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Bawa A, Banitsas K, Abbod M. A Movement Classification of Polymyalgia Rheumatica Patients Using Myoelectric Sensors. SENSORS (BASEL, SWITZERLAND) 2024; 24:1500. [PMID: 38475036 DOI: 10.3390/s24051500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 02/21/2024] [Accepted: 02/22/2024] [Indexed: 03/14/2024]
Abstract
Gait disorder is common among people with neurological disease and musculoskeletal disorders. The detection of gait disorders plays an integral role in designing appropriate rehabilitation protocols. This study presents a clinical gait analysis of patients with polymyalgia rheumatica to determine impaired gait patterns using machine learning models. A clinical gait assessment was conducted at KATH hospital between August and September 2022, and the 25 recruited participants comprised 18 patients and 7 control subjects. The demographics of the participants follow: age 56 years ± 7, height 175 cm ± 8, and weight 82 kg ± 10. Electromyography data were collected from four strained hip muscles of patients, which were the rectus femoris, vastus lateralis, biceps femoris, and semitendinosus. Four classification models were used-namely, support vector machine (SVM), rotation forest (RF), k-nearest neighbors (KNN), and decision tree (DT)-to distinguish the gait patterns for the two groups. SVM recorded the highest accuracy of 85% among the classifiers, while KNN had 75%, RF had 80%, and DT had the lowest accuracy of 70%. Furthermore, the SVM classifier had the highest sensitivity of 92%, while RF had 86%, DT had 90%, and KNN had the lowest sensitivity of 84%. The classifiers achieved significant results in discriminating between the impaired gait pattern of patients with polymyalgia rheumatica and control subjects. This information could be useful for clinicians designing therapeutic exercises and may be used for developing a decision support system for diagnostic purposes.
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Affiliation(s)
- Anthony Bawa
- Department of Electronic and Electrical Engineering, Brunel University London, Uxbridge UB8 3PH, UK
| | - Konstantinos Banitsas
- Department of Electronic and Electrical Engineering, Brunel University London, Uxbridge UB8 3PH, UK
| | - Maysam Abbod
- Department of Electronic and Electrical Engineering, Brunel University London, Uxbridge UB8 3PH, UK
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2
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Ershad F, Houston M, Patel S, Contreras L, Koirala B, Lu Y, Rao Z, Liu Y, Dias N, Haces-Garcia A, Zhu W, Zhang Y, Yu C. Customizable, reconfigurable, and anatomically coordinated large-area, high-density electromyography from drawn-on-skin electrode arrays. PNAS NEXUS 2023; 2:pgac291. [PMID: 36712933 PMCID: PMC9837666 DOI: 10.1093/pnasnexus/pgac291] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 12/09/2022] [Indexed: 06/18/2023]
Abstract
Accurate anatomical matching for patient-specific electromyographic (EMG) mapping is crucial yet technically challenging in various medical disciplines. The fixed electrode construction of multielectrode arrays (MEAs) makes it nearly impossible to match an individual's unique muscle anatomy. This mismatch between the MEAs and target muscles leads to missing relevant muscle activity, highly redundant data, complicated electrode placement optimization, and inaccuracies in classification algorithms. Here, we present customizable and reconfigurable drawn-on-skin (DoS) MEAs as the first demonstration of high-density EMG mapping from in situ-fabricated electrodes with tunable configurations adapted to subject-specific muscle anatomy. The DoS MEAs show uniform electrical properties and can map EMG activity with high fidelity under skin deformation-induced motion, which stems from the unique and robust skin-electrode interface. They can be used to localize innervation zones (IZs), detect motor unit propagation, and capture EMG signals with consistent quality during large muscle movements. Reconfiguring the electrode arrangement of DoS MEAs to match and extend the coverage of the forearm flexors enables localization of the muscle activity and prevents missed information such as IZs. In addition, DoS MEAs customized to the specific anatomy of subjects produce highly informative data, leading to accurate finger gesture detection and prosthetic control compared with conventional technology.
