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González-Graniel E, Mercado-Gutierrez JA, Martínez-Díaz S, Castro-Liera I, Santillan-Mendez IM, Yanez-Suarez O, Quiñones-Uriostegui I, Rodríguez-Reyes G. Sensing and Control Strategies Used in FES Systems Aimed at Assistance and Rehabilitation of Foot Drop: A Systematic Literature Review. J Pers Med 2024; 14:874. [PMID: 39202064 PMCID: PMC11355777 DOI: 10.3390/jpm14080874] [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: 06/30/2024] [Revised: 08/11/2024] [Accepted: 08/12/2024] [Indexed: 09/03/2024] Open
Abstract
Functional electrical stimulation (FES) is a rehabilitation and assistive technique used for stroke survivors. FES systems mainly consist of sensors, a control algorithm, and a stimulation unit. However, there is a critical need to reassess sensing and control techniques in FES systems to enhance their efficiency. This SLR was carried out following the PRISMA 2020 statement. Four databases (PubMed, Scopus, Web of Science, Wiley Online Library) from 2010 to 2024 were searched using terms related to sensing and control strategies in FES systems. A total of 322 articles were chosen in the first stage, while only 60 of them remained after the final filtering stage. This systematic review mainly focused on sensor techniques and control strategies to deliver FES. The most commonly used sensors reported were inertial measurement units (IMUs), 45% (27); biopotential electrodes, 36.7% (22); vision-based systems, 18.3% (11); and switches, 18.3% (11). The control strategy most reported is closed-loop; however, most of the current commercial FES systems employ open-loop strategies due to their simplicity. Three main factors were identified that should be considered when choosing a sensor for gait-oriented FES systems: wearability, accuracy, and affordability. We believe that the combination of computer vision systems with artificial intelligence-based control algorithms can contribute to the development of minimally invasive and personalized FES systems for the gait rehabilitation of patients with FDS.
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Affiliation(s)
- Estefanía González-Graniel
- División de estudios de Posgrado e Investiagación, TecNM-Instituto Tecnológico de la Paz, La Paz 28080, Mexico; (E.G.-G.); (I.C.-L.); (I.M.S.-M.)
| | - Jorge A. Mercado-Gutierrez
- Instituto Nacional de Rehabilitación Luis Guillermo Ibarra Ibarra, Mexico City 14389, Mexico; (J.A.M.-G.); (I.Q.-U.)
| | - Saúl Martínez-Díaz
- División de estudios de Posgrado e Investiagación, TecNM-Instituto Tecnológico de la Paz, La Paz 28080, Mexico; (E.G.-G.); (I.C.-L.); (I.M.S.-M.)
| | - Iliana Castro-Liera
- División de estudios de Posgrado e Investiagación, TecNM-Instituto Tecnológico de la Paz, La Paz 28080, Mexico; (E.G.-G.); (I.C.-L.); (I.M.S.-M.)
| | - Israel M. Santillan-Mendez
- División de estudios de Posgrado e Investiagación, TecNM-Instituto Tecnológico de la Paz, La Paz 28080, Mexico; (E.G.-G.); (I.C.-L.); (I.M.S.-M.)
| | - Oscar Yanez-Suarez
- Electrical Engineering Department, Universidad Autónoma Metropolitana—Unidad Iztapalapa, Mexico City 09340, Mexico;
| | - Ivett Quiñones-Uriostegui
- Instituto Nacional de Rehabilitación Luis Guillermo Ibarra Ibarra, Mexico City 14389, Mexico; (J.A.M.-G.); (I.Q.-U.)
| | - Gerardo Rodríguez-Reyes
- Instituto Nacional de Rehabilitación Luis Guillermo Ibarra Ibarra, Mexico City 14389, Mexico; (J.A.M.-G.); (I.Q.-U.)
