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Wang X, Li L, Wei Y, Zhou P. Clustering index analysis on EMG-Torque relation-based representation of complex neuromuscular changes after spinal cord injury. J Electromyogr Kinesiol 2024; 76:102885. [PMID: 38723398 DOI: 10.1016/j.jelekin.2024.102885] [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: 12/29/2023] [Revised: 03/12/2024] [Accepted: 04/26/2024] [Indexed: 05/23/2024] Open
Abstract
Spinal cord injury (SCI) resulting in complex neuromuscular pathology is not sufficiently well understood. To better quantify neuromuscular changes after SCI, this study uses a clustering index (CI) method for surface electromyography (sEMG) clustering representation to investigate the relation between sEMG and torque in SCI survivors. The sEMG signals were recorded from 13 subjects with SCI and 13 gender-age matched able-bodied subjects during isometric contraction of the biceps brachii muscle at different torque levels using a linear electrode array. Two torque representations, maximum voluntary contraction (MVC%) and absolute torque, were used. CI values were calculated for sEMG. Regression analyses were performed on CI values and torque levels of elbow flexion, revealing a strong linear relationship. The slopes of regressions between SCI survivors and control subjects were compared. The findings indicated that the range of distribution of CI values and slopes was greater in subjects with SCI than in control subjects (p < 0.05). The increase or decrease in slope was also observed at the individual level. This suggests that the CI and its sEMG clustering-torque relation may serve as valuable quantitative indicators for determining neuromuscular lesions after SCI, contributing to the development of effective rehabilitation strategies for improving motor performance.
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Affiliation(s)
- Xiang Wang
- Institute of Medical Research, Northwestern Polytechnical University, Xi'an, China; Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen, China
| | - Le Li
- Institute of Medical Research, Northwestern Polytechnical University, Xi'an, China; Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen, China.
| | - Yongli Wei
- School of Rehabilitation Science and Engineering, University of Health and Rehabilitation Sciences, Qingdao, China
| | - Ping Zhou
- School of Rehabilitation Science and Engineering, University of Health and Rehabilitation Sciences, Qingdao, China
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Scano A, Guanziroli E, Brambilla C, Amendola C, Pirovano I, Gasperini G, Molteni F, Spinelli L, Molinari Tosatti L, Rizzo G, Re R, Mastropietro A. A Narrative Review on Multi-Domain Instrumental Approaches to Evaluate Neuromotor Function in Rehabilitation. Healthcare (Basel) 2023; 11:2282. [PMID: 37628480 PMCID: PMC10454517 DOI: 10.3390/healthcare11162282] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 08/02/2023] [Accepted: 08/10/2023] [Indexed: 08/27/2023] Open
Abstract
In clinical scenarios, the use of biomedical sensors, devices and multi-parameter assessments is fundamental to provide a comprehensive portrait of patients' state, in order to adapt and personalize rehabilitation interventions and support clinical decision-making. However, there is a huge gap between the potential of the multidomain techniques available and the limited practical use that is made in the clinical scenario. This paper reviews the current state-of-the-art and provides insights into future directions of multi-domain instrumental approaches in the clinical assessment of patients involved in neuromotor rehabilitation. We also summarize the main achievements and challenges of using multi-domain approaches in the assessment of rehabilitation for various neurological disorders affecting motor functions. Our results showed that multi-domain approaches combine information and measurements from different tools and biological signals, such as kinematics, electromyography (EMG), electroencephalography (EEG), near-infrared spectroscopy (NIRS), and clinical scales, to provide a comprehensive and objective evaluation of patients' state and recovery. This multi-domain approach permits the progress of research in clinical and rehabilitative practice and the understanding of the pathophysiological changes occurring during and after rehabilitation. We discuss the potential benefits and limitations of multi-domain approaches for clinical decision-making, personalized therapy, and prognosis. We conclude by highlighting the need for more standardized methods, validation studies, and the integration of multi-domain approaches in clinical practice and research.
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Affiliation(s)
- Alessandro Scano
- Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), Via A. Corti 12, 20133 Milan, Italy; (C.B.); (L.M.T.)
| | - Eleonora Guanziroli
- Villa Beretta Rehabilitation Center, Via N. Sauro 17, 23845 Costa Masnaga, Italy; (E.G.); (G.G.); (F.M.)
| | - Cristina Brambilla
- Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), Via A. Corti 12, 20133 Milan, Italy; (C.B.); (L.M.T.)
| | - Caterina Amendola
- Dipartimento di Fisica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy; (C.A.); (R.R.)
| | - Ileana Pirovano
- Institute of Biomedical Technologies (ITB), Italian National Research Council (CNR), Via Fratelli Cervi 93, 20054 Segrate, Italy; (I.P.); (G.R.); (A.M.)
| | - Giulio Gasperini
- Villa Beretta Rehabilitation Center, Via N. Sauro 17, 23845 Costa Masnaga, Italy; (E.G.); (G.G.); (F.M.)