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Affiliation(s)
- Faheem Ershad
- Department of Biomedical Engineering, Pennsylvania State University, University Park, PA, 16801, USA
- Department of Biomedical Engineering, University of Houston, Houston, TX, 77204, USA
| | - Michael Houston
- Department of Biomedical Engineering, University of Houston, Houston, TX, 77204, USA
| | - Shubham Patel
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA, 16801, USA
- Department of Mechanical Engineering, University of Houston, Houston, TX, 77204, USA
| | - Luis Contreras
- Department of Biomedical Engineering, University of Houston, Houston, TX, 77204, USA
| | - Bikram Koirala
- Department of Mechanical Engineering, University of Houston, Houston, TX, 77204, USA
- Department of Engineering Technology, University of Houston, Houston, TX, 77204, USA
| | - Yuntao Lu
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA, 16801, USA
- Materials Science and Engineering Program, University of Houston, Houston, TX, 77204, USA
| | - Zhoulyu Rao
- Department of Engineering Science and Mechanics, Pennsylvania State University, University Park, PA, 16801, USA
- Materials Science and Engineering Program, University of Houston, Houston, TX, 77204, USA
| | - Yang Liu
- Department of Biomedical Engineering, University of Houston, Houston, TX, 77204, USA
| | - Nicholas Dias
- Department of Biomedical Engineering, University of Houston, Houston, TX, 77204, USA
| | - Arturo Haces-Garcia
- Department of Engineering Technology, University of Houston, Houston, TX, 77204, USA
- Department of Electrical and Computer Engineering, University of Houston, Houston, TX, 77204, USA
| | - Weihang Zhu
- Department of Mechanical Engineering, University of Houston, Houston, TX, 77204, USA
- Department of Engineering Technology, University of Houston, Houston, TX, 77204, USA
| | - Yingchun Zhang
- Department of Biomedical Engineering, University of Houston, Houston, TX, 77204, USA
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Balbinot G, Li G, Wiest MJ, Pakosh M, Furlan JC, Kalsi-Ryan S, Zariffa J. Properties of the surface electromyogram following traumatic spinal cord injury: a scoping review. J Neuroeng Rehabil 2021; 18:105. [PMID: 34187509 PMCID: PMC8244234 DOI: 10.1186/s12984-021-00888-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 05/27/2021] [Indexed: 12/23/2022] Open
Abstract
Traumatic spinal cord injury (SCI) disrupts spinal and supraspinal pathways, and this process is reflected in changes in surface electromyography (sEMG). sEMG is an informative complement to current clinical testing and can capture the residual motor command in great detail-including in muscles below the level of injury with seemingly absent motor activities. In this comprehensive review, we sought to describe how the sEMG properties are changed after SCI. We conducted a systematic literature search followed by a narrative review focusing on sEMG analysis techniques and signal properties post-SCI. We found that early reports were mostly focused on the qualitative analysis of sEMG patterns and evolved to semi-quantitative scores and a more detailed amplitude-based quantification. Nonetheless, recent studies are still constrained to an amplitude-based analysis of the sEMG, and there are opportunities to more broadly characterize the time- and frequency-domain properties of the signal as well as to take fuller advantage of high-density EMG techniques. We recommend the incorporation of a broader range of signal properties into the neurophysiological assessment post-SCI and the development of a greater understanding of the relation between these sEMG properties and underlying physiology. Enhanced sEMG analysis could contribute to a more complete description of the effects of SCI on upper and lower motor neuron function and their interactions, and also assist in understanding the mechanisms of change following neuromodulation or exercise therapy.
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Affiliation(s)
- Gustavo Balbinot
- KITE-Toronto Rehabilitation Institute, University Health Network, Toronto, ON, M5G 2A2, Canada.
| | - Guijin Li
- KITE-Toronto Rehabilitation Institute, University Health Network, Toronto, ON, M5G 2A2, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, Canada
| | - Matheus Joner Wiest
- KITE-Toronto Rehabilitation Institute, University Health Network, Toronto, ON, M5G 2A2, Canada
| | - Maureen Pakosh
- Library & Information Services, Toronto Rehabilitation Institute, University Health Network, Toronto, Canada
| | - Julio Cesar Furlan
- KITE-Toronto Rehabilitation Institute, University Health Network, Toronto, ON, M5G 2A2, Canada
- Rehabilitation Sciences Institute, University of Toronto, Toronto, Canada
- Department of Medicine, Division of Physical Medicine and Rehabilitation, University of Toronto, Toronto, Canada
- Division of Physical Medicine and Rehabilitation, Toronto Rehabilitation Institute, University Health Network, Toronto, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, Canada
| | - Sukhvinder Kalsi-Ryan
- KITE-Toronto Rehabilitation Institute, University Health Network, Toronto, ON, M5G 2A2, Canada
- Rehabilitation Sciences Institute, University of Toronto, Toronto, Canada
- Department of Physical Therapy, University of Toronto, Toronto, Canada
| | - Jose Zariffa
- KITE-Toronto Rehabilitation Institute, University Health Network, Toronto, ON, M5G 2A2, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, Canada
- Rehabilitation Sciences Institute, University of Toronto, Toronto, Canada
- Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada
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Reduce Surface Electromyography Channels for Gesture Recognition by Multitask Sparse Representation and Minimum Redundancy Maximum Relevance. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:9929684. [PMID: 34136113 PMCID: PMC8177973 DOI: 10.1155/2021/9929684] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 05/09/2021] [Accepted: 05/21/2021] [Indexed: 11/21/2022]
Abstract
Surface electromyography- (sEMG-) based gesture recognition is widely used in rehabilitation training, artificial prosthesis, and human-computer interaction. The purpose of this study is to simplify the sEMG devices by reducing channels while achieving comparably high gesture recognition accuracy. We propose a compound channel selection scheme by combining the variable selection algorithms based on multitask sparse representation (MTSR) and minimum Redundancy Maximum Relevance (mRMR). Specifically, channelwise features are first extracted to compose channel-feature paired variables, for which variable selection procedures by MTSR and mRMR are carried out, respectively. Then, we rank all the channels according to their occurrences in each variable selection procedure and figure out a certain number of informative channels by fusing these rankings of channels. Finally, the gesture classification performance using the selected channels is evaluated by the support vector machine (SVM) classifier. Experiment results validate the effectiveness of this proposed method.
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Chen L, Liu X, Xuan B, Zhang J, Liu Z, Zhang Y. Selection of EMG Sensors Based on Motion Coordinated Analysis. SENSORS 2021; 21:s21041147. [PMID: 33562131 PMCID: PMC7915866 DOI: 10.3390/s21041147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Revised: 02/01/2021] [Accepted: 02/03/2021] [Indexed: 11/16/2022]
Abstract
The intelligent prosthesis driven by electromyography (EMG) signal provides a solution for the movement of the disabled. The proper position of EMG sensors can improve the prosthesis's motion recognition ability. To exert the amputee's action-oriented ability and the prosthesis' control ability, the EMG spatial distribution and internal connection of the prosthetic wearer is analyzed in three kinds of movement conditions: appropriate angle, excessive angle, and angle too small. Firstly, the correlation characteristics between the EMG channels are analyzed by mutual information to construct a muscle functional network. Secondly, the network's features of different movement conditions are analyzed by calculating the characteristic of nodes and evaluating the importance of nodes. Finally, the convergent cross-mapping method is applied to construct a directed network, and the critical muscle groups which can reflect the user's movement intention are determined. Experiment shows that this method can accurately determine the EMG location and simplify the distribution of EMG sensors inside the prosthetic socket. The network characteristics of key muscle groups can distinguish different movements effectively and provide a new strategy for decoding the relationship between limb nerve control and body movement.