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Li Z, Guiraud D, Andreu D, Gelis A, Fattal C, Hayashibe M. Real-Time Closed-Loop Functional Electrical Stimulation Control of Muscle Activation with Evoked Electromyography Feedback for Spinal Cord Injured Patients. Int J Neural Syst 2017; 28:1750063. [PMID: 29378445 DOI: 10.1142/s0129065717500630] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Functional electrical stimulation (FES) is a neuroprosthetic technique to help restore motor function of spinal cord-injured (SCI) patients. Through delivery of electrical pulses to muscles of motor-impaired subjects, FES is able to artificially induce their muscle contractions. Evoked electromyography (eEMG) is used to record such FES-induced electrical muscle activity and presents a form of [Formula: see text]-wave. In order to monitor electrical muscle activity under stimulation and ensure safe stimulation configurations, closed-loop FES control with eEMG feedback is needed to be developed for SCI patients who lose their voluntary muscle contraction ability. This work proposes a closed-loop FES system for real-time control of muscle activation on the triceps surae and tibialis muscle groups through online modulating pulse width (PW) of electrical stimulus. Subject-specific time-variant muscle responses under FES are explicitly reflected by muscle excitation model, which is described by Hammerstein system with its input and output being, respectively, PW and eEMG. Model predictive control is adopted to compute the PW based on muscle excitation model which can online update its parameters. Four muscle activation patterns are provided as desired control references to validate the proposed closed-loop FES control paradigm. Real-time experimental results on three able-bodied subjects and five SCI patients in clinical environment show promising performances of tracking the aforementioned reference muscle activation patterns based on the proposed closed-loop FES control scheme.
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Affiliation(s)
- Zhan Li
- 1 School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, P. R. China.,2 INRIA, University of Montpellier, Montpellier, France
| | - David Guiraud
- 2 INRIA, University of Montpellier, Montpellier, France
| | - David Andreu
- 2 INRIA, University of Montpellier, Montpellier, France
| | | | - Charles Fattal
- 3 Centre Neurologique PROPARA, Montpellier, France.,4 COS DIVIO, Dijon, France
| | - Mitsuhiro Hayashibe
- 2 INRIA, University of Montpellier, Montpellier, France.,5 Tohoku University, Sendai, Japan
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Hayashibe M. Evoked Electromyographically Controlled Electrical Stimulation. Front Neurosci 2016; 10:335. [PMID: 27471448 PMCID: PMC4943954 DOI: 10.3389/fnins.2016.00335] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2016] [Accepted: 07/01/2016] [Indexed: 11/29/2022] Open
Abstract
Time-variant muscle responses under electrical stimulation (ES) are often problematic for all the applications of neuroprosthetic muscle control. This situation limits the range of ES usage in relevant areas, mainly due to muscle fatigue and also to changes in stimulation electrode contact conditions, especially in transcutaneous ES. Surface electrodes are still the most widely used in noninvasive applications. Electrical field variations caused by changes in the stimulation contact condition markedly affect the resulting total muscle activation levels. Fatigue phenomena under functional electrical stimulation (FES) are also well known source of time-varying characteristics coming from muscle response under ES. Therefore, it is essential to monitor the actual muscle state and assess the expected muscle response by ES so as to improve the current ES system in favor of adaptive muscle-response-aware FES control. To deal with this issue, we have been studying a novel control technique using evoked electromyography (eEMG) signals to compensate for these muscle time-variances under ES for stable neuroprosthetic muscle control. In this perspective article, I overview the background of this topic and highlight important points to be aware of when using ES to induce the desired muscle activation regardless of the time-variance. I also demonstrate how to deal with the common critical problem of ES to move toward robust neuroprosthetic muscle control with the Evoked Electromyographically Controlled Electrical Stimulation paradigm.
