The realization of robotic neurorehabilitation in clinical: use of computational intelligence and future prospects analysis

Yang, Jiali; Zhao, Zhiqi; Du, Chenzhen; Wang, Wei; Qin, Ping; Qiu, Juhui; Wang, Guixue

doi:10.1080/17434440.2020.1852930

Cited by 10 publications

(7 citation statements)

References 104 publications

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“…The integration of AI and machine learning (ML) in the field of orthopedic prosthesis design has the potential to revolutionize the way prosthetics are developed and used. AI can be used to analyze large amounts of data and make predictions about the behavior of prosthetics in real-world scenarios [ 76 ]. For example, ML algorithms can be used to predict how a prosthetic will behave under different loads, or to optimize the design of prosthetic components for improved biocompatibility, durability, and functionality [ 77 ].…”

Section: Discussionmentioning

confidence: 99%

Overcoming Challenges and Innovations in Orthopedic Prosthesis Design: An Interdisciplinary Perspective

Kulkarni,

Paudel,

Magar

et al. 2023

Biomedical Materials & Devices

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Recent advances in the orthopedic prostheses design have significantly improved the quality of life for individuals with orthopedic disabilities. However, there are still critical challenges that need to be addressed to further enhance the functionality of orthopedic prostheses improving biocompatibility to promote better integration with natural tissues, enhancing durability to withstand the demands of daily use, and improving sensory feedback for better control of movement are the most pressing issues. To address these challenges, promising emerging solutions such as smart prosthetics, 3D printing, regenerative medicine, and artificial intelligence have been developed. These innovative technologies hold the potential to significantly enhance the functionality of orthopedic prostheses. Realizing the full potential of these next-generation orthopedic prostheses requires addressing several critical factors. These include interdisciplinary collaboration between experts in orthopedics, materials science, biology, and engineering, increased investment in research and development, standardization of components to ensure quality and reliability, and improved access to prosthetics. A comprehensive review of these challenges and considerations for future orthopedic prosthesis design is s provided in this paper addressing the further advances to the field. By addressing these issues, we can continue to improve the lives of individuals with orthopedic disabilities and further enhance the field of orthopedic prosthetics.

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Section: Discussionmentioning

confidence: 99%

Overcoming Challenges and Innovations in Orthopedic Prosthesis Design: An Interdisciplinary Perspective

Kulkarni,

Paudel,

Magar

et al. 2023

Biomedical Materials & Devices

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“…To this end, a certain method is required. In recent years, many computational intelligence methods have been investigated extensively [25][26][27][28][29][30]. In this work, FKH is proposed to estimate the unknown degradation model (i.e., the improved Wiener process) coefficients.…”

Section: Estimation Of Coefficients Of the Improved Wiener Process Based On Fkhmentioning

confidence: 99%

Computational Intelligence-Based Prognosis for Hybrid Mechatronic System Using Improved Wiener Process

Wang

et al. 2021

Actuators

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In this article, a fast krill herd algorithm is developed for prognosis of hybrid mechatronic system using the improved Wiener degradation process. First, the diagnostic hybrid bond graph is used to model the hybrid mechatronic system and derive global analytical redundancy relations. Based on the global analytical redundancy relations, the fault signature matrix and mode change signature matrix for fault and mode change isolation can be obtained. Second, in order to determine the true faults from the suspected fault candidates after fault isolation, a fault estimation method based on adaptive square root cubature Kalman filter is proposed when the noise distributions are unknown. Then, the improved Wiener process incorporating nonlinear term is developed to build the degradation model of incipient fault based on the fault estimation results. For prognosis, the fast krill herd algorithm is proposed to estimate unknown degradation model coefficients. After that, the probability density function of remaining useful life is derived using the identified degradation model. Finally, the proposed methods are validated by simulations.

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“…Lower limb motor dysfunction is a common sequela of stroke patients. The elderly is a high-risk group for stroke, and as the population ages, the incidence of stroke increases dramatically [ 1 , 2 ]. The plasticity of the human brain and central nervous system is the basis of rehabilitation medicine.…”

Section: Introductionmentioning

confidence: 99%

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

Tian

Wang

Zheng

et al. 2022

Sensors

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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.

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The realization of robotic neurorehabilitation in clinical: use of computational intelligence and future prospects analysis

Cited by 10 publications

References 104 publications

Overcoming Challenges and Innovations in Orthopedic Prosthesis Design: An Interdisciplinary Perspective

Overcoming Challenges and Innovations in Orthopedic Prosthesis Design: An Interdisciplinary Perspective

Computational Intelligence-Based Prognosis for Hybrid Mechatronic System Using Improved Wiener Process

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

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