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Affiliation(s)
- Lingling Chen
- School of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin 300131, China; (L.C.); (X.L.); (Z.L.); (Y.Z.)
- Engineering Research Center of Intelligent Rehabilitation Device and Detection Technology, Ministry of Education, Tianjin 300131, China
| | - Xiaotian Liu
- School of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin 300131, China; (L.C.); (X.L.); (Z.L.); (Y.Z.)
| | - Bokai Xuan
- School of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin 300131, China; (L.C.); (X.L.); (Z.L.); (Y.Z.)
- Correspondence: ; Tel.: +86-1382-155-7991
| | - Jie Zhang
- School of Chemical Engineering and Advanced Materials, Newcastle University, Newcastle upon Tyne NE1 7RU, UK;
| | - Zuojun Liu
- School of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin 300131, China; (L.C.); (X.L.); (Z.L.); (Y.Z.)
- Engineering Research Center of Intelligent Rehabilitation Device and Detection Technology, Ministry of Education, Tianjin 300131, China
| | - Yan Zhang
- School of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin 300131, China; (L.C.); (X.L.); (Z.L.); (Y.Z.)
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Lw-CNN-Based Myoelectric Signal Recognition and Real-Time Control of Robotic Arm for Upper-Limb Rehabilitation. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2020; 2020:8846021. [PMID: 33456452 PMCID: PMC7785339 DOI: 10.1155/2020/8846021] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 11/22/2020] [Accepted: 12/15/2020] [Indexed: 11/18/2022]
Abstract
Deep-learning models can realize the feature extraction and advanced abstraction of raw myoelectric signals without necessitating manual selection. Raw surface myoelectric signals are processed with a deep model in this study to investigate the feasibility of recognizing upper-limb motion intents and real-time control of auxiliary equipment for upper-limb rehabilitation training. Surface myoelectric signals are collected on six motions of eight subjects' upper limbs. A light-weight convolutional neural network (Lw-CNN) and support vector machine (SVM) model are designed for myoelectric signal pattern recognition. The offline and online performance of the two models are then compared. The average accuracy is (90 ± 5)% for the Lw-CNN and (82.5 ± 3.5)% for the SVM in offline testing of all subjects, which prevails over (84 ± 6)% for the online Lw-CNN and (79 ± 4)% for SVM. The robotic arm control accuracy is (88.5 ± 5.5)%. Significance analysis shows no significant correlation (p = 0.056) among real-time control, offline testing, and online testing. The Lw-CNN model performs well in the recognition of upper-limb motion intents and can realize real-time control of a commercial robotic arm.
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Fang C, He B, Wang Y, Cao J, Gao S. EMG-Centered Multisensory Based Technologies for Pattern Recognition in Rehabilitation: State of the Art and Challenges. BIOSENSORS 2020; 10:E85. [PMID: 32722542 PMCID: PMC7460307 DOI: 10.3390/bios10080085] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Revised: 07/20/2020] [Accepted: 07/22/2020] [Indexed: 01/18/2023]
Abstract
In the field of rehabilitation, the electromyography (EMG) signal plays an important role in interpreting patients' intentions and physical conditions. Nevertheless, utilizing merely the EMG signal suffers from difficulty in recognizing slight body movements, and the detection accuracy is strongly influenced by environmental factors. To address the above issues, multisensory integration-based EMG pattern recognition (PR) techniques have been developed in recent years, and fruitful results have been demonstrated in diverse rehabilitation scenarios, such as achieving high locomotion detection and prosthesis control accuracy. Owing to the importance and rapid development of the EMG centered multisensory fusion technologies in rehabilitation, this paper reviews both theories and applications in this emerging field. The principle of EMG signal generation and the current pattern recognition process are explained in detail, including signal preprocessing, feature extraction, classification algorithms, etc. Mechanisms of collaborations between two important multisensory fusion strategies (kinetic and kinematics) and EMG information are thoroughly explained; corresponding applications are studied, and the pros and cons are discussed. Finally, the main challenges in EMG centered multisensory pattern recognition are discussed, and a future research direction of this area is prospected.
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Affiliation(s)
- Chaoming Fang
- School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100083, China; (C.F.); (Y.W.)
| | - Bowei He
- School of Automation Science and Electrical Engineering, Beihang University, Beijing 100083, China;
| | - Yixuan Wang
- School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100083, China; (C.F.); (Y.W.)
| | - Jin Cao
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02138, USA;
| | - Shuo Gao
- School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100083, China; (C.F.); (Y.W.)
