A reward–punishment feedback control strategy based on energy information for wrist rehabilitation

Wang, Jiajin; Zhang, Jiaji; Zuo, Guokun; Shi, Changcheng; Guo, Shuai

doi:10.1177/1729881420940651

Cited by 11 publications

(7 citation statements)

References 31 publications

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“…The rehabilitation doctor observes the actual action and the model action displayed by the mobile terminal when the patient uses the device. The evaluation conclusions are as follows: ① The model action has a high degree of reduction relative to the actual action of the patient, and the action is accurate and continuous, which can replace manual monitoring, and does not use cameras, and does not infringe on the privacy of the patient; ② the range of motion displayed by the motion reconstruction app is within 5 °of the manual measured value, meeting the needs of rehabilitation medicine for motor function evaluation and training guidance; and ③ the system delay is less than 0.5 s, which has good real-time performance and can respond quickly to emergencies, ensuring the safety of patients' out-of-hospital rehabilitation [25].…”

Section: Results Analysismentioning

confidence: 96%

[Retracted] Sensor‐Based Exercise Rehabilitation Robot Training Method

Xie

Zhang

2023

Journal of Sensors

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In order to provide convenience for rehabilitation doctors to formulate rehabilitation plans for patients, this paper proposes a training method for exercise rehabilitation robots based on sensors. In this research, the customized wearable sensor and universal mobile terminal are used as the hardware. Based on the sensor, the motion capture algorithm and motion reconstruction algorithm are developed. The table of experimental results shows that the cost can be saved by using the sensor, and the data can be captured accurately, which can meet the needs of rehabilitation medicine for motor function evaluation and training guidance. The range of motion of the joint and the manual measurement value are within 5°, which can meet the needs of rehabilitation medicine for motor function evaluation and training guidance. The system delay is less than 0.5 s, which has good real-time performance and can respond quickly to emergencies, ensuring the safety of patients’ out-of-hospital rehabilitation. The training method of motion rehabilitation robot based on sensor is helpful for rehabilitation doctors to carry out statistical data of functional evaluation and is of great significance for rehabilitation doctors to make training plans for patients and carry out rehabilitation training.

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Section: Results Analysismentioning

confidence: 96%

[Retracted] Sensor‐Based Exercise Rehabilitation Robot Training Method

Xie

Zhang

2023

Journal of Sensors

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“…Overall, these results show that rewards and punishments enhance performance by increasing ICF and decreasing SICI during movement preparation only. One implication is that targeting M1's GLUTergic and GABAA signalling movement preparationbut not feedback processingprocesses using pharmacological and/or non-invasive brain stimulation interventions can further enhance the neurorehabilitative potential of rewards in clinical settings [15][16][17][18] .…”

Section: Incentives Did Not Alter Icf or Sici During Feedback Processingmentioning

confidence: 99%

“…Namely, changes in CSE reflect the excitability of cortical, subcortical, as well as spinal structures 12 , which can further reflect an increase in glutamatergic (GLUTergic) and/or a decrease in gammaaminobutyric acid (GABA)ergic activity 13,14 . Importantly, rewards and punishments are increasingly recognised as potential enhancers of rehabilitation following physical and brain insults [15][16][17][18] . Therefore, elucidating their mechanisms of action could lead to improved therapeutic interventions 19 .…”

Section: Introductionmentioning

confidence: 99%

The Neurochemical Mechanisms Underlying the Enhancing Effects of Rewards and Punishments on Motor Performance

Hamel

Pearson

Sifi

et al. 2023

Preprint

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Monetary rewards and punishments enhance motor performance and are associated with corticospinal excitability (CSE) increases within the motor cortex (M1) during movement preparation. However, such CSE changes have unclear origins; they could stem from increased glutamatergic (GLUTergic) facilitation and/or decreased type A gamma-aminobutyric acid (GABAA)-mediated inhibition within M1. To investigate this, paired-pulse transcranial magnetic stimulation was used to assess GLUTergic facilitation and GABAA inhibition within M1 whilst participants prepared to execute 4-element finger-press sequences. Behaviourally, rewards and punishments enhanced both reaction and movement times. Neurochemically, regardless of rewards or punishments, a digit-specific increase in GLUTergic facilitation and digit-unspecific decrease in GABAA inhibition occurred during preparation as movement onset approached. In parallel, both rewards and punishments non-specifically increased GLUTergic facilitation, but only rewards non-specifically decreased GABAA inhibition during preparation. This suggests that, to enhance performance, rewards both increase GLUTergic facilitation and decrease GABAA inhibition whilst punishments selectively increase GLUTergic facilitation. A control experiment revealed that such changes were not observed post-movement as participants processed reward and punishment feedback, indicating they were selective to movement preparation. Collectively, these results map the neurochemical changes in M1 by which incentives enhance motor performance.

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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%

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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A reward–punishment feedback control strategy based on energy information for wrist rehabilitation

Cited by 11 publications

References 31 publications

[Retracted] Sensor‐Based Exercise Rehabilitation Robot Training Method

[Retracted] Sensor‐Based Exercise Rehabilitation Robot Training Method

The Neurochemical Mechanisms Underlying the Enhancing Effects of Rewards and Punishments on Motor Performance

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

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