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Su D, Hu Z, Wu J, Shang P, Luo Z. Review of adaptive control for stroke lower limb exoskeleton rehabilitation robot based on motion intention recognition. Front Neurorobot 2023; 17:1186175. [PMID: 37465413 PMCID: PMC10350518 DOI: 10.3389/fnbot.2023.1186175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 06/13/2023] [Indexed: 07/20/2023] Open
Abstract
Stroke is a significant cause of disability worldwide, and stroke survivors often experience severe motor impairments. Lower limb rehabilitation exoskeleton robots provide support and balance for stroke survivors and assist them in performing rehabilitation training tasks, which can effectively improve their quality of life during the later stages of stroke recovery. Lower limb rehabilitation exoskeleton robots have become a hot topic in rehabilitation therapy research. This review introduces traditional rehabilitation assessment methods, explores the possibility of lower limb exoskeleton robots combining sensors and electrophysiological signals to assess stroke survivors' rehabilitation objectively, summarizes standard human-robot coupling models of lower limb rehabilitation exoskeleton robots in recent years, and critically introduces adaptive control models based on motion intent recognition for lower limb exoskeleton robots. This provides new design ideas for the future combination of lower limb rehabilitation exoskeleton robots with rehabilitation assessment, motion assistance, rehabilitation treatment, and adaptive control, making the rehabilitation assessment process more objective and addressing the shortage of rehabilitation therapists to some extent. Finally, the article discusses the current limitations of adaptive control of lower limb rehabilitation exoskeleton robots for stroke survivors and proposes new research directions.
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Affiliation(s)
- Dongnan Su
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zhigang Hu
- School of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, China
- Henan Intelligent Rehabilitation Medical Robot Engineering Research Center, Henan University of Science and Technology, Luoyang, China
| | - Jipeng Wu
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Peng Shang
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zhaohui Luo
- State-Owned Changhong Machinery Factory, Guilin, China
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Zhao SN, Cui Y, He Y, He Z, Diao Z, Peng F, Cheng C. Teleoperation control of a wheeled mobile robot based on Brain-machine Interface. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:3638-3660. [PMID: 36899597 DOI: 10.3934/mbe.2023170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
This paper presents a novel teleoperation system using Electroencephalogram (EEG) to control the motion of a wheeled mobile robot (WMR). Different from the other traditional motion controlling method, the WMR is braked with the EEG classification results. Furthermore, the EEG will be induced by using the online BMI (Brain Machine Interface) system, and adopting the non-intrusion induced mode SSVEP (steady state visually evoked potentials). Then, user's motion intention can be recognized by canonical correlation analysis (CCA) classifier, which will be converted into motion commands of the WMR. Finally, the teleoperation technique is utilized to manage the information of the movement scene and adjust the control instructions based on the real-time information. Bezier curve is used to parameterize the path planning of the robot, and the trajectory can be adjusted in real time by EEG recognition results. A motion controller based on error model is proposed to track the planned trajectory by using velocity feedback control, providing excellent track tracking performance. Finally, the feasibility and performance of the proposed teleoperation brain-controlled WMR system are verified using demonstration experiments.
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Affiliation(s)
- Su-Na Zhao
- College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China
| | - Yingxue Cui
- College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China
| | - Yan He
- College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China
| | - Zhendong He
- College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China
| | - Zhihua Diao
- College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China
| | - Fang Peng
- Zhongshan Institute, University of Electronic Science and Technology of China, Zhongshan 528402, China
| | - Chao Cheng
- Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, China
- Weihai Institute for Bionics, Jilin University, Weihai 264402, China
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Zhao H, Yang J, Yang J, Jiang H, Qin Y, Lei Q. Evaluation of the improvement of walking ability in patients with spinal cord injury using lower limb rehabilitation robots based on data science. Transl Neurosci 2023; 14:20220320. [PMID: 37954153 PMCID: PMC10638838 DOI: 10.1515/tnsci-2022-0320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 09/25/2023] [Accepted: 10/07/2023] [Indexed: 11/14/2023] Open
Abstract
Spinal cord injury (SCI) is a serious disabling injury, and the main factors causing SCI in patients include car accidents, falls from heights, as well as heavy blows and falls. These factors can all cause spinal cord compression or even complete rupture. After SCI, problems with the movement, balance, and walking ability of the lower limbs are most common, and SCI can cause abnormalities in patient's movement, sensation, and other aspects. Therefore, in the treatment of SCI, it is necessary to strengthen the rehabilitation training (RT) of patients based on data science to improve their motor ability and play a positive role in the recovery of their walking ability. This article used lower limb rehabilitation robot (LLRR) to improve the walking ability of SCI patients and applied them to SCI rehabilitation. The purpose is to improve the limb movement function of patients by imitating and assisting their limb movements, thereby achieving pain relief and muscle strength enhancement and promoting rehabilitation. The experimental results showed that the functional ambulation category (FAC) scale scores of Group A and Group B were 0.79 and 0.81, respectively, in the first 10 weeks of the experiment. After 10 weeks of the experiment, the FAC scores of Group A and Group B were 2.42 and 4.36, respectively. After the experiment, the FAC score of Group B was much higher than that of Group A, indicating that Group B was more effective in improving patients' walking ability compared to Group A. This also indicated that LLRR rehabilitation training can enhance the walking ability of SCI patients.
