scholarly journals Human–robot interactive control based on reinforcement learning for gait rehabilitation training robot

2019 ◽  
Vol 16 (2) ◽  
pp. 172988141983958 ◽  
Author(s):  
Guo Bingjing ◽  
Han Jianhai ◽  
Li Xiangpan ◽  
Yan Lin
Keyword(s):  
Reinforcement Learning ◽  
Lower Limb ◽  
Position Control ◽  
Human Robot Interaction ◽  
Experimental Results ◽  
Gait Rehabilitation ◽  
Sigmoid Function ◽  
Interactive Control ◽  
Rehabilitation Training ◽  
Active Rehabilitation

A human–robot interactive control is proposed to govern the assistance provided by a lower limb exoskeleton robot to patients in the gait rehabilitation training. The rehabilitation training robot with two lower limb exoskeletons is driven by the pneumatic proportional servo system and has two rotational degrees of freedom of each lower limb. An adaptive admittance model is adopted considering its suitability for human–robot interaction. The adaptive law of the admittance parameters is designed with Sigmoid function and the reinforcement learning algorithm. Individualized admittance parameters suitable for patients are obtained by reinforcement learning. Experiments in passive and active rehabilitation training modes were carried out to verify the proposed control method. The passive rehabilitation training experimental results verify the effectiveness of the inner-loop position control strategy, which can meet the demands of gait tracking accuracy in rehabilitation training. The active rehabilitation training experimental results demonstrate that the personal adaption and active compliance are provided by the interactive controller in the robot-assistance for patients. The combined effects of flexibility of pneumatic actuators and compliance provided by the controller contribute to the training comfort, safety, and therapeutic outcome in the gait rehabilitation.

scholarly journals Design of an Active and Passive Control System of Hand Exoskeleton for Rehabilitation

2019 ◽  
Vol 9 (11) ◽  
pp. 2291 ◽  
Author(s):  
Fuhai Zhang ◽  
Legeng Lin ◽  
Lei Yang ◽  
Yili Fu
Keyword(s):  
Control System ◽  
Passive Control ◽  
Control Method ◽  
Human Robot Interaction ◽  
Impedance Method ◽  
Hand Rehabilitation ◽  
Inverse Dynamic ◽  
Rehabilitation Training ◽  
Hand Exoskeleton ◽  
Active Rehabilitation

Aiming at stroke patients’ hand rehabilitation training, we present a hand exoskeleton with both active and passive control modes for neural rehabilitation. The exoskeleton control system is designed as a human–robot interaction control system based on field-programmable gate array (FPGA) and Android mobile terminal with good portability and openness. Passive rehabilitation pattern based on proportional derivative (PD) inverse dynamic control method and active rehabilitation pattern based on impedance method, are established respectively. By the comparison of the threshold value and the force on the fingertip of the exoskeleton from the sensor, the automatic switch between active and passive rehabilitation mode is accomplished. The hand model is built in Android environment that can synchronize the movement of the hand. It can also induce patients to participate in rehabilitation training actively. To verify the proposed control approach, we set up and conduct an experiment to do the passive rehabilitation mode, active rehabilitation mode, and active plus passive mode experimental researches. The experiment results effectively verify the feasibility of the exoskeleton system fulfilling the proposed control strategy.

Experimental Research on Trajectory Control of Walking Rehabilitation Training Robot

2012 ◽  
Vol 187 ◽  
pp. 177-185 ◽  
Author(s):  
Zhong Wen ◽  
Jin Wu Qian ◽  
Lin Yong Shen ◽  
Ya Nan Zhang
Keyword(s):  
Impedance Control ◽  
Control Strategies ◽  
Experimental Tests ◽  
Training Model ◽  
Interaction Force ◽  
Human Robot Interaction ◽  
Rehabilitation Training ◽  
Position Servo Control ◽  
Walking Rehabilitation ◽  
Active Rehabilitation

In order to improve the effect of smooth motion, the impedance control is introduced into the control system of walking rehabilitation training robot. An impedance control strategy for active rehabilitation training model is proposed and analyzed, and an adaptive control algorithm is proposed on that basis. The experimental tests of three control methods including PD position servo control, impedance control and adaption control are implemented on the robot prototype. The experimental results show that the robot can continuously adjust its gait trajectory according to the human-robot interaction force to meet the expected gait characteristics under the latter two control strategies. The feasibility and validity of the two control strategies is proved, which will be used in active rehabilitation training.

scholarly journals Control and Implementation of 2-DOF Lower Limb Exoskeleton Experiment Platform

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Zhenlei Chen ◽  
Qing Guo ◽  
Huiyu Xiong ◽  
Dan Jiang ◽  
Yao Yan
Keyword(s):  
Lower Limb ◽  
Degrees Of Freedom ◽  
Position Control ◽  
Interaction Force ◽  
Human Robot Interaction ◽  
Detection Accuracy ◽  
Robot Interaction ◽  
Experiment Platform ◽  
Lower Limb Exoskeleton

AbstractIn this study, a humanoid prototype of 2-DOF (degrees of freedom) lower limb exoskeleton is introduced to evaluate the wearable comfortable effect between person and exoskeleton. To improve the detection accuracy of the human-robot interaction torque, a BPNN (backpropagation neural networks) is proposed to estimate this interaction force and to compensate for the measurement error of the 3D-force/torque sensor. Meanwhile, the backstepping controller is designed to realize the exoskeleton's passive position control, which means that the person passively adapts to the exoskeleton. On the other hand, a variable admittance controller is used to implement the exoskeleton's active follow-up control, which means that the person's motion is motivated by his/her intention and the exoskeleton control tries best to improve the human-robot wearable comfortable performance. To improve the wearable comfortable effect, serval regular gait tasks with different admittance parameters and step frequencies are statistically performed to obtain the optimal admittance control parameters. Finally, the BPNN compensation algorithm and two controllers are verified by the experimental exoskeleton prototype with human-robot cooperative motion.

