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Zhang L, Yang J, Yang Q, An W, Wang D, Cui B. Effectiveness of kneeling training in improving mobility and balance post-stroke. BMC Sports Sci Med Rehabil 2024; 16:163. [PMID: 39095858 PMCID: PMC11295609 DOI: 10.1186/s13102-024-00953-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2024] [Accepted: 07/23/2024] [Indexed: 08/04/2024]
Abstract
BACKGROUND Fall prevention and balance control constitute critical components of rehabilitation for stroke survivors. Kneeling training, characterized by its low center of gravity focus, has been incorporated into rehabilitation regimens to enhance postural control across various pathological conditions. Despite its widespread use, empirical evidence substantiating the efficacy of kneeling training is limited, particularly in the context of mobility and balance improvement for patients who have had a stroke. This study aims to substantiate the safety and effectiveness of kneeling training in individuals recovering from stroke. METHODS A randomized controlled trial comparing kneeling training and conventional rehabilitation training was conducted, involving sixty-seven participants allocated to the Kneeling Training Group (KNT) and the Conventional Rehabilitation Group (CVR). The KNT group underwent 30-minute sessions of kneeling training, while the CVR group received conventional treadmill walking training, both administered six times per week over four weeks. Evaluation encompassed the Fugl-Meyer Assessment for Lower Extremity (FMA-LE), the Berg Balance Scale (BBS), and gait analysis was conducted at baseline, as well as at the 2 and 4-week intervals. RESULTS Our study established the safety of a 4-week kneeling training program. Notably, the KNT group exhibited more pronounced improvements in BBS scores at weeks 2 and 4 compared to the CVR group. However, no significant disparities emerged in FMA-LE and gait analysis between the two groups. Our findings suggest that kneeling training may serve as a viable option for enhancing lower limb balance in survivors who have had a stroke. CONCLUSIONS We conclude that kneeling training, characterized by its safety, simplicity, and no restrictions on location or equipment, represents a valuable therapeutic approach for enhancing walking balance in individuals recovering from stroke. TRIAL REGISTRATION Clinical trials ChiCTR1900028385, December 20, 2019.
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Affiliation(s)
- Li Zhang
- Department of Rehabilitation Medicine, The Second Hospital of Shandong University, 247# Beiyuan street, Jinan, Shandong, China
| | - Jianguo Yang
- Department of Rehabilitation Medicine, The Chengwu People's Hospital, 66# Puji road, Chengwu county, Heze, Shandong, China
| | - Qiu Yang
- Department of Rehabilitation Medicine, The Chengwu People's Hospital, 66# Puji road, Chengwu county, Heze, Shandong, China
| | - Wenhan An
- Department of Rehabilitation Medicine, The Second Hospital of Shandong University, 247# Beiyuan street, Jinan, Shandong, China
| | - Daoqing Wang
- Department of Rehabilitation Medicine, The Second Hospital of Shandong University, 247# Beiyuan street, Jinan, Shandong, China
| | - Baojuan Cui
- Department of Rehabilitation Medicine, The Second Hospital of Shandong University, 247# Beiyuan street, Jinan, Shandong, China.
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Huo Y, Wang X, Zhao W, Hu H, Li L. Effects of EMG-based robot for upper extremity rehabilitation on post-stroke patients: a systematic review and meta-analysis. Front Physiol 2023; 14:1172958. [PMID: 37256069 PMCID: PMC10226272 DOI: 10.3389/fphys.2023.1172958] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 04/20/2023] [Indexed: 06/01/2023] Open
Abstract
Objective: A growing body of research shows the promise and efficacy of EMG-based robot interventions in improving the motor function in stroke survivors. However, it is still controversial whether the effect of EMG-based robot is more effective than conventional therapies. This study focused on the effects of EMG-based robot on upper limb motor control, spasticity and activity limitation in stroke survivors compared with conventional rehabilitation techniques. Methods: We searched electronic databases for relevant randomized controlled trials. Outcomes included Fugl-Meyer assessment scale (FMA), Modified Ashworth Scale (MAS), and activity level. Result: Thirteen studies with 330 subjects were included. The results showed that the outcomes post intervention was significantly improved in the EMG-based robot group. Results from subgroup analyses further revealed that the efficacy of the treatment was better in patients in the subacute stage, those who received a total treatment time of less than 1000 min, and those who received EMG-based robotic therapy combined with electrical stimulation (ES). Conclusion: The effect of EMG-based robot is superior to conventional therapies in terms of improving upper extremity motor control, spasticity and activity limitation. Further research should explore optimal parameters of EMG-based robot therapy and its long-term effects on upper limb function in post-stroke patients. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/; Identifier: 387070.
