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Jiang X, Ma C, Nazarpour K. One-shot random forest model calibration for hand gesture decoding. J Neural Eng 2024; 21:016006. [PMID: 38225863 DOI: 10.1088/1741-2552/ad1786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 12/20/2023] [Indexed: 01/17/2024]
Abstract
Objective.Most existing machine learning models for myoelectric control require a large amount of data to learn user-specific characteristics of the electromyographic (EMG) signals, which is burdensome. Our objective is to develop an approach to enable the calibration of a pre-trained model with minimal data from a new myoelectric user.Approach.We trained a random forest (RF) model with EMG data from 20 people collected during the performance of multiple hand grips. To adapt the decision rules for a new user, first, the branches of the pre-trained decision trees were pruned using the validation data from the new user. Then new decision trees trained merely with data from the new user were appended to the pruned pre-trained model.Results.Real-time myoelectric experiments with 18 participants over two days demonstrated the improved accuracy of the proposed approach when compared to benchmark user-specific RF and the linear discriminant analysis models. Furthermore, the RF model that was calibrated on day one for a new participant yielded significantly higher accuracy on day two, when compared to the benchmark approaches, which reflects the robustness of the proposed approach.Significance.The proposed model calibration procedure is completely source-free, that is, once the base model is pre-trained, no access to the source data from the original 20 people is required. Our work promotes the use of efficient, explainable, and simple models for myoelectric control.
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Affiliation(s)
- Xinyu Jiang
- School of Informatics, The University of Edinburgh, Edinburgh, United Kingdom
| | - Chenfei Ma
- School of Informatics, The University of Edinburgh, Edinburgh, United Kingdom
| | - Kianoush Nazarpour
- School of Informatics, The University of Edinburgh, Edinburgh, United Kingdom
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Huang Y, Yang B, Wong TWL, Ng SSM, Hu X. Personalized robots for long-term telerehabilitation after stroke: a perspective on technological readiness and clinical translation. FRONTIERS IN REHABILITATION SCIENCES 2024; 4:1329927. [PMID: 38259875 PMCID: PMC10800453 DOI: 10.3389/fresc.2023.1329927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 12/21/2023] [Indexed: 01/24/2024]
Abstract
Stroke rehabilitation, which demands consistent, intensive, and adaptable intervention in the long term, faced significant challenges due to the COVID-19 pandemic. During this time, telerehabilitation emerged as a noteworthy complement to traditional rehabilitation services, offering the convenience of at-home care delivery and overcoming geographical and resource limitations. Self-help rehabilitation robots deliver repetitive and intensive physical assistance, thereby alleviating the labor burden. However, robots have rarely demonstrated long-term readiness for poststroke telerehabilitation services. The transition from research trials to general clinical services presents several challenges that may undermine the rehabilitative gains observed in these studies. This perspective discusses the technological readiness of personal use robots in the context of telerehabilitation and identifies the potential challenges for their clinical translation. The goal is to leverage technology to seamlessly integrate it into standard clinical workflows, ultimately enhancing the outcomes of stroke rehabilitation.
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Affiliation(s)
- Yanhuan Huang
- Department of Biomedical Engineering, Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Bibo Yang
- Department of Biomedical Engineering, Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Thomson Wai-Lung Wong
- Department of Rehabilitation Sciences, Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Shamay S. M. Ng
- Department of Rehabilitation Sciences, Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Xiaoling Hu
- Department of Biomedical Engineering, Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
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钟 旭, 张 弼, 李 纪, 张 亮, 元 香, 张 鹏, 赵 新. [Multi-modal synergistic quantitative analysis and rehabilitation assessment of lower limbs for exoskeleton]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2023; 40:953-964. [PMID: 37879925 PMCID: PMC10600416 DOI: 10.7507/1001-5515.202212028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Revised: 08/24/2023] [Indexed: 10/27/2023]
Abstract
In response to the problem that the traditional lower limb rehabilitation scale assessment method is time-consuming and difficult to use in exoskeleton rehabilitation training, this paper proposes a quantitative assessment method for lower limb walking ability based on lower limb exoskeleton robot training with multimodal synergistic information fusion. The method significantly improves the efficiency and reliability of the rehabilitation assessment process by introducing quantitative synergistic indicators fusing electrophysiological and kinematic level information. First, electromyographic and kinematic data of the lower extremity were collected from subjects trained to walk wearing an exoskeleton. Then, based on muscle synergy theory, a synergistic quantification algorithm was used to construct synergistic index features of electromyography and kinematics. Finally, the electrophysiological and kinematic level information was fused to build a modal feature fusion model and output the lower limb motor function score. The experimental results showed that the correlation coefficients of the constructed synergistic features of electromyography and kinematics with the clinical scale were 0.799 and 0.825, respectively. The results of the fused synergistic features in the K-nearest neighbor (KNN) model yielded higher correlation coefficients ( r = 0.921, P < 0.01). This method can modify the rehabilitation training mode of the exoskeleton robot according to the assessment results, which provides a basis for the synchronized assessment-training mode of "human in the loop" and provides a potential method for remote rehabilitation training and assessment of the lower extremity.