| | - Franco Molteni
- Villa Beretta Rehabilitation Center, Via N. Sauro 17, 23845 Costa Masnaga, Italy; (E.G.); (G.G.); (F.M.)
| | - Lorenzo Spinelli
- Institute for Photonics and Nanotechnology (IFN), Italian National Research Council (CNR), Piazza Leonardo da Vinci 32, 20133 Milan, Italy;
| | - Lorenzo Molinari Tosatti
- Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), Via A. Corti 12, 20133 Milan, Italy; (C.B.); (L.M.T.)
| | - Giovanna Rizzo
- Institute of Biomedical Technologies (ITB), Italian National Research Council (CNR), Via Fratelli Cervi 93, 20054 Segrate, Italy; (I.P.); (G.R.); (A.M.)
| | - Rebecca Re
- Dipartimento di Fisica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy; (C.A.); (R.R.)
- Institute for Photonics and Nanotechnology (IFN), Italian National Research Council (CNR), Piazza Leonardo da Vinci 32, 20133 Milan, Italy;
| | - Alfonso Mastropietro
- Institute of Biomedical Technologies (ITB), Italian National Research Council (CNR), Via Fratelli Cervi 93, 20054 Segrate, Italy; (I.P.); (G.R.); (A.M.)
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Balbinot G, Joner Wiest M, Li G, Pakosh M, Cesar Furlan J, Kalsi-Ryan S, Zariffa J. The use of surface EMG in neurorehabilitation following traumatic spinal cord injury: a scoping review. Clin Neurophysiol 2022; 138:61-73. [DOI: 10.1016/j.clinph.2022.02.028] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 02/06/2022] [Accepted: 02/27/2022] [Indexed: 11/03/2022]
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Yin G, Zhang X, Chen D, Li H, Chen J, Chen C, Lemos S. Processing Surface EMG Signals for Exoskeleton Motion Control. Front Neurorobot 2020; 14:40. [PMID: 32765250 PMCID: PMC7381241 DOI: 10.3389/fnbot.2020.00040] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 05/26/2020] [Indexed: 01/30/2023] Open
Abstract
The surface electromyography (sEMG) signal has been used for volitional control of robotic assistive devices. There are still challenges in improving system performance accuracy and signal processing to remove systematic noise. This study presents procedures and a pilot validation of the EMG-driven speed-control of exoskeleton and integrated treadmill with a goal to provide better interaction between a user and the system. The gait cycle duration (GCD) was extracted from sEMG signals using the autocorrelation algorithm and Bayesian fusion algorithm. GCDs of various walking speeds were then programmed to control the motion speed of exoskeleton robotic system. The performance and efficiency of this sEMG-controlled robotic assistive ambulation system was tested and validated among 6 healthy volunteers. The results demonstrated that the autocorrelation algorithm extracted the GCD from individual muscle contraction. The GCDs of individual muscles had variability between different walking steps under a designated walking speed. Bayesian fusion algorithms processed the GCDs of multiple muscles yielding a final GCD with the least variance. The fused GCD effectively controlled the motion speeds of exoskeleton and treadmill. The higher amplitude of EMG signals with shorter GCD was found during a faster walking speed. The algorithms using fused GCDs and gait stride length yielded trajectory joint motion tracks in a shape of sine curve waveform. The joint angles of the exoskeleton measured by a decoder mounted on the hip turned out to be in sine waveforms. The hip joint motion track of the exoskeleton matched the angles projected by trajectory curve generated by computer algorithms based on the fused GCDs with high agreement. The EMG-driven speed-control provided the human-machine inter-limb coordination mechanisms for an intuitive speed control of the exoskeleton-treadmill system at the user's intents. Potentially the whole system can be used for gait rehabilitation of incomplete spinal cord hemispheric stroke patients as goal-directed and task-oriented training tool.
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Affiliation(s)
- Gui Yin
- Institute of Robotics and Intelligent Systems, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
- Shaanxi Key Laboratory of Intelligent Robots, Xi’an Jiaotong University, Xi’an, China
| | - Xiaodong Zhang
- Institute of Robotics and Intelligent Systems, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
- Shaanxi Key Laboratory of Intelligent Robots, Xi’an Jiaotong University, Xi’an, China
| | - Dawei Chen
- Robotic Rehabilitation Laboratory, Department of Biomedical Engineering, Wayne State University, Detroit, MI, United States
| | - Hanzhe Li
- Institute of Robotics and Intelligent Systems, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, China
- Shaanxi Key Laboratory of Intelligent Robots, Xi’an Jiaotong University, Xi’an, China
| | | | - Chaoyang Chen
- Robotic Rehabilitation Laboratory, Department of Biomedical Engineering, Wayne State University, Detroit, MI, United States
- Department of Rehabilitation Medicine, First Affiliated Hospital, Fujian Medical University, Fuzhou, China
- Department of Orthopaedic Surgery and Sport Medicine, Detroit Medical Center, Detroit, MI, United States
| | - Stephen Lemos
- Department of Orthopaedic Surgery and Sport Medicine, Detroit Medical Center, Detroit, MI, United States
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