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Affiliation(s)
- Mitsuhiro Hayashibe
- Institut National de Recherche en Informatique et en Automatique (INRIA), University of Montpellier Montpellier, France
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Real-time estimation of FES-induced joint torque with evoked EMG : Application to spinal cord injured patients. J Neuroeng Rehabil 2016; 13:60. [PMID: 27334441 PMCID: PMC4918196 DOI: 10.1186/s12984-016-0169-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2016] [Accepted: 06/14/2016] [Indexed: 11/10/2022] Open
Abstract
Background Functional electrical stimulation (FES) is a neuroprosthetic technique for restoring lost motor function of spinal cord injured (SCI) patients and motor-impaired subjects by delivering short electrical pulses to their paralyzed muscles or motor nerves. FES induces action potentials respectively on muscles or nerves so that muscle activity can be characterized by the synchronous recruitment of motor units with its compound electromyography (EMG) signal is called M-wave. The recorded evoked EMG (eEMG) can be employed to predict the resultant joint torque, and modeling of FES-induced joint torque based on eEMG is an essential step to provide necessary prediction of the expected muscle response before achieving accurate joint torque control by FES. Methods Previous works on FES-induced torque tracking issues were mainly based on offline analysis. However, toward personalized clinical rehabilitation applications, real-time FES systems are essentially required considering the subject-specific muscle responses against electrical stimulation. This paper proposes a wireless portable stimulator used for estimating/predicting joint torque based on real time processing of eEMG. Kalman filter and recurrent neural network (RNN) are embedded into the real-time FES system for identification and estimation. Results Prediction results on 3 able-bodied subjects and 3 SCI patients demonstrate promising performances. As estimators, both Kalman filter and RNN approaches show clinically feasible results on estimation/prediction of joint torque with eEMG signals only, moreover RNN requires less computational requirement. Conclusion The proposed real-time FES system establishes a platform for estimating and assessing the mechanical output, the electromyographic recordings and associated models. It will contribute to open a new modality for personalized portable neuroprosthetic control toward consolidated personal healthcare for motor-impaired patients.
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Li Z, Guiraud D, Andreu D, Fattal C, Gelis A, Hayashibe M. A Hybrid Functional Electrical Stimulation for Real-Time Estimation of Joint Torque and Closed-Loop Control of Muscle Activation. Eur J Transl Myol 2016; 26:6064. [PMID: 27990235 PMCID: PMC5128968 DOI: 10.4081/ejtm.2016.6064] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
As a neuroprosthetic technique, functional electrical stimulation (FES) can restore lost motor performance of impaired patients. Through delivering electrical pulses to target muscles, the joint movement can be eventually elicited. This work presents a real-time FES system which is able to deal with two neuroprosthetic missions: one is estimating FES-induced joint torque with evoked electromyograph (eEMG), and the other is artificially controlling muscle activation with such eEMG feedback. The clinical experiment results on spinal cord injured (SCI) patients and healthy subjects show promising performance of the proposed FES system.
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Affiliation(s)
- Zhan Li
- INRIA-LIRMM, University of Montpellier, Montpellier, France; University of Electronic Science and Technology of China, Chengdu, China
| | - David Guiraud
- INRIA-LIRMM, University of Montpellier , Montpellier, France
| | - David Andreu
- INRIA-LIRMM, University of Montpellier , Montpellier, France
| | - Charles Fattal
- PROPARA Rehabilitation Center, Montpellier, France; COS DIVIO, Dijon, France
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Ibitoye MO, Estigoni EH, Hamzaid NA, Wahab AKA, Davis GM. The effectiveness of FES-evoked EMG potentials to assess muscle force and fatigue in individuals with spinal cord injury. SENSORS 2014; 14:12598-622. [PMID: 25025551 PMCID: PMC4168418 DOI: 10.3390/s140712598] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2014] [Revised: 07/09/2014] [Accepted: 07/09/2014] [Indexed: 11/16/2022]
Abstract
The evoked electromyographic signal (eEMG) potential is the standard index used to monitor both electrical changes within the motor unit during muscular activity and the electrical patterns during evoked contraction. However, technical and physiological limitations often preclude the acquisition and analysis of the signal especially during functional electrical stimulation (FES)-evoked contractions. Hence, an accurate quantification of the relationship between the eEMG potential and FES-evoked muscle response remains elusive and continues to attract the attention of researchers due to its potential application in the fields of biomechanics, muscle physiology, and rehabilitation science. We conducted a systematic review to examine the effectiveness of eEMG potentials to assess muscle force and fatigue, particularly as a biofeedback descriptor of FES-evoked contractions in individuals with spinal cord injury. At the outset, 2867 citations were identified and, finally, fifty-nine trials met the inclusion criteria. Four hypotheses were proposed and evaluated to inform this review. The results showed that eEMG is effective at quantifying muscle force and fatigue during isometric contraction, but may not be effective during dynamic contractions including cycling and stepping. Positive correlation of up to r = 0.90 (p < 0.05) between the decline in the peak-to-peak amplitude of the eEMG and the decline in the force output during fatiguing isometric contractions has been reported. In the available prediction models, the performance index of the eEMG signal to estimate the generated muscle force ranged from 3.8% to 34% for 18 s to 70 s ahead of the actual muscle force generation. The strength and inherent limitations of the eEMG signal to assess muscle force and fatigue were evident from our findings with implications in clinical management of spinal cord injury (SCI) population.