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Affiliation(s)
- 旭 钟
- 中国科学院 沈阳自动化研究所 机器人学国家重点实验室(沈阳 110016)State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, P. R. China
- 中国科学院 机器人与智能制造创新研究院(沈阳 110016)Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, P. R. China
- 扬州大学附属医院 医学工程处(江苏扬州 225003)Medical Engineering Department, Affiliated Hospital of Yangzhou University, Yangzhou, Jiangsu 225003, P. R. China
| | - 弼 张
- 中国科学院 沈阳自动化研究所 机器人学国家重点实验室(沈阳 110016)State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, P. R. China
- 中国科学院 机器人与智能制造创新研究院(沈阳 110016)Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, P. R. China
| | - 纪桅 李
- 中国科学院 沈阳自动化研究所 机器人学国家重点实验室(沈阳 110016)State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, P. R. China
- 中国科学院 机器人与智能制造创新研究院(沈阳 110016)Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, P. R. China
| | - 亮 张
- 中国科学院 沈阳自动化研究所 机器人学国家重点实验室(沈阳 110016)State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, P. R. China
| | - 香南 元
- 中国科学院 沈阳自动化研究所 机器人学国家重点实验室(沈阳 110016)State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, P. R. China
| | - 鹏 张
- 中国科学院 沈阳自动化研究所 机器人学国家重点实验室(沈阳 110016)State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, P. R. China
| | - 新刚 赵
- 中国科学院 沈阳自动化研究所 机器人学国家重点实验室(沈阳 110016)State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, P. R. China
- 中国科学院 机器人与智能制造创新研究院(沈阳 110016)Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, P. R. China
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Meng L, Zhang T, Zhao X, Wang D, Xu R, Yang A, Ming D. A quantitative lower limb function assessment method based on fusion of surface EMG and inertial data in stroke patients during cycling task. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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Zhou S, Zhang J, Chen F, Wong TWL, Ng SSM, Li Z, Zhou Y, Zhang S, Guo S, Hu X. Automatic theranostics for long-term neurorehabilitation after stroke. Front Aging Neurosci 2023; 15:1154795. [PMID: 37261267 PMCID: PMC10228725 DOI: 10.3389/fnagi.2023.1154795] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 04/25/2023] [Indexed: 06/02/2023] Open
Affiliation(s)
- Sa Zhou
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Jianing Zhang
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Fei Chen
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Thomson Wai-Lung Wong
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Shamay S. M. Ng
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Zengyong Li
- Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability, National Research Centre for Rehabilitation Technical Aids Beijing, Beijing, China
| | - Yongjin Zhou
- Health Science Center, School of Biomedical Engineering, Shenzhen University, Shenzhen, China
| | - Shaomin Zhang
- Key Laboratory of Biomedical Engineering of Education Ministry, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Department of Biomedical Engineering, School of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Song Guo
- Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
| | - Xiaoling Hu
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
- Shenzhen Research Institute, The Hong Kong Polytechnic University, Shenzhen, China
- University Research Facility in Behavioural and Systems Neuroscience (UBSN), The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
- Research Institute for Smart Ageing (RISA), The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
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Beredjiklian PK, Kachooei AR, Gallant G, Abboudi J, Kwok M, Takei R, Hotchkiss RN. Abnormal Patterns of Biceps and Triceps Co-Contraction Following Elbow Surgery May Result in Elbow Stiffness. THE ARCHIVES OF BONE AND JOINT SURGERY 2023; 11:398-403. [PMID: 37404301 PMCID: PMC10314983 DOI: 10.22038/abjs.2023.64666.3105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Accepted: 04/24/2023] [Indexed: 07/06/2023]
Abstract
Objectives This study examines the pattern of muscular contraction and the intensity of this contraction of the biceps and triceps following elbow surgery. Methods We performed a prospective electromyographic study of 16 patients undergoing 19 surgical procedures on the elbow joint. We measured the resting EMG signal intensity of the biceps and triceps of the operated and the normal sides at 90 degrees. We then calculated the peak EMG signal intensity during passive elbow flexion and extension of the operated side. Results Seventeen of 19 elbows (89%) displayed a co-contraction pattern of the biceps and triceps near the end of flexion and extension during the passive range of motion. The co-contraction pattern was observed near the end of the range of motion in both flexion and extension. In addition to the observed co-contraction patterns, we detected higher contraction intensities for the biceps and triceps muscles in all patients in both flexion and extension for the elbows, which had been treated surgically. Further analysis suggests an inverse correlation between the biceps contraction intensity and the arc of motion measured at the latest follow-up. Conclusion The co-contraction pattern and increased contraction intensity of periarticular muscle groups may result in internal splinting mechanisms, contributing to the development of elbow joint stiffness, which is frequently observed following elbow surgery.