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing 100083, China
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8
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Two-dimensional discrete feature based spatial attention CapsNet For sEMG signal recognition. APPL INTELL 2020. [DOI: 10.1007/s10489-020-01725-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Yao B, Peng Y, Zhang X, Zhang Y, Zhou P, Pu J. The influence of common component on myoelectric pattern recognition. J Int Med Res 2020; 48:300060520903617. [PMID: 32208942 PMCID: PMC7370808 DOI: 10.1177/0300060520903617] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Objective Methods Results Conclusions
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Affiliation(s)
- Bo Yao
- Chinese Academy of Medical Sciences & Peking Union Medical College, Institute of Biomedical Engineering, Tianjin, China.,Department of Physical Medicine and Rehabilitation, University of Texas Health Science Center at Houston, Houston, TX, USA.,TIRR Memorial Hermann Research Center, Houston, TX, USA
| | - Yun Peng
- University of Houston, Houston, TX, USA
| | - Xu Zhang
- University of Science and Technology of China, Hefei, China
| | | | - Ping Zhou
- Department of Physical Medicine and Rehabilitation, University of Texas Health Science Center at Houston, Houston, TX, USA.,TIRR Memorial Hermann Research Center, Houston, TX, USA
| | - Jiangbo Pu
- Department of Physical Medicine and Rehabilitation, University of Texas Health Science Center at Houston, Houston, TX, USA
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McDonald CG, Sullivan JL, Dennis TA, O'Malley MK. A Myoelectric Control Interface for Upper-Limb Robotic Rehabilitation Following Spinal Cord Injury. IEEE Trans Neural Syst Rehabil Eng 2020; 28:978-987. [PMID: 32167899 DOI: 10.1109/tnsre.2020.2979743] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Spinal cord injury (SCI) is a widespread, life-altering injury leading to impairment of sensorimotor function that, while once thought to be permanent, is now being treated with the hope of one day being able to restore function. Surface electromyography (EMG) presents an opportunity to examine and promote human engagement at the neuromuscular level, enabling new protocols for intervention that could be combined with robotic rehabilitation, particularly when robot motion or force sensing may be unusable due to the user's impairment. In this paper, a myoelectric control interface to an exoskeleton for the elbow and wrist was evaluated on a population of ten able-bodied participants and four individuals with cervical-level SCI. The ability of an EMG classifier to discern intended direction of motion in single-degree-of-freedom (DoF) and multi-DoF control modes was assessed for usability in a therapy-like setting. The classifier demonstrated high accuracy for able-bodied participants (averages over 99% for single-DoF and near 90% for multi-DoF), and performance in the SCI group was promising, warranting further study (averages ranging from 85% to 95% for single-DoF, and variable multi-DoF performance averaging around 60%). These results are encouraging for the future use of myoelectric interfaces in robotic rehabilitation for SCI.
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Campbell E, Phinyomark A, Scheme E. Current Trends and Confounding Factors in Myoelectric Control: Limb Position and Contraction Intensity. SENSORS (BASEL, SWITZERLAND) 2020; 20:E1613. [PMID: 32183215 PMCID: PMC7146367 DOI: 10.3390/s20061613] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Revised: 03/08/2020] [Accepted: 03/09/2020] [Indexed: 11/17/2022]
Abstract
This manuscript presents a hybrid study of a comprehensive review and a systematic(research) analysis. Myoelectric control is the cornerstone ofmany assistive technologies used in clinicalpractice, such as prosthetics and orthoses, and human-computer interaction, such as virtual reality control.Although the classification accuracy of such devices exceeds 90% in a controlled laboratory setting,myoelectric devices still face challenges in robustness to variability of daily living conditions.The intrinsic physiological mechanisms limiting practical implementations of myoelectric deviceswere explored: the limb position effect and the contraction intensity effect. The degradationof electromyography (EMG) pattern recognition in the presence of these factors was demonstratedon six datasets, where classification performance was 13% and 20% lower than the controlledsetting for the limb position and contraction intensity effect, respectively. The experimental designsof limb position and contraction intensity literature were surveyed. Current state-of-the-art trainingstrategies and robust algorithms for both effects were compiled and presented. Recommendationsfor future limb position effect studies include: the collection protocol providing exemplars of at least 6positions (four limb positions and three forearm orientations), three-dimensional space experimentaldesigns, transfer learning approaches, and multi-modal sensor configurations. Recommendationsfor future contraction intensity effect studies include: the collection of dynamic contractions, nonlinearcomplexity features, and proportional control.
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Affiliation(s)
- Evan Campbell
- Department of Electrical and Computer Engineering, University of New Brunswick, Canada
- Institute of Biomedical Engineering, University of New Brunswick, Canada
| | - Angkoon Phinyomark
- Institute of Biomedical Engineering, University of New Brunswick, Canada
| | - Erik Scheme
- Department of Electrical and Computer Engineering, University of New Brunswick, Canada
- Institute of Biomedical Engineering, University of New Brunswick, Canada
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Wang Z, Chen G, Li W, Liu H, Wang W. Multichannel optimization for electromyogram signals with complex features in a decomposition-based multi-objective evolution framework with adaptive angle selection. INT J ADV ROBOT SYST 2020. [DOI: 10.1177/1729881420917016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Intelligent manufacturing is a focus of current manufacturing research, and, in combination with the Internet, it enables accurate real-time control of intelligent equipment. Highly accurate real-time prosthesis control has very important applications in therapeutics, intelligent prosthesis, and other fields. However, the applicability of the current electromyogram signal recognition method is not strong because of multiple factors. These include considering one objective (correctness) only and the inability to consider differences of recognition accuracy between actions, to recognize the number of channels, or to recognize computational complexity. In this article, we propose a multi-objective evolutionary algorithm based on a decomposition-based multi-objective differential evolution framework to construct a multi-objective model for electromyogram signals with multiple features and channels. Such channels and features are balanced and selected by using a support vector machine as an electromyogram signal classifier. Results of substantial experiment analyses indicate that the multi-objective electromyogram signal recognition method is superior to the single-objective ant colony algorithm and that the decomposition-based multiobjective evolutionary algorithms with Angle-based updating and global margin ranking is better than the decomposition-based multi-objective evolutionary algorithm and decomposition-based multiobjective evolutionary algorithms with angle-based updating strategy in handling multi-objective models for electromyogram signals.