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Affiliation(s)
- Morufu Olusola Ibitoye
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia.
| | - Eduardo H Estigoni
- Clinical Exercise and Rehabilitation Unit, The University of Sydney, Sydney, 2006 NSW, Australia.
| | - Nur Azah Hamzaid
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia.
| | - Ahmad Khairi Abdul Wahab
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia.
| | - Glen M Davis
- Clinical Exercise and Rehabilitation Unit, The University of Sydney, Sydney, 2006 NSW, Australia.
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Li Z, Hayashibe M, Fattal C, Guiraud D. Muscle Fatigue Tracking with Evoked EMG via Recurrent Neural Network: Toward Personalized Neuroprosthetics. IEEE COMPUT INTELL M 2014. [DOI: 10.1109/mci.2014.2307224] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Zhang Q, Hayashibe M, Azevedo-Coste C. Evoked electromyography-based closed-loop torque control in functional electrical stimulation. IEEE Trans Biomed Eng 2013; 60:2299-307. [PMID: 23529189 DOI: 10.1109/tbme.2013.2253777] [Citation(s) in RCA: 55] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
This paper proposed a closed-loop torque control strategy of functional electrical stimulation (FES) with the aim of obtaining an accurate, safe, and robust FES system. Generally, FES control systems are faced with the challenge of how to deal with time-variant muscle dynamics due to physiological and biochemical factors (such as fatigue). The degraded muscle force needs to be compensated in order to ensure the accuracy of the motion restored by FES. Another challenge concerns the fact that implantable sensors are unavailable to feedback torque information for FES in humans. As FES-evoked electromyography (EMG) represents the activity of stimulated muscles, and also enables joint torque prediction as presented in our previous studies, here we propose an EMG-feedback predictive controller of FES to control joint torque adaptively. EMG feedback contributes to taking the activated muscle state in the FES torque control system into account. The nature of the predictive controller facilitates prediction of the muscle mechanical response and the system can therefore control joint torque from EMG feedback and also respond to time-variant muscle state changes. The control performance, fatigue compensation and aggressive control suppression capabilities of the proposed controller were evaluated and discussed through experimental and simulation studies.
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Affiliation(s)
- Qin Zhang
- DEMAR Project, INRIA Sophia-Antipolis and LIRMM, CNRS University of Montpellier, Montpellier 34095, France.
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Scott TR, Haugland M. Command and control interfaces for advanced neuroprosthetic applications. Neuromodulation 2012; 4:165-75. [PMID: 22151720 DOI: 10.1046/j.1525-1403.2001.00165.x] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Command and control interfaces permit the intention and situation of the user to influence the operation of the neural prosthesis. The wishes of the user are communicated via command interfaces to the neural prosthesis and the situation of the user by feedback control interfaces. Both these interfaces have been reviewed separately and are discussed in light of the current state of the art and projections for the future. It is apparent that as system functional complexity increases, the need for simpler command interfaces will increase. Such systems will demand more information to function effectively in order not to unreasonably increase user attention overhead. This will increase the need for bioelectric and biomechanical signals in a comprehensible form via elegant feedback control interfaces. Implementing such systems will also increase the computational demand on such neural prostheses.