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Affiliation(s)
- Yunxia Huo
- Institute of Medical Research, Northwestern Polytechnical University, Xi’an, China
- Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen, China
| | - Xiaohan Wang
- Institute of Medical Research, Northwestern Polytechnical University, Xi’an, China
- Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen, China
| | - Weihua Zhao
- Northwestern Polytechnical University Hospital, Xi’an, China
| | - Huijing Hu
- Institute of Medical Research, Northwestern Polytechnical University, Xi’an, China
| | - Le Li
- Institute of Medical Research, Northwestern Polytechnical University, Xi’an, China
- Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen, China
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Gong Z, Lo WLA, Wang R, Li L. Electrical impedance myography combined with quantitative assessment techniques in paretic muscle of stroke survivors: Insights and challenges. Front Aging Neurosci 2023; 15:1130230. [PMID: 37020859 PMCID: PMC10069712 DOI: 10.3389/fnagi.2023.1130230] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 02/27/2023] [Indexed: 03/18/2023] Open
Abstract
Aging is a non-modifiable risk factor for stroke and the global burden of stroke is continuing to increase due to the aging society. Muscle dysfunction, common sequela of stroke, has long been of research interests. Therefore, how to accurately assess muscle function is particularly important. Electrical impedance myography (EIM) has proven to be feasible to assess muscle impairment in patients with stroke in terms of micro structures, such as muscle membrane integrity, extracellular and intracellular fluids. However, EIM alone is not sufficient to assess muscle function comprehensively given the complex contributors to paretic muscle after an insult. This article discusses the potential to combine EIM and other common quantitative methods as ways to improve the assessment of muscle function in stroke survivors. Clinically, these combined assessments provide not only a distinct advantage for greater accuracy of muscle assessment through cross-validation, but also the physiological explanation on muscle dysfunction at the micro level. Different combinations of assessments are discussed with insights for different purposes. The assessments of morphological, mechanical and contractile properties combined with EIM are focused since changes in muscle structures, tone and strength directly reflect the muscle function of stroke survivors. With advances in computational technology, finite element model and machine learning model that incorporate multi-modal evaluation parameters to enable the establishment of predictive or diagnostic model will be the next step forward to assess muscle function for individual with stroke.
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Affiliation(s)
- Ze Gong
- Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen, China
- Institute of Medical Research, Northwestern Polytechnical University, Xi’an, China
| | - Wai Leung Ambrose Lo
- Department of Rehabilitation Medicine, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Ruoli Wang
- KTH MoveAbility Lab, Department of Engineering Mechanics, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Le Li
- Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen, China
- Institute of Medical Research, Northwestern Polytechnical University, Xi’an, China
- *Correspondence: Le Li,
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Li X, Lu Q, Chen P, Gong S, Yu X, He H, Li K. Assistance level quantification-based human-robot interaction space reshaping for rehabilitation training. Front Neurorobot 2023; 17:1161007. [PMID: 37205055 PMCID: PMC10185799 DOI: 10.3389/fnbot.2023.1161007] [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: 02/07/2023] [Accepted: 04/10/2023] [Indexed: 05/21/2023] Open
Abstract
Stroke has become a major disease that seriously threatens human health due to its high incidence and disability rates. Most patients undergo upper limb motor dysfunction after stroke, which significantly impairs the ability of stroke survivors in their activities of daily living (ADL). Robots provide an optional solution for stroke rehabilitation by attending therapy in the hospital and the community, however, the rehabilitation robot still has difficulty in providing needed assistance interactively like human clinicians in conventional therapy. For safe and rehabilitation training, a human-robot interaction space reshaping method was proposed based on the recovery states of patients. According to different recovery states, we designed seven experimental protocols suitable for distinguishing rehabilitation training sessions. To achieve assist-as-needed (AAN) control, a PSO-SVM classification model and an LSTM-KF regression model were introduced to recognize the motor ability of patients with electromyography (EMG) and kinematic data, and a region controller for interaction space shaping was studied. Ten groups of offline and online experiments and corresponding data processing were conducted, and the machine learning and AAN control results were presented, which ensured the effective and the safe upper limb rehabilitation training. To discuss the human-robot interaction in different training stages and sessions, we defined a quantified assistance level index that characterizes the rehabilitation needs by considering the engagement of the patients and had the potential to apply in clinical upper limb rehabilitation training.
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Affiliation(s)
- Xiangyun Li
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Qi Lu
- Sichuan University-Pittsburgh Institute, Sichuan University, Chengdu, China
| | - Peng Chen
- School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China
- *Correspondence: Peng Chen
| | - Shan Gong
- Sichuan University-Pittsburgh Institute, Sichuan University, Chengdu, China
| | - Xi Yu
- Department of Orthopedic Surgery and Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, China
- Department of Rehabilitation Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Hongchen He
- Department of Rehabilitation Medicine, West China Hospital, Sichuan University, Chengdu, China
- School of Rehabilitation Sciences, West China School of Medicine, Sichuan University, Chengdu, China
- Key Laboratory of Rehabilitation Medicine in Sichuan Province, West China Hospital, Sichuan University, Chengdu, China
- Hongchen He
| | - Kang Li
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
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Zheng Z, Wu Z, Zhao R, Ni Y, Jing X, Gao S. A Review of EMG-, FMG-, and EIT-Based Biosensors and Relevant Human–Machine Interactivities and Biomedical Applications. BIOSENSORS 2022; 12:bios12070516. [PMID: 35884319 PMCID: PMC9313012 DOI: 10.3390/bios12070516] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 07/06/2022] [Accepted: 07/09/2022] [Indexed: 11/23/2022]
Abstract
Wearables developed for human body signal detection receive increasing attention in the current decade. Compared to implantable sensors, wearables are more focused on body motion detection, which can support human–machine interaction (HMI) and biomedical applications. In wearables, electromyography (EMG)-, force myography (FMG)-, and electrical impedance tomography (EIT)-based body information monitoring technologies are broadly presented. In the literature, all of them have been adopted for many similar application scenarios, which easily confuses researchers when they start to explore the area. Hence, in this article, we review the three technologies in detail, from basics including working principles, device architectures, interpretation algorithms, application examples, merits and drawbacks, to state-of-the-art works, challenges remaining to be solved and the outlook of the field. We believe the content in this paper could help readers create a whole image of designing and applying the three technologies in relevant scenarios.
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Affiliation(s)
| | | | | | | | | | - Shuo Gao
- Correspondence: ; Tel.: +86-18600737330
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