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Affiliation(s)
- Zheng Wang
- College of Computer Science and technology, Zhejiang University of Technology, Hangzhou, Zhejiang, China
| | - Guoqi Chen
- College of Computer Science and technology, Zhejiang University of Technology, Hangzhou, Zhejiang, China
| | - Weikun Li
- College of Computer Science and technology, Zhejiang University of Technology, Hangzhou, Zhejiang, China
| | - Honghai Liu
- Intelligent Systems and Biomedical Robotics Group, School of Computing, University of Portsmouth, Portsmouth PO1 3HE, UK
| | - Wanliang Wang
- College of Computer Science and technology, Zhejiang University of Technology, Hangzhou, Zhejiang, China
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Lu Z, Stampas A, Francisco GE, Zhou P. Offline and online myoelectric pattern recognition analysis and real-time control of a robotic hand after spinal cord injury. J Neural Eng 2019; 16:036018. [PMID: 30836346 DOI: 10.1088/1741-2552/ab0cf0] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE The objective of this study was to investigate the feasibility of applying myoelectric pattern recognition for controlling a robotic hand in individuals with spinal cord injury (SCI). APPROACH Surface electromyogram (sEMG) signals of six hand motion patterns were recorded from 12 subjects with SCI. Online and offline classification performance of two classifiers (Gaussian Naive Bayes classifier, GNB, and support vector machine, SVM) were investigated. An exoskeleton hand was then controlled in real-time using the classification results. The control accuracy and its correlation with function assessments were investigated. MAIN RESULTS Average offline classification accuracy of all tested SCI subjects was (73.6 ± 14.0)% for GNB and (77.6 ± 11.6)% for SVM, respectively. Average online classification accuracy was significantly lower, (64.3 ± 15.0)% for GNB and (70.2 ± 13.2)% for SVM. Average control accuracy of (81.0 ± 16.3)% was achieved in real-time control of the robotic hand using myoelectric pattern recognition. Correlation between control accuracy and grip/pinch force was observed. SIGNIFICANCE The results show that it is feasible to extract hand motion intent from individuals with SCI and control a robotic hand device using myoelectric pattern recognition. The performance of real-time control can be predicted based on functional assessments.
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Affiliation(s)
- Zhiyuan Lu
- Department of Physical Medicine and Rehabilitation, University of Texas Health Science Center, Houston, TX, United States of America. TIRR Memorial Hermann Research Center, Houston, TX, United States of America
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Automatic detection of oral and pharyngeal phases in swallowing using classification algorithms and multichannel EMG. J Electromyogr Kinesiol 2018; 43:193-200. [DOI: 10.1016/j.jelekin.2018.10.004] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2018] [Revised: 06/29/2018] [Accepted: 10/04/2018] [Indexed: 11/19/2022] Open
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15
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A New Competitive Binary Grey Wolf Optimizer to Solve the Feature Selection Problem in EMG Signals Classification. COMPUTERS 2018. [DOI: 10.3390/computers7040058] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Features extracted from the electromyography (EMG) signal normally consist of irrelevant and redundant features. Conventionally, feature selection is an effective way to evaluate the most informative features, which contributes to performance enhancement and feature reduction. Therefore, this article proposes a new competitive binary grey wolf optimizer (CBGWO) to solve the feature selection problem in EMG signals classification. Initially, short-time Fourier transform (STFT) transforms the EMG signal into time-frequency representation. Ten time-frequency features are extracted from the STFT coefficient. Then, the proposed method is used to evaluate the optimal feature subset from the original feature set. To evaluate the effectiveness of proposed method, CBGWO is compared with binary grey wolf optimization (BGWO1 and BGWO2), binary particle swarm optimization (BPSO), and genetic algorithm (GA). The experimental results show the superiority of CBGWO not only in classification performance, but also feature reduction. In addition, CBGWO has a very low computational cost, which is more suitable for real world application.
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Halilaj E, Rajagopal A, Fiterau M, Hicks JL, Hastie TJ, Delp SL. Machine learning in human movement biomechanics: Best practices, common pitfalls, and new opportunities. J Biomech 2018; 81:1-11. [PMID: 30279002 DOI: 10.1016/j.jbiomech.2018.09.009] [Citation(s) in RCA: 178] [Impact Index Per Article: 29.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2018] [Accepted: 09/08/2018] [Indexed: 12/11/2022]
Abstract
Traditional laboratory experiments, rehabilitation clinics, and wearable sensors offer biomechanists a wealth of data on healthy and pathological movement. To harness the power of these data and make research more efficient, modern machine learning techniques are starting to complement traditional statistical tools. This survey summarizes the current usage of machine learning methods in human movement biomechanics and highlights best practices that will enable critical evaluation of the literature. We carried out a PubMed/Medline database search for original research articles that used machine learning to study movement biomechanics in patients with musculoskeletal and neuromuscular diseases. Most studies that met our inclusion criteria focused on classifying pathological movement, predicting risk of developing a disease, estimating the effect of an intervention, or automatically recognizing activities to facilitate out-of-clinic patient monitoring. We found that research studies build and evaluate models inconsistently, which motivated our discussion of best practices. We provide recommendations for training and evaluating machine learning models and discuss the potential of several underutilized approaches, such as deep learning, to generate new knowledge about human movement. We believe that cross-training biomechanists in data science and a cultural shift toward sharing of data and tools are essential to maximize the impact of biomechanics research.