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Affiliation(s)
- T R Scott
- Quadriplegic Hand Research Unit, Royal North Shore Hospital, Sydney, Australia and Center for Sensory-Motor Interaction, Aalborg University, Aalborg, Denmark
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Hayashibe M, Zhang Q, Guiraud D, Fattal C. Evoked EMG-based torque prediction under muscle fatigue in implanted neural stimulation. J Neural Eng 2011; 8:064001. [PMID: 21975831 DOI: 10.1088/1741-2560/8/6/064001] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
In patients with complete spinal cord injury, fatigue occurs rapidly and there is no proprioceptive feedback regarding the current muscle condition. Therefore, it is essential to monitor the muscle state and assess the expected muscle response to improve the current FES system toward adaptive force/torque control in the presence of muscle fatigue. Our team implanted neural and epimysial electrodes in a complete paraplegic patient in 1999. We carried out a case study, in the specific case of implanted stimulation, in order to verify the corresponding torque prediction based on stimulus evoked EMG (eEMG) when muscle fatigue is occurring during electrical stimulation. Indeed, in implanted stimulation, the relationship between stimulation parameters and output torques is more stable than external stimulation in which the electrode location strongly affects the quality of the recruitment. Thus, the assumption that changes in the stimulation-torque relationship would be mainly due to muscle fatigue can be made reasonably. The eEMG was proved to be correlated to the generated torque during the continuous stimulation while the frequency of eEMG also decreased during fatigue. The median frequency showed a similar variation trend to the mean absolute value of eEMG. Torque prediction during fatigue-inducing tests was performed based on eEMG in model cross-validation where the model was identified using recruitment test data. The torque prediction, apart from the potentiation period, showed acceptable tracking performances that would enable us to perform adaptive closed-loop control through implanted neural stimulation in the future.
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Al-ani T, Cazettes F, Palfi S, Lefaucheur JP. Automatic removal of high-amplitude stimulus artefact from neuronal signal recorded in the subthalamic nucleus. J Neurosci Methods 2011; 198:135-46. [PMID: 21463654 DOI: 10.1016/j.jneumeth.2011.03.022] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2010] [Revised: 03/11/2011] [Accepted: 03/26/2011] [Indexed: 11/26/2022]
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Zhang Q, Hayashibe M, Papaiordanidou M, Fraisse P, Fattal C, Guiraud D. Torque prediction using stimulus evoked EMG and its identification for different muscle fatigue states in SCI subjects. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2010; 2010:3523-3526. [PMID: 21097036 DOI: 10.1109/iembs.2010.5627745] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Muscle fatigue is an unavoidable problem when electrical stimulation is applied to paralyzed muscles. The detection and compensation of muscle fatigue is essential to avoid movement failure and achieve desired trajectory. This work aims to predict ankle plantar-flexion torque using stimulus evoked EMG (eEMG) during different muscle fatigue states. Five spinal cord injured patients were recruited for this study. An intermittent fatigue protocol was delivered to triceps surae muscle to induce muscle fatigue. A hammerstein model was used to capture the muscle contraction dynamics to represent eEMG-torque relationship. The prediction of ankle torque was based on measured eEMG and past measured or past predicted torque. The latter approach makes it possible to use eEMG as a synthetic force sensor when force measurement is not available in daily use. Some previous researches suggested to use eEMG information directly to detect and predict muscle force during fatigue assuming a fixed relationship between eEMG and generated force. However, we found that the prediction became less precise with the increase of muscle fatigue when fixed parameter model was used. Therefore, we carried out the torque prediction with an adaptive parameters using the latest measurement. The prediction of adapted model was improved with 16.7%-50.8% comparing to the fixed model.
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Affiliation(s)
- Qin Zhang
- INRIA Sophia-Antipolis, DEMAR project, LIRMM, France.
