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

Guo, Baolong; Han, Jiang; Li, Xiangpan; Lin, Yan

doi:10.1177/1729881419839584

Cited by 32 publications

(30 citation statements)

References 29 publications

(38 reference statements)

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“…In practice, impedance control can be implemented as an M/K/B (inertia/stiffness/damping) based dynamical system relating joint angles to torques [47,49,89,127,137,153,210,[219][220][221][222][223]. Either a reference target trajectory is played back over time [38,145,206], or the target is fixed and changes (also the stiffness and damping) only when the gait state changes [137,166,172,180,[224][225][226].…”

Section: Action Sublayermentioning

confidence: 99%

Review of control strategies for lower-limb exoskeletons to assist gait

Baud

Manzoori

Ijspeert

et al. 2021

J NeuroEngineering Rehabil

129

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Background Many lower-limb exoskeletons have been developed to assist gait, exhibiting a large range of control methods. The goal of this paper is to review and classify these control strategies, that determine how these devices interact with the user. Methods In addition to covering the recent publications on the control of lower-limb exoskeletons for gait assistance, an effort has been made to review the controllers independently of the hardware and implementation aspects. The common 3-level structure (high, middle, and low levels) is first used to separate the continuous behavior (mid-level) from the implementation of position/torque control (low-level) and the detection of the terrain or user’s intention (high-level). Within these levels, different approaches (functional units) have been identified and combined to describe each considered controller. Results 291 references have been considered and sorted by the proposed classification. The methods identified in the high-level are manual user input, brain interfaces, or automatic mode detection based on the terrain or user’s movements. In the mid-level, the synchronization is most often based on manual triggers by the user, discrete events (followed by state machines or time-based progression), or continuous estimations using state variables. The desired action is determined based on position/torque profiles, model-based calculations, or other custom functions of the sensory signals. In the low-level, position or torque controllers are used to carry out the desired actions. In addition to a more detailed description of these methods, the variants of implementation within each one are also compared and discussed in the paper. Conclusions By listing and comparing the features of the reviewed controllers, this work can help in understanding the numerous techniques found in the literature. The main identified trends are the use of pre-defined trajectories for full-mobilization and event-triggered (or adaptive-frequency-oscillator-synchronized) torque profiles for partial assistance. More recently, advanced methods to adapt the position/torque profiles online and automatically detect terrains or locomotion modes have become more common, but these are largely still limited to laboratory settings. An analysis of the possible underlying reasons of the identified trends is also carried out and opportunities for further studies are discussed.

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Section: Action Sublayermentioning

confidence: 99%

Review of control strategies for lower-limb exoskeletons to assist gait

Baud

Manzoori

Ijspeert

et al. 2021

J NeuroEngineering Rehabil

129

View full text Add to dashboard Cite

show abstract

“…Recent years have witnessed increasing application of optimization techniques in control of LLEs in order to better meet different control objectives, which can be 1) quick adaptation to different user' condition or preference [170]- [172], 2) minimizing human joint torque or metabolic cost [173], [174], and 3) faster comfortable walking [175], etc. These techniques have been employed to perform parameter optimization [171], [173], [176], trajectory optimization [12], [172], [174], [175], [177], and control law optimization (optimal control) [178]- [180].…”

Section: ) Optimization Techniquesmentioning

confidence: 99%

“…For example, the performance of Bayesian Optimization and Evolution Strategy were evaluated respectively in [173] to find the best set of energy shaping factors that minimize human joint torque exerted. Moreover, reinforcement learning technique SARSA was employed in [176] to adjust the parameters in admittance controller for the purpose of encouraging patient force input in active rehabilitation training.…”

Section: ) Optimization Techniquesmentioning

confidence: 99%

Review on Control Strategies for Lower Limb Rehabilitation Exoskeletons

Cao²,

Zhu

2021

IEEE Access

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Research on lower limb exoskeleton (LLE) for rehabilitation have developed rapidly to meet the need of the population with neurologic injuries. LLEs for rehabilitation include therapeutic LLEs that aim to restore walking ability for patients, and assistive LLEs that offer support on activities in daily life. A substantial part of them can serve both purposes. However, these devices are yet to reach the final goal of performing human-machine joint movement agilely and smartly. Control strategy plays an important role in achieving their designed goal. At present, control strategies face three major challenges: how to detect human intention, how to do motion control with given intentions, and how to optimize control parameters to suit different individuals. As a contribution, this paper offers an overview on the state-of-the-art control strategies for rehabilitation LLEs by classifying them into eight categories, each of which is presented with a technical summary and tabulated information of representative papers. Moreover, current approaches addressing the three challenges are discussed in a macroscopic perspective. Finally, it has been explored which requirements the future control strategies should meet for maximizing the performance of rehabilitation LLEs.

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“…Human computer interaction control can provide patients with good personal adaptability and active compliance in rehabilitation treatment. It also improves the training comfort, safety and rehabilitation effect in gait rehabilitation [6]. Moreover, the human-computer interaction system can supplement the gait rehabilitation plan of patients, reduce the workload of doctors, give patients motivation and encouragement in the treatment process, and improve the patient participation and performance in the treatment process [7,8,9].…”

Section: Introductionmentioning

confidence: 99%

Research on Intelligent Following Motion Control System of Rehabilitation Robot

Miao

Gao

Zhao

et al. 2021

Preprint

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Background: In response to the current problem of low intelligence of mobile lower limb motor rehabilitation aids, this article proposes an intelligent control scheme based on human movement behavior in order to control the rehabilitation robot to follow the patient's movement. Methods: Firstly, a multi-sensor data acquisition system is designed according to the motion characteristics of human body. By analyzing and processing the motion data, the change law of human center of gravity and behavior intention are obtained, and the behavior intention of human is used as the control command of the robot following motion. In order to achieve the goal of the rehabilitation robot following human motion, an adaptive radial basis function neural network (ARBFNN) sliding mode controller is designed based on the robot dynamic model. The controller can reduce the impact of fluctuations in the human center of gravity on changes in the parameters of the robot control system, and enhance the adaptability of the system to other disturbance factors, and improve the accuracy of following human motion. Finally, the motion following experiment of the rehabilitation robot is carried out. Results: The experimental results show that the robot can recognize the motion intention of human body, and achieve the training goal of following different subjects to complete straight lines and curves. Conclusions: According to the experimental results, the accuracy of the multi-sensor data acquisition system and control algorithm design is verified, which demonstrates the feasibility of the proposed intelligent control scheme.

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

Cited by 32 publications

References 29 publications

Review of control strategies for lower-limb exoskeletons to assist gait

Review of control strategies for lower-limb exoskeletons to assist gait

Review on Control Strategies for Lower Limb Rehabilitation Exoskeletons

Research on Intelligent Following Motion Control System of Rehabilitation Robot

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