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Affiliation(s)
- Hui Zhao
- Department of Rehabilitation Medicine Center, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu610041, Sichuan, PR China
- Key Laboratory of Rehabilitation Medicine in Sichuan Province, Chengdu610041, Sichuan, PR China
| | - Jingyi Yang
- Department of Rehabilitation Medicine Center, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu610041, Sichuan, PR China
- Key Laboratory of Rehabilitation Medicine in Sichuan Province, Chengdu610041, Sichuan, PR China
| | - Jie Yang
- Department of Rehabilitation Medicine Center, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu610041, Sichuan, PR China
- Key Laboratory of Rehabilitation Medicine in Sichuan Province, Chengdu610041, Sichuan, PR China
| | - Hongying Jiang
- Department of Rehabilitation Medicine Center, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu610041, Sichuan, PR China
- Key Laboratory of Rehabilitation Medicine in Sichuan Province, Chengdu610041, Sichuan, PR China
| | - Yecai Qin
- Department of Rehabilitation Medicine Center, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu610041, Sichuan, PR China
- Key Laboratory of Rehabilitation Medicine in Sichuan Province, Chengdu610041, Sichuan, PR China
| | - Qian Lei
- Department of Rehabilitation Medicine Center, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu610041, Sichuan, PR China
- Key Laboratory of Rehabilitation Medicine in Sichuan Province, Chengdu610041, Sichuan, PR China
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Jiang C, Wu C, Xiao X, Lin C. Robust neural dynamics with adaptive coefficient applied to solve the dynamic matrix square root. COMPLEX INTELL SYST 2022. [DOI: 10.1007/s40747-022-00954-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
AbstractZeroing neural networks (ZNN) have shown their state-of-the-art performance on dynamic problems. However, ZNNs are vulnerable to perturbations, which causes reliability concerns in these models owing to the potentially severe consequences. Although it has been reported that some models possess enhanced robustness but cost worse convergence speed. In order to address these problems, a robust neural dynamic with an adaptive coefficient (RNDAC) model is proposed, aided by the novel adaptive activation function and robust evolution formula to boost convergence speed and preserve robustness accuracy. In order to validate and analyze the performance of the RNDAC model, it is applied to solve the dynamic matrix square root (DMSR) problem. Related experiment results show that the RNDAC model reliably solves the DMSR question perturbed by various noises. Using the RNDAC model, we are able to reduce the residual error from 10$$^1$$
1
to 10$$^{-4}$$
-
4
with noise perturbed and reached a satisfying and competitive convergence speed, which converges within 3 s.
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Luna TD, Santamaria V, Ai X, Agrawal SK. Reactive Postural Control During Sit-to-Stand Motion. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3181351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Tatiana D. Luna
- Department of Mechanical Engineering, Columbia University, New York, NY, USA
| | - Victor Santamaria
- Department of Mechanical Engineering, Columbia University, New York, NY, USA
| | - Xupeng Ai
- Department of Mechanical Engineering, Columbia University, New York, NY, USA
| | - Sunil K. Agrawal
- Department of Mechanical Engineering, Department of Rehabilitation and Regenerative Medicine, Columbia University, New York, NY, USA
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