposed scheme prevents potential harm to the user due to excessive interactive torque on the human elbow joint, such as related muscle fatigue and joint injuries.

Bi-Level Adaptive Computed-Current Impedance Controller for Electrically Driven Robots

This paper presents a bi-level adaptive computed-current impedance controller for electrically driven robots. This study aims to reduce calculation complexities by utilizing the electrical equations of actuators, instead of the entire model of the electromechanical system. Moreover, taking the dynamical effects of mechanical parts into account through the current’s feedback, external disturbances are compensated. In order to handle uncertainties, a bi-level optimization problem is formulated to obtain guaranteed stability besides the estimation convergence. An adaptation rule and its optimal tuning gain are achieved. The proposed method is applied to control of a rehabilitation robot to evaluate its performance.

Dynamic output feedback control for a walking assistance training robot to handle shifts in the center of gravity and time-varying arm of force in omniwheel

In this study, we designed a dynamic output feedback control for a walking assistance training robot. The proposed system addresses the problems of shifts in the center of gravity and vibration associated with time-varying arm of force in omniwheel to improve the accuracy of the trajectory tracking of the walking assistance training robot. The dynamic output feedback controller was developed by constructing a velocity observer on a stochastic dynamic model of the walking assistance training robot via integration with a Lyapunov function. An analysis of the time-varying arm of force in omniwheel revealed that it increased the accuracy of the walking assistance training robot dynamic model. Thus, an adaptive law was designed such that the vibration caused by the time-varying arm of force in omniwheel was eliminated. The mean absolute practical stability in the position and velocity tracking errors was verified based on Young’s inequality and stochastic stability theory. The simulation results show that the walking assistance training robot with the shifts in the center of gravity was able to track a designed trajectory and that the application of the adaptive law effectively eliminates the vibrations caused by the time-varying arm of force in omniwheel.

New Motion Intention Acquisition Method of Lower Limb Rehabilitation Robot Based on Static Torque Sensors

The rehabilitation robot is an application of robotic technology for people with limb disabilities. This paper investigates a new applicable and effective sitting/lying lower limb rehabilitation robot (the LLR-Ro). In order to improve the patient’s training initiative and accelerate the rehabilitation process, a new motion intention acquisition method based on static torque sensors is proposed. This motion intention acquisition method is established through the dynamics modeling of human–machine coordination, which is built on the basis of Lagrangian equations. Combined with the static torque sensors installed on the mechanism leg joint axis, the LLR-Ro can obtain the active force from the patient’s leg. Based on the variation of the patient’s active force and the kinematic functional relationship of the patient’s leg end point, the patient motion intention is obtained and used in the proposed active rehabilitation training method. The simulation experiment demonstrates the correctness of mechanism leg dynamics equations through ADAMS software and MATLAB software. The calibration experiment of the joint torque sensors’ combining limit range filter with an average value filter provides the hardware support for active rehabilitation training. The consecutive variation of the torque sensors from just the mechanism leg weight, as well as both the mechanism leg and the patient leg weights, obtains the feasibility of lower limb motion intention acquisition.

A Tutorial Survey and Comparison of Impedance Control on Robotic Manipulation

There have been significant interests and efforts in the field of impedance control on robotic manipulation over last decades. Impedance control aims to achieve the desired mechanical interaction between the robotic equipment and its environment. This paper gives the overview and comparison of basic concepts and principles, implementation strategies, crucial techniques, and practical applications concerning the impedance control of robotic manipulation. This work attempts to serve as a tutorial to people outside the field and to promote discussion of a unified vision of impedance control within the field of robotic manipulation. The goal is to help readers quickly get into the problems of their interests related to impedance control of robotic manipulation and to provide guidance and insights in finding appropriate strategies and solutions.