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Ren H, Liu T, Wang J. Design and Analysis of an Upper Limb Rehabilitation Robot Based on Multimodal Control. SENSORS (BASEL, SWITZERLAND) 2023; 23:8801. [PMID: 37960505 PMCID: PMC10647264 DOI: 10.3390/s23218801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 10/24/2023] [Accepted: 10/27/2023] [Indexed: 11/15/2023]
Abstract
To address the rehabilitation needs of upper limb hemiplegic patients in various stages of recovery, streamline the workload of rehabilitation professionals, and provide data visualization, our research team designed a six-degree-of-freedom upper limb exoskeleton rehabilitation robot inspired by the human upper limb's structure. We also developed an eight-channel synchronized signal acquisition system for capturing surface electromyography (sEMG) signals and elbow joint angle data. Utilizing Solidworks, we modeled the robot with a focus on modularity, and conducted structural and kinematic analyses. To predict the elbow joint angles, we employed a back propagation neural network (BPNN). We introduced three training modes: a PID control, bilateral control, and active control, each tailored to different phases of the rehabilitation process. Our experimental results demonstrated a strong linear regression relationship between the predicted reference values and the actual elbow joint angles, with an R-squared value of 94.41% and an average error of four degrees. Furthermore, these results validated the increased stability of our model and addressed issues related to the size and single-mode limitations of upper limb rehabilitation robots. This work lays the theoretical foundation for future model enhancements and further research in the field of rehabilitation.
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Affiliation(s)
- Hang Ren
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200000, China;
| | - Tongyou Liu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 201100, China;
| | - Jinwu Wang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200000, China;
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 201100, China;
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Bu D, Guo S, Guo J, Li H, Wang H. Low-Density sEMG-Based Pattern Recognition of Unrelated Movements Rejection for Wrist Joint Rehabilitation. MICROMACHINES 2023; 14:555. [PMID: 36984962 PMCID: PMC10056026 DOI: 10.3390/mi14030555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 02/16/2023] [Accepted: 02/25/2023] [Indexed: 06/18/2023]
Abstract
sEMG-based pattern recognition commonly assumes a limited number of target categories, and the classifiers often predict each target category depending on probability. In wrist rehabilitation training, the patients may make movements that do not belong to the target category unconsciously. However, most pattern recognition methods can only identify limited patterns and are prone to be disturbed by abnormal movement, especially for wrist joint movements. To address the above the problem, a sEMG-based rejection method for unrelated movements is proposed to identify wrist joint unrelated movements using center loss. In this paper, the sEMG signal collected by the Myo armband is used as the input of the sEMG control method. First, the sEMG signal is processed by sliding signal window and image coding. Then, the CNN with center loss and softmax loss is used to describe the spatial information from the sEMG image to extract discriminative features and target movement recognition. Finally, the deep spatial information is used to train the AE to reject unrelated movements based on the reconstruction loss. The results show that the proposed method can realize the target movements recognition and reject unrelated movements with an F-score of 93.4% and a rejection accuracy of 95% when the recall is 0.9, which reveals the effectiveness of the proposed method.
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Affiliation(s)
- Dongdong Bu
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Shuxiang Guo
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China
- Key Laboratory of Convergence Medical Engineering System and Healthcare Technology, Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Jin Guo
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China
- Key Laboratory of Convergence Medical Engineering System and Healthcare Technology, Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - He Li
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Hanze Wang
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China
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Cisnal A, Gordaliza P, Pérez Turiel J, Fraile JC. Interaction with a Hand Rehabilitation Exoskeleton in EMG-Driven Bilateral Therapy: Influence of Visual Biofeedback on the Users' Performance. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23042048. [PMID: 36850650 PMCID: PMC9964655 DOI: 10.3390/s23042048] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 02/08/2023] [Accepted: 02/09/2023] [Indexed: 05/06/2023]
Abstract
The effectiveness of EMG biofeedback with neurorehabilitation robotic platforms has not been previously addressed. The present work evaluates the influence of an EMG-based visual biofeedback on the user performance when performing EMG-driven bilateral exercises with a robotic hand exoskeleton. Eighteen healthy subjects were asked to perform 1-min randomly generated sequences of hand gestures (rest, open and close) in four different conditions resulting from the combination of using or not (1) EMG-based visual biofeedback and (2) kinesthetic feedback from the exoskeleton movement. The user performance in each test was measured by computing similarity between the target gestures and the recognized user gestures using the L2 distance. Statistically significant differences in the subject performance were found in the type of provided feedback (p-value 0.0124). Pairwise comparisons showed that the L2 distance was statistically significantly lower when only EMG-based visual feedback was present (2.89 ± 0.71) than with the presence of the kinesthetic feedback alone (3.43 ± 0.75, p-value = 0.0412) or the combination of both (3.39 ± 0.70, p-value = 0.0497). Hence, EMG-based visual feedback enables subjects to increase their control over the movement of the robotic platform by assessing their muscle activation in real time. This type of feedback could benefit patients in learning more quickly how to activate robot functions, increasing their motivation towards rehabilitation.
