Multi-Sensor Fusion-Based Mirror Adaptive Assist-as-Needed Control Strategy of a Soft Exoskeleton for Upper Limb Rehabilitation

Li, Ning; Yang, Yang; Li, Gang; Yang, Tié; Wang, Yihan; Chen, Wenyuan; Yu, Peng; Xue, Xiujuan; Zhang, Chuang; Wang, Wenxue; Xi, Ning; Liu, Lianqing

doi:10.1109/tase.2022.3225727

Cited by 16 publications

(5 citation statements)

References 46 publications

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“…However, too much assistance may make patients slack off, and too little assistance will not help patients implement training tasks—both may reduce rehabilitation effects. In order to realize efficient rehabilitation training, human–robot interaction methods need to follow the assisted-as-need (AAN) principle ( Li N. et al, 2024 ). At present, impedance control is usually used to implement the AAN strategy ( Han et al, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

Multi-mode adaptive control strategy for a lower limb rehabilitation robot

Liang,

Yan,

Dai

et al. 2024

Front. Bioeng. Biotechnol.

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Different patients have different rehabilitation requirements. It is essential to ensure the safety and comfort of patients at different recovery stages during rehabilitation training. This study proposes a multi-mode adaptive control method to achieve a safe and compliant rehabilitation training strategy. First, patients’ motion intention and motor ability are evaluated based on the average human–robot interaction force per task cycle. Second, three kinds of rehabilitation training modes—robot-dominant, patient-dominant, and safety-stop—are established, and the adaptive controller can dexterously switch between the three training modes. In the robot-dominant mode, based on the motion errors, the patient’s motor ability, and motion intention, the controller can adaptively adjust its assistance level and impedance parameters to help patients complete rehabilitation tasks and encourage them to actively participate. In the patient-dominant mode, the controller only adjusts the training speed. When the trajectory error is too large, the controller switches to the safety-stop mode to ensure patient safety. The stabilities of the adaptive controller under three training modes are then proven using Lyapunov theory. Finally, the effectiveness of the multi-mode adaptive controller is verified by simulation results.

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Section: Introductionmentioning

confidence: 99%

Multi-mode adaptive control strategy for a lower limb rehabilitation robot

Liang,

Yan,

Dai

et al. 2024

Front. Bioeng. Biotechnol.

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“…The assessment of patients' function abilities is predominantly categorized into two main approaches: the biomechanical model-based method [7,8] and the motor performance-based method [7,[10][11][12][13]. In the context of biomechanical modeling, a skeletal muscle model is usually constructed and analyzed using biomechanical theories.…”

Section: Introductionmentioning

confidence: 99%

“…Building upon the techniques employed to assess a patient's motor functional ability, the determination of actual assistive torque and the specific implementation method of real-time control have emerged as focal points of research in AAN control. These research areas encompass several approaches, such as direct adjustment of assistive force/torque through force control methods [8,9,12], adaptive adjustment of impedance/admittance coefficients to achieving force/position interaction performance [7,13], and intelligent learning algorithms [14,15]. Shawgi Younis et al [7] applied an adaptive inertia-related torque controller.…”

Section: Introductionmentioning

confidence: 99%

“…This calculated task's strength requirement was then compared with the operator's strength capability to gauge the assistance force which was the input of the admittance controller. In [12], an upper limb mirror control strategy based on adaptive AAN was proposed. This adaptive AAN module combined the traditional impedance control with a method for assessing the movement state of the affected limb.…”

Section: Introductionmentioning

confidence: 99%

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Patient’s Healthy Limb Motion Characteristics Based Assist-as-Needed Control Strategy for Upper-Limb Rehabilitation Robot

Guo,

Li,

Huang

et al. 2024

Preprint

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The implementation of a progressive rehabilitation training model to promote patients’ motivation efforts can greatly restore the damaged central nervous system function in patients. The patients’ active engagement can be effectively stimulated by assist-as-needed (AAN) robot rehabilitation training. However, its application in robotic therapy has been hindered by a simple determination method of robot assisted torque which focuses on the evaluation only of the affected limb's movement ability. Moreover, the expected effect of assistance depends on the designer, which deviates from the patient's expectations, and its applicability to different patients is deficient. In this study, we propose a control method with personalized treatment features based on the idea of estimating and mapping the stiffness of the patient’s healthy limbs. This control method comprises an interactive control module in the task-oriented space based on the quantitative evaluation of motion needs and an inner loop position control module for the pneumatic swing cylinder in the joint space. An upper limb endpoint stiffness estimation model is constructed and a parameter identification algorithm is designed. The upper limb endpoint stiffness which characterizes the patient's ability to complete training movements is obtained by collecting surface electromyographic (sEMG) signals and human-robot interaction forces during patient movement. Then the motor needs of the affected limb when completing the same movement are quantified based on the performance of the healthy limb. A stiffness mapping algorithm is designed to dynamically adjust the rehabilitation training trajectory and auxiliary force of the robot based on the actual movement ability of the affected limb, achieving AAN control. Experimental studies were conducted on a self-developed pneumatic upper limb rehabilitation robot, and the results showed that the proposed AAN control method can effectively estimate the patient's movement needs and achieve progressive rehabilitation training. The rehabilitation training robot that simulates the movement characteristics of the patient's healthy limbs drives the affected limb, making the intensity of the rehabilitation training task more in line with the patient's pre-morbid limb use habits, and also beneficial for the consistency of bilateral limb movements.

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“…Currently, AAN controllers have been applied to upper [28][29][30] and lower [31][32][33] limb rehabilitation to stimulate active patient participation in rehabilitation training. AAN controllers for virtual channels based on task trajectories have also been developed [26,27].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Adaptive Passive Control Strategy Design for Upper-Limb End-Effector Rehabilitation Robot

Jing-yan

et al. 2023

Sensors

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Robot-assisted rehabilitation therapy has been proven to effectively improve upper-limb motor function in stroke patients. However, most current rehabilitation robotic controllers will provide too much assistance force and focus only on the patient’s position tracking performance while ignoring the patient’s interactive force situation, resulting in the inability to accurately assess the patient’s true motor intention and difficulty stimulating the patient’s initiative, thus negatively affecting the patient’s rehabilitation outcome. Therefore, this paper proposes a fuzzy adaptive passive (FAP) control strategy based on subjects’ task performance and impulse. To ensure the safety of subjects, a passive controller based on the potential field is designed to guide and assist patients in their movements, and the stability of the controller is demonstrated in a passive formalism. Then, using the subject’s task performance and impulse as evaluation indicators, fuzzy logic rules were designed and used as an evaluation algorithm to quantitively assess the subject’s motor ability and to adaptively modify the stiffness coefficient of the potential field and thus change the magnitude of the assistance force to stimulate the subject’s initiative. Through experiments, this control strategy has been shown to not only improve the subject’s initiative during the training process and ensure their safety during training but also enhance the subject’s motor learning ability.

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Multi-Sensor Fusion-Based Mirror Adaptive Assist-as-Needed Control Strategy of a Soft Exoskeleton for Upper Limb Rehabilitation

Cited by 16 publications

References 46 publications

Multi-mode adaptive control strategy for a lower limb rehabilitation robot

Multi-mode adaptive control strategy for a lower limb rehabilitation robot

Patient’s Healthy Limb Motion Characteristics Based Assist-as-Needed Control Strategy for Upper-Limb Rehabilitation Robot

Fuzzy Adaptive Passive Control Strategy Design for Upper-Limb End-Effector Rehabilitation Robot

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