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Affiliation(s)
- Eni Halilaj
- Department of Mechanical Engineering, Carnegie Mellon University, United States.
| | - Apoorva Rajagopal
- Department of Mechanical Engineering, Stanford University, United States
| | - Madalina Fiterau
- Department of Computer Science, Stanford University, United States
| | - Jennifer L Hicks
- Department of Bioengineering, Stanford University, United States
| | - Trevor J Hastie
- Department of Statistics, Stanford University, United States; Department of Health Research and Policy, Stanford University, United States
| | - Scott L Delp
- Department of Mechanical Engineering, Stanford University, United States; Department of Bioengineering, Stanford University, United States; Department of Orthopaedic Surgery, Stanford University, United States
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Yang G, Deng J, Pang G, Zhang H, Li J, Deng B, Pang Z, Xu J, Jiang M, Liljeberg P, Xie H, Yang H. An IoT-Enabled Stroke Rehabilitation System Based on Smart Wearable Armband and Machine Learning. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE-JTEHM 2018; 6:2100510. [PMID: 29805919 PMCID: PMC5957264 DOI: 10.1109/jtehm.2018.2822681] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Revised: 02/12/2018] [Accepted: 03/16/2018] [Indexed: 11/16/2022]
Abstract
Surface electromyography signal plays an important role in hand function recovery training. In this paper, an IoT-enabled stroke rehabilitation system was introduced which was based on a smart wearable armband (SWA), machine learning (ML) algorithms, and a 3-D printed dexterous robot hand. User comfort is one of the key issues which should be addressed for wearable devices. The SWA was developed by integrating a low-power and tiny-sized IoT sensing device with textile electrodes, which can measure, pre-process, and wirelessly transmit bio-potential signals. By evenly distributing surface electrodes over user’s forearm, drawbacks of classification accuracy poor performance can be mitigated. A new method was put forward to find the optimal feature set. ML algorithms were leveraged to analyze and discriminate features of different hand movements, and their performances were appraised by classification complexity estimating algorithms and principal components analysis. According to the verification results, all nine gestures can be successfully identified with an average accuracy up to 96.20%. In addition, a 3-D printed five-finger robot hand was implemented for hand rehabilitation training purpose. Correspondingly, user’s hand movement intentions were extracted and converted into a series of commands which were used to drive motors assembled inside the dexterous robot hand. As a result, the dexterous robot hand can mimic the user’s gesture in a real-time manner, which shows the proposed system can be used as a training tool to facilitate rehabilitation process for the patients after stroke.
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Affiliation(s)
- Geng Yang
- State Key Laboratory of Fluid Power and Mechatronic Systems, College of Mechanical EngineeringZhejiang UniversityHangzhou310058China
| | - Jia Deng
- State Key Laboratory of Fluid Power and Mechatronic Systems, College of Mechanical EngineeringZhejiang UniversityHangzhou310058China
| | - Gaoyang Pang
- State Key Laboratory of Fluid Power and Mechatronic Systems, College of Mechanical EngineeringZhejiang UniversityHangzhou310058China
| | - Hao Zhang
- State Key Laboratory of Fluid Power and Mechatronic Systems, College of Mechanical EngineeringZhejiang UniversityHangzhou310058China
| | - Jiayi Li
- State Key Laboratory of Fluid Power and Mechatronic Systems, College of Mechanical EngineeringZhejiang UniversityHangzhou310058China
| | - Bin Deng
- State Key Laboratory of Fluid Power and Mechatronic Systems, College of Mechanical EngineeringZhejiang UniversityHangzhou310058China
| | - Zhibo Pang
- ABB Corporate Research721 78VästeråsSweden
| | - Juan Xu
- Department of GeriatricsThe 117th hospital of PLAHangzhou310013China
| | - Mingzhe Jiang
- Department of Future TechnologiesUniversity of Turku20500TurkuFinland
| | - Pasi Liljeberg
- Department of Future TechnologiesUniversity of Turku20500TurkuFinland
| | - Haibo Xie
- State Key Laboratory of Fluid Power and Mechatronic Systems, College of Mechanical EngineeringZhejiang UniversityHangzhou310058China
| | - Huayong Yang
- State Key Laboratory of Fluid Power and Mechatronic Systems, College of Mechanical EngineeringZhejiang UniversityHangzhou310058China
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18
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Zhang Y, Liao Y, Wu X, Chen L, Xiong Q, Gao Z, Zheng X, Li G, Hou W. Non-Uniform Sample Assignment in Training Set Improving Recognition of Hand Gestures Dominated with Similar Muscle Activities. Front Neurorobot 2018; 12:3. [PMID: 29483866 PMCID: PMC5816264 DOI: 10.3389/fnbot.2018.00003] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Accepted: 01/18/2018] [Indexed: 11/22/2022] Open
Abstract
So far, little is known how the sample assignment of surface electromyogram (sEMG) features in training set influences the recognition efficiency of hand gesture, and the aim of this study is to explore the impact of different sample arrangements in training set on the classification of hand gestures dominated with similar muscle activation patterns. Seven right-handed healthy subjects (24.2 ± 1.2 years) were recruited to perform similar grasping tasks (fist, spherical, and cylindrical grasping) and similar pinch tasks (finger, key, and tape pinch). Each task was sustained for 4 s and followed by a 5-s rest interval to avoid fatigue, and the procedure was repeated 60 times for every task. sEMG were recorded from six forearm hand muscles during grasping or pinch tasks, and 4-s sEMG from each channel was segmented for empirical mode decomposition analysis trial by trial. The muscle activity was quantified with zero crossing (ZC) and Wilson amplitude (WAMP) of the first four resulting intrinsic mode function. Thereafter, a sEMG feature vector was constructed with the ZC and WAMP of each channel sEMG, and a classifier combined with support vector machine and genetic algorithm was used for hand gesture recognition. The sample number for each hand gesture was designed to be rearranged according to different sample proportion in training set, and corresponding recognition rate was calculated to evaluate the effect of sample assignment change on gesture classification. Either for similar grasping or pinch tasks, the sample assignment change in training set affected the overall recognition rate of candidate hand gesture. Compare to conventional results with uniformly assigned training samples, the recognition rate of similar pinch gestures was significantly improved when the sample of finger-, key-, and tape-pinch gesture were assigned as 60, 20, and 20%, respectively. Similarly, the recognition rate of similar grasping gestures also rose when the sample proportion of fist, spherical, and cylindrical grasping was 40, 30, and 30%, respectively. Our results suggested that the recognition rate of hand gestures can be regulated by change sample arrangement in training set, which can be potentially used to improve fine-gesture recognition for myoelectric robotic hand exoskeleton control.