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

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3439 ◽  
Author(s):  
Yongfei Feng ◽  
Hongbo Wang ◽  
Luige Vladareanu ◽  
Zheming Chen ◽  
Di Jin
Keyword(s):  
Lower Limb ◽  
Rehabilitation Robot ◽  
Active Force ◽  
Rehabilitation Training ◽  
Active Rehabilitation ◽  
Limb Rehabilitation ◽  
Static Torque ◽  
Lower Limb Rehabilitation ◽  
Acquisition Method ◽  
Motion Intention

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.

Impedance Control Based Sliding Mode for Lower Limb Rehabilitation Robot

2014 ◽  
Vol 672-674 ◽  
pp. 1770-1773 ◽  
Author(s):  
Fu Cheng Cao ◽  
Li Min Du
Keyword(s):  
Dynamic Response ◽  
Sliding Mode Control ◽  
Lower Limb ◽  
Control Method ◽  
Sliding Mode ◽  
Impedance Control ◽  
Rehabilitation Robot ◽  
Active Rehabilitation ◽  
Limb Rehabilitation ◽  
Lower Limb Rehabilitation

Aimed at improving the dynamic response of the lower limb for patients, an impedance control method based on sliding mode was presented to implement an active rehabilitation. Impedance control can achieve a target-reaching training without the help of a therapist and sliding mode control has a robustness to system uncertainty and vary limb strength. Simulations demonstrate the efficacy of the proposed method for lower limb rehabilitation.

scholarly journals Systematic review on wearable lower-limb exoskeletons for gait training in neuromuscular impairments

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Antonio Rodríguez-Fernández ◽  
Joan Lobo-Prat ◽  
Josep M. Font-Llagunes
Keyword(s):  
Systematic Review ◽  
Clinical Efficacy ◽  
Lower Limb ◽  
Gait Training ◽  
Health Conditions ◽  
Current Evidence ◽  
Clinical Aspects ◽  
Gait Rehabilitation ◽  
Comprehensive Overview ◽  
Walking Aids

AbstractGait disorders can reduce the quality of life for people with neuromuscular impairments. Therefore, walking recovery is one of the main priorities for counteracting sedentary lifestyle, reducing secondary health conditions and restoring legged mobility. At present, wearable powered lower-limb exoskeletons are emerging as a revolutionary technology for robotic gait rehabilitation. This systematic review provides a comprehensive overview on wearable lower-limb exoskeletons for people with neuromuscular impairments, addressing the following three questions: (1) what is the current technological status of wearable lower-limb exoskeletons for gait rehabilitation?, (2) what is the methodology used in the clinical validations of wearable lower-limb exoskeletons?, and (3) what are the benefits and current evidence on clinical efficacy of wearable lower-limb exoskeletons? We analyzed 87 clinical studies focusing on both device technology (e.g., actuators, sensors, structure) and clinical aspects (e.g., training protocol, outcome measures, patient impairments), and make available the database with all the compiled information. The results of the literature survey reveal that wearable exoskeletons have potential for a number of applications including early rehabilitation, promoting physical exercise, and carrying out daily living activities both at home and the community. Likewise, wearable exoskeletons may improve mobility and independence in non-ambulatory people, and may reduce secondary health conditions related to sedentariness, with all the advantages that this entails. However, the use of this technology is still limited by heavy and bulky devices, which require supervision and the use of walking aids. In addition, evidence supporting their benefits is still limited to short-intervention trials with few participants and diversity among their clinical protocols. Wearable lower-limb exoskeletons for gait rehabilitation are still in their early stages of development and randomized control trials are needed to demonstrate their clinical efficacy.

Kinematic dexterity analysis of human-robot interaction of an upper limb rehabilitation robot

2020 ◽  
pp. 1-17
Author(s):  
Qing Sun ◽  
Shuai Guo ◽  
Leigang Zhang
Keyword(s):  
Upper Limb ◽  
Constraint Equation ◽  
Human Robot Interaction ◽  
Rehabilitation Robot ◽  
Connecting Rod ◽  
Robot Interaction ◽  
Upper Limb Rehabilitation ◽  
Rehabilitation Training ◽  
Limb Rehabilitation ◽  
Training Trajectory

BACKGROUND: The definition of rehabilitation training trajectory is of great significance during rehabilitation training, and the dexterity of human-robot interaction motion provides a basis for selecting the trajectory of interaction motion. OBJECTIVE: Aimed at the kinematic dexterity of human-robot interaction, a velocity manipulability ellipsoid intersection volume (VMEIV) index is proposed for analysis, and the dexterity distribution cloud map is obtained with the human-robot cooperation space. METHOD: Firstly, the motion constraint equation of human-robot interaction is established, and the Jacobian matrix is obtained based on the speed of connecting rod. Then, the Monte Carlo method and the cell body segmentation method are used to obtain the collaborative space of human-robot interaction, and the VMEIV of human-robot interaction is solved in the cooperation space. Finally, taking the upper limb rehabilitation robot as the research object, the dexterity analysis of human-robot interaction is carried out by using the index of the approximate volume of the VMEIV. RESULTS: The results of the simulation and experiment have a certain consistency, which indicates that the VMEIV index is effective as an index of human-robot interaction kinematic dexterity. CONCLUSIONS: The VMEIV index can measure the kinematic dexterity of human-robot interaction, and provide a reference for the training trajectory selection of rehabilitation robot.

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