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Affiliation(s)
| | - Amir R. Kachooei
- Rothman Orthopedic Institute, Adventhealth, Orlando, FL, USA
- University of Central Florida, Orlando, FL, USA
| | - Greg Gallant
- Rothman Orthopedic Institute at Thomas Jefferson University, Philadelphia, PA, USA
| | - Jack Abboudi
- Rothman Orthopedic Institute at Thomas Jefferson University, Philadelphia, PA, USA
| | - Moody Kwok
- Rothman Orthopedic Institute at Thomas Jefferson University, Philadelphia, PA, USA
| | - Robert Takei
- Rothman Orthopedic Institute at Thomas Jefferson University, Philadelphia, PA, USA
| | - Robert N. Hotchkiss
- Division of Hand and Upper Extremity Surgery, Hospital for Special Surgery, New York, NY, USA
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Guo Z, Zhou S, Ji K, Zhuang Y, Song J, Nam C, Hu X, Zheng Y. Corticomuscular integrated representation of voluntary motor effort in robotic control for wrist-hand rehabilitation after stroke. J Neural Eng 2022; 19. [PMID: 35193124 DOI: 10.1088/1741-2552/ac5757] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 02/22/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE The central-to-peripheral voluntary motor effort (VME) in physical practice of the paretic limb is a dominant force for driving functional neuroplasticity on motor restoration post-stroke. However, current rehabilitation robots isolated the central and peripheral involvements in the control design, resulting in limited rehabilitation effectiveness. The purpose of this study was to design a corticomuscular coherence (CMC) and electromyography (EMG)-driven (CMC-EMG-driven) system with central-and-peripheral integrated representation of VME for wrist-hand rehabilitation after stroke. APPROACH The CMC-EMG-driven control was developed in a neuromuscular electrical stimulation (NMES)-robot system, i.e., CMC-EMG-driven NMES-robot system, to instruct and assist the wrist-hand extension and flexion in persons after stroke. A pilot single-group trial of 20 training sessions was conducted with the developed system to assess the feasibility for wrist-hand practice on the chronic strokes (n=16). The rehabilitation effectiveness was evaluated through clinical assessments, CMC, and EMG activation levels. MAIN RESULTS The trigger success rate and laterality index (LI) of CMC were significantly increased in wrist-hand extension across training sessions (p<0.05). After the training, significant improvements in the target wrist-hand joints and suppressed compensation from the proximal shoulder-elbow joints were observed through the clinical scores and EMG activation levels (p<0.05). The central-to-peripheral VME distribution across upper extremity (UE) muscles was also significantly improved, as revealed by the CMC values (p<0.05). SIGNIFICANCE Precise wrist-hand rehabilitation was achieved by the developed system, presenting suppressed cortical and muscular compensation from the contralesional hemisphere and the proximal UE, and improved distribution of the central-and-peripheral VME on UE muscles.
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Affiliation(s)
- Ziqi Guo
- The Hong Kong Polytechnic University, Rm S107a, Dept. of BME, PolyU, Hung H, Hung Hom, Kowloon, Kowloon, Nil, HONG KONG
| | - Sa Zhou
- The Hong Kong Polytechnic University, Rm S107a, Dept. of BME, PolyU, Hung H, Hung Hom, Kowloon, Hong Kong, Kowloon, HONG KONG
| | - Kailai Ji
- The Hong Kong Polytechnic University, Dept. of BME, PolyU, Hung H, Hung Hom, Kowloon, Kowloon, Hong Kong, HONG KONG
| | - Yongqi Zhuang
- Biomedical Engineering, Hong Kong Polytechnic University, BME PolyU, Kowloon, HONG KONG
| | - Jie Song
- The Hong Kong Polytechnic University, Rm S107a, Dept. of BME, PolyU, Hung H, Hung Hom, Kowloon, Hong Kong, Kowloon, Nil, HONG KONG
| | - Chingyi Nam
- The Hong Kong Polytechnic University, Rm S107a, Dept. of BME, PolyU, Hung H, Hung Hom, Kowloon, Hong Kong, Kowloon, Nil, HONG KONG
| | - Xiaoling Hu
- Biomedical Engineering, Hong Kong Polytechnic University, Rm ST420, Dept. of BME, PolyU, Hung H, Hung Hom, Kowloon, Hong Kong, Kowloon, HONG KONG
| | - Yongping Zheng
- Biomedical Engineering, The Hong Kong Polytechnic University, BME PolyU, Hong Kong, Nil, CHINA
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