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Affiliation(s)
- Yao Zhang
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing, China
| | - Yanjian Liao
- Chongqing Engineering Research Center of Medical Electronics Technology, Chongqing, China
| | - Xiaoying Wu
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing, China
| | - Lin Chen
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing, China
| | - Qiliang Xiong
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing, China
| | - Zhixian Gao
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing, China
| | - Xiaolin Zheng
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing, China.,Chongqing Engineering Research Center of Medical Electronics Technology, Chongqing, China
| | - Guanglin Li
- Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Wensheng Hou
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing, China.,Chongqing Engineering Research Center of Medical Electronics Technology, Chongqing, China
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Nilsson N, Håkansson B, Ortiz-Catalan M. Classification complexity in myoelectric pattern recognition. J Neuroeng Rehabil 2017; 14:68. [PMID: 28693533 PMCID: PMC5504674 DOI: 10.1186/s12984-017-0283-5] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2016] [Accepted: 06/26/2017] [Indexed: 11/10/2022] Open
Abstract
Background Limb prosthetics, exoskeletons, and neurorehabilitation devices can be intuitively controlled using myoelectric pattern recognition (MPR) to decode the subject’s intended movement. In conventional MPR, descriptive electromyography (EMG) features representing the intended movement are fed into a classification algorithm. The separability of the different movements in the feature space significantly affects the classification complexity. Classification complexity estimating algorithms (CCEAs) were studied in this work in order to improve feature selection, predict MPR performance, and inform on faulty data acquisition. Methods CCEAs such as nearest neighbor separability (NNS), purity, repeatability index (RI), and separability index (SI) were evaluated based on their correlation with classification accuracy, as well as on their suitability to produce highly performing EMG feature sets. SI was evaluated using Mahalanobis distance, Bhattacharyya distance, Hellinger distance, Kullback–Leibler divergence, and a modified version of Mahalanobis distance. Three commonly used classifiers in MPR were used to compute classification accuracy (linear discriminant analysis (LDA), multi-layer perceptron (MLP), and support vector machine (SVM)). The algorithms and analytic graphical user interfaces produced in this work are freely available in BioPatRec. Results NNS and SI were found to be highly correlated with classification accuracy (correlations up to 0.98 for both algorithms) and capable of yielding highly descriptive feature sets. Additionally, the experiments revealed how the level of correlation between the inputs of the classifiers influences classification accuracy, and emphasizes the classifiers’ sensitivity to such redundancy. Conclusions This study deepens the understanding of the classification complexity in prediction of motor volition based on myoelectric information. It also provides researchers with tools to analyze myoelectric recordings in order to improve classification performance.
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Affiliation(s)
- Niclas Nilsson
- Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden.
| | - Bo Håkansson
- Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Max Ortiz-Catalan
- Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden.,Integrum AB, Mölndal, Sweden
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McDonald CG, Dennis TA, O'Malley MK. Characterization of surface electromyography patterns of healthy and incomplete spinal cord injury subjects interacting with an upper-extremity exoskeleton. IEEE Int Conf Rehabil Robot 2017; 2017:164-169. [PMID: 28813812 DOI: 10.1109/icorr.2017.8009240] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Rehabilitation exoskeletons may make use of myoelectric control to restore in patients with significant motor impairment following a spinal cord injury (SCI) a sense of volitional control over their limb - a crucial component for recovery of movement. Little investigation has been done into the feasibility of using surface electromyography (sEMG) as an exoskeleton control interface for SCI patients, whose impairment manifests in a highly variable way across the patient population. We have demonstrated that by using only a small subset of features extracted from eight bipolar electrodes recording on the upper arm and forearm muscles, we can achieve high predictive accuracy for the intended direction of motion. Five healthy subjects and two SCI subjects performed voluntary isometric contractions while wearing an exoskeleton for the wrist and elbow joints, generating six distinct single and multi-DoF motions in a total of sixteen possible directions. Using linear discriminant analysis, classification performance was then evaluated using randomly selected holdout test data from the same recording session. Commonalities across subjects, both healthy and SCI, were analyzed at the levels of selected features and the values of commonly selected features. Future work will be to investigate group-specific classification of SCI subjects' intended movements for use in the real-time control of a rehabilitation exoskeleton.