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Affiliation(s)
- Ana Cisnal
- Instituto de las Tecnologías Avanzadas de la Producción (ITAP), School of Industrial Engineering, University of Valladolid, 47011 Valladolid, Spain
- Correspondence:
| | - Paula Gordaliza
- Basque Center for Applied Mathematics (BCAM), 48009 Bilbo, Spain
| | - Javier Pérez Turiel
- Instituto de las Tecnologías Avanzadas de la Producción (ITAP), School of Industrial Engineering, University of Valladolid, 47011 Valladolid, Spain
| | - Juan Carlos Fraile
- Instituto de las Tecnologías Avanzadas de la Producción (ITAP), School of Industrial Engineering, University of Valladolid, 47011 Valladolid, Spain
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Li L, Zhang Y, Huang L, Zhao J, Wang J, Liu T. Robot Assisted Treatment of Hand Functional Rehabilitation Based on Visual Motor Imagination. Front Aging Neurosci 2022; 14:870871. [PMID: 35462690 PMCID: PMC9029075 DOI: 10.3389/fnagi.2022.870871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 03/07/2022] [Indexed: 11/17/2022] Open
Abstract
This pilot study implements a hybrid brain computer interface paradigm based on motor imagery (MI) and steady-state visual evoked potential (SSVEP), in order to explore the neural mechanism and clinical effect of MI-SSVEP intervention paradigm on upper limb functional rehabilitation. In this study, EEG data of 12 healthy participants were collected, and the activation regions of MI-SSVEP paradigm were identified by power spectral density (PSD). By analyzing the inter trial phase consistency (ITPC) of characteristic regions and the causal relationship of brain network, the motor cognitive process including high-level somatosensory joint cortex in the intervention process of MI-SSVEP was studied. Subsequently, this study verified the clinical effect of MI-SSVEP intervention paradigm for 61 stroke patients. The results show that the robot assisted therapy using MI-SSVEP intervention paradigm can more effectively improve the rehabilitation effect of patients.
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Affiliation(s)
- Long Li
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Sciences and Technology, Xi’an Jiaotong University, Xi’an, China
- The Key Laboratory of Neuro-Informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Institute of Health and Rehabilitation Science, School of Life Sciences and Technology, Xi’an Jiaotong University, Xi’an, China
- National Engineering Research Center of Health Care and Medical Devices, Xi’an, China
| | - Yanlong Zhang
- Key Laboratory for Artificial Intelligence and Cognitive Neuroscience of Language, Xi’an International Studies University, Xi’an, China
| | - Liang Huang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Sciences and Technology, Xi’an Jiaotong University, Xi’an, China
- The Key Laboratory of Neuro-Informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Institute of Health and Rehabilitation Science, School of Life Sciences and Technology, Xi’an Jiaotong University, Xi’an, China
- National Engineering Research Center of Health Care and Medical Devices, Xi’an, China
| | - Jie Zhao
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Sciences and Technology, Xi’an Jiaotong University, Xi’an, China
- The Key Laboratory of Neuro-Informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Institute of Health and Rehabilitation Science, School of Life Sciences and Technology, Xi’an Jiaotong University, Xi’an, China
- National Engineering Research Center of Health Care and Medical Devices, Xi’an, China
| | - Jue Wang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Sciences and Technology, Xi’an Jiaotong University, Xi’an, China
- The Key Laboratory of Neuro-Informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Institute of Health and Rehabilitation Science, School of Life Sciences and Technology, Xi’an Jiaotong University, Xi’an, China
- National Engineering Research Center of Health Care and Medical Devices, Xi’an, China
- *Correspondence: Jue Wang,
| | - Tian Liu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Sciences and Technology, Xi’an Jiaotong University, Xi’an, China
- The Key Laboratory of Neuro-Informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Institute of Health and Rehabilitation Science, School of Life Sciences and Technology, Xi’an Jiaotong University, Xi’an, China
- National Engineering Research Center of Health Care and Medical Devices, Xi’an, China
- *Correspondence: Jue Wang,
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Bu D, Guo S, Li H. sEMG-Based Motion Recognition of Upper Limb Rehabilitation Using the Improved Yolo-v4 Algorithm. Life (Basel) 2022; 12:life12010064. [PMID: 35054457 PMCID: PMC8778025 DOI: 10.3390/life12010064] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 12/22/2021] [Accepted: 12/30/2021] [Indexed: 01/02/2023] Open
Abstract
The surface electromyography (sEMG) signal is widely used as a control source of the upper limb exoskeleton rehabilitation robot. However, the traditional way of controlling the exoskeleton robot by the sEMG signal requires one to specially extract and calculate for complex sEMG features. Moreover, due to the huge amount of calculation and individualized difference, the real-time control of the exoskeleton robot cannot be realized. Therefore, this paper proposes a novel method using an improved detection algorithm to recognize limb joint motion and detect joint angle based on sEMG images, aiming to obtain a high-security and fast-processing action recognition strategy. In this paper, MobileNetV2 combined the Ghost module as the feature extraction network to obtain the pretraining model. Then, the target detection network Yolo-V4 was used to estimate the six movement categories of the upper limb joints and to predict the joint movement angles. The experimental results showed that the proposed motion recognition methods were available. Every 100 pictures can be accurately identified in approximately 78 pictures, and the processing speed of every single picture on the PC side was 17.97 ms. For the train data, the mAP@0.5 could reach 82.3%, and mAP@0.5–0.95 could reach 0.42; for the verification data, the average recognition accuracy could reach 80.7%.
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Affiliation(s)
- Dongdong Bu
- Key Laboratory of Convergence Biomedical Engineering System and Healthcare Technology, The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, No. 5, Zhongguancun South Street, Haidian District, Beijing 100081, China; (D.B.); (H.L.)
| | - Shuxiang Guo
- Key Laboratory of Convergence Biomedical Engineering System and Healthcare Technology, The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, No. 5, Zhongguancun South Street, Haidian District, Beijing 100081, China; (D.B.); (H.L.)
- Faculty of Engineering, Kagawa University, 2217-20 Hayashi-cho, Takamatsu 760-8521, Kagawa, Japan
- Correspondence: ; Tel.: +86-010-68918255
| | - He Li
- Key Laboratory of Convergence Biomedical Engineering System and Healthcare Technology, The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, No. 5, Zhongguancun South Street, Haidian District, Beijing 100081, China; (D.B.); (H.L.)
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