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Lu Z, Chen X, Zhang X, Tong KY, Zhou P. Real-Time Control of an Exoskeleton Hand Robot with Myoelectric Pattern Recognition. Int J Neural Syst 2017; 27:1750009. [DOI: 10.1142/s0129065717500095] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Robot-assisted training provides an effective approach to neurological injury rehabilitation. To meet the challenge of hand rehabilitation after neurological injuries, this study presents an advanced myoelectric pattern recognition scheme for real-time intention-driven control of a hand exoskeleton. The developed scheme detects and recognizes user’s intention of six different hand motions using four channels of surface electromyography (EMG) signals acquired from the forearm and hand muscles, and then drives the exoskeleton to assist the user accomplish the intended motion. The system was tested with eight neurologically intact subjects and two individuals with spinal cord injury (SCI). The overall control accuracy was [Formula: see text] for the neurologically intact subjects and [Formula: see text] for the SCI subjects. The total lag of the system was approximately 250[Formula: see text]ms including data acquisition, transmission and processing. One SCI subject also participated in training sessions in his second and third visits. Both the control accuracy and efficiency tended to improve. These results show great potential for applying the advanced myoelectric pattern recognition control of the wearable robotic hand system toward improving hand function after neurological injuries.
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Affiliation(s)
- Zhiyuan Lu
- Department of Physical Medicine and Rehabilitation, University of Texas Health Science Center at Houston, 7000 Fannin St., Houston, TX, USA
- TIRR Memorial Hermann Research Center, 1333B Moursund St., Houston, TX, USA
| | - Xiang Chen
- Biomedical Engineering Program, University of Science and Technology of China, Hefei, P. R. China
| | - Xu Zhang
- Biomedical Engineering Program, University of Science and Technology of China, Hefei, P. R. China
| | - Kay-Yu Tong
- Division of Biomedical Engineering, Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, P. R. China
| | - Ping Zhou
- Department of Physical Medicine and Rehabilitation, University of Texas Health Science Center at Houston, 7000 Fannin St., Houston, TX, USA
- TIRR Memorial Hermann Research Center, 1333B Moursund St., Houston, TX, USA
- Guangdong Work Injury Rehabilitation Center, 68 Qide Rd., Guangzhou, P. R. China
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22
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Al-Angari HM, Kanitz G, Tarantino S, Cipriani C. Distance and mutual information methods for EMG feature and channel subset selection for classification of hand movements. Biomed Signal Process Control 2016. [DOI: 10.1016/j.bspc.2016.01.011] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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23
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Ertuğrul ÖF, Kaya Y, Tekin R. A novel approach for SEMG signal classification with adaptive local binary patterns. Med Biol Eng Comput 2015; 54:1137-46. [PMID: 26718556 DOI: 10.1007/s11517-015-1443-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2014] [Accepted: 12/14/2015] [Indexed: 10/22/2022]
Abstract
Feature extraction plays a major role in the pattern recognition process, and this paper presents a novel feature extraction approach, adaptive local binary pattern (aLBP). aLBP is built on the local binary pattern (LBP), which is an image processing method, and one-dimensional local binary pattern (1D-LBP). In LBP, each pixel is compared with its neighbors. Similarly, in 1D-LBP, each data in the raw is judged against its neighbors. 1D-LBP extracts feature based on local changes in the signal. Therefore, it has high a potential to be employed in medical purposes. Since, each action or abnormality, which is recorded in SEMG signals, has its own pattern, and via the 1D-LBP these (hidden) patterns may be detected. But, the positions of the neighbors in 1D-LBP are constant depending on the position of the data in the raw. Also, both LBP and 1D-LBP are very sensitive to noise. Therefore, its capacity in detecting hidden patterns is limited. To overcome these drawbacks, aLBP was proposed. In aLBP, the positions of the neighbors and their values can be assigned adaptively via the down-sampling and the smoothing coefficients. Therefore, the potential to detect (hidden) patterns, which may express an illness or an action, is really increased. To validate the proposed feature extraction approach, two different datasets were employed. Achieved accuracies by the proposed approach were higher than obtained results by employed popular feature extraction approaches and the reported results in the literature. Obtained accuracy results were brought out that the proposed method can be employed to investigate SEMG signals. In summary, this work attempts to develop an adaptive feature extraction scheme that can be utilized for extracting features from local changes in different categories of time-varying signals.
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Affiliation(s)
- Ömer Faruk Ertuğrul
- Department of Electrical and Electronic Engineering, Batman University, 72060, Batman, Turkey.
| | - Yılmaz Kaya
- Department of Computer Engineering, Siirt University, 56100, Siirt, Turkey
| | - Ramazan Tekin
- Department of Computer Engineering, Batman University, 72060, Batman, Turkey
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Naik GR, Al-Timemy AH, Nguyen HT. Transradial Amputee Gesture Classification Using an Optimal Number of sEMG Sensors: An Approach Using ICA Clustering. IEEE Trans Neural Syst Rehabil Eng 2015; 24:837-46. [PMID: 26394431 DOI: 10.1109/tnsre.2015.2478138] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Surface electromyography (sEMG)-based pattern recognition studies have been widely used to improve the classification accuracy of upper limb gestures. Information extracted from multiple sensors of the sEMG recording sites can be used as inputs to control powered upper limb prostheses. However, usage of multiple EMG sensors on the prosthetic hand is not practical and makes it difficult for amputees due to electrode shift/movement, and often amputees feel discomfort in wearing sEMG sensor array. Instead, using fewer numbers of sensors would greatly improve the controllability of prosthetic devices and it would add dexterity and flexibility in their operation. In this paper, we propose a novel myoelectric control technique for identification of various gestures using the minimum number of sensors based on independent component analysis (ICA) and Icasso clustering. The proposed method is a model-based approach where a combination of source separation and Icasso clustering was utilized to improve the classification performance of independent finger movements for transradial amputee subjects. Two sEMG sensor combinations were investigated based on the muscle morphology and Icasso clustering and compared to Sequential Forward Selection (SFS) and greedy search algorithm. The performance of the proposed method has been validated with five transradial amputees, which reports a higher classification accuracy ( > 95%). The outcome of this study encourages possible extension of the proposed approach to real time prosthetic applications.
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