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Mobasser F, Eklund JM, Hashtrudi-Zaad K. Estimation of Elbow-Induced Wrist Force With EMG Signals Using Fast Orthogonal Search. IEEE Trans Biomed Eng 2007; 54:683-93. [PMID: 17405375 DOI: 10.1109/tbme.2006.889190] [Citation(s) in RCA: 58] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
In many studies and applications that include direct human involvement-such as human-robot interaction, control of prosthetic arms, and human factor studies-hand force is needed for monitoring or control purposes. The use of inexpensive and easily portable active electromyogram (EMG) electrodes and position sensors would be advantageous in these applications compared to the use of force sensors, which are often very expensive and require bulky frames. Multilayer perceptron artificial neural networks (MLPANN) have been used commonly in the literature to model the relationship between surface EMG signals and muscle or limb forces for different anatomies. This paper investigates the use of fast orthogonal search (FOS), a time-domain method for rapid nonlinear system identification, for elbow-induced wrist force estimation. It further compares the forces estimated using FOS with the forces estimated by MLPANN for the same human anatomy under an ensemble of operational conditions. In this paper, the EMG signal readings from upper arm muscles involved in elbow joint movement and sensed elbow angular position and velocity are utilized as inputs. A single degree-of-freedom robotic experimental testbed has been constructed and used for data collection, training and validation.
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Affiliation(s)
- Farid Mobasser
- Invenium Technologies Corp., Toronto, ON M2N 6K1, Canada.
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Bassingthwaighte JB, Chizeck HJ, Atlas LE, Qian H. Multiscale modeling of cardiac cellular energetics. Ann N Y Acad Sci 2005; 1047:395-424. [PMID: 16093514 PMCID: PMC2864600 DOI: 10.1196/annals.1341.035] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Multiscale modeling is essential to integrating knowledge of human physiology starting from genomics, molecular biology, and the environment through the levels of cells, tissues, and organs all the way to integrated systems behavior. The lowest levels concern biophysical and biochemical events. The higher levels of organization in tissues, organs, and organism are complex, representing the dynamically varying behavior of billions of cells interacting together. Models integrating cellular events into tissue and organ behavior are forced to resort to simplifications to minimize computational complexity, thus reducing the model's ability to respond correctly to dynamic changes in external conditions. Adjustments at protein and gene regulatory levels shortchange the simplified higher-level representations. Our cell primitive is composed of a set of subcellular modules, each defining an intracellular function (action potential, tricarboxylic acid cycle, oxidative phosphorylation, glycolysis, calcium cycling, contraction, etc.), composing what we call the "eternal cell," which assumes that there is neither proteolysis nor protein synthesis. Within the modules are elements describing each particular component (i.e., enzymatic reactions of assorted types, transporters, ionic channels, binding sites, etc.). Cell subregions are stirred tanks, linked by diffusional or transporter-mediated exchange. The modeling uses ordinary differential equations rather than stochastic or partial differential equations. This basic model is regarded as a primitive upon which to build models encompassing gene regulation, signaling, and long-term adaptations in structure and function. During simulation, simpler forms of the model are used, when possible, to reduce computation. However, when this results in error, the more complex and detailed modules and elements need to be employed to improve model realism. The processes of error recognition and of mapping between different levels of model form complexity are challenging but are essential for successful modeling of large-scale systems in reasonable time. Currently there is to this end no established methodology from computational sciences.
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O'Keeffe DT, Lyons GM, Donnelly AE, Byrne CA. Stimulus artifact removal using a software-based two-stage peak detection algorithm. J Neurosci Methods 2001; 109:137-45. [PMID: 11513948 DOI: 10.1016/s0165-0270(01)00407-1] [Citation(s) in RCA: 69] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Abstract
The analysis of stimulus evoked neuromuscular potentials or m-waves is a useful technique for improved feedback control in functional electrical stimulation systems. Usually, however, these signals are contaminated by stimulus artifact. A novel software technique, which uses a two-stage peak detection algorithm, has been developed to remove the unwanted artifact from the recorded signal. The advantage of the technique is that it can be used on all stimulation artifact-contaminated electroneurophysiologic data provided that the artifact and the biopotential are non-overlapping. The technique does not require any estimation of the stimulus artifact shape or duration. With the developed technique, it is not necessary to record a pure artifact signal for template estimation, a process that can increase the complexity of experimentation. The technique also does not require the recording of any external hardware synchronisation pulses. The method avoids the use of analogue or digital filtering techniques, which endeavour to remove certain high frequency components of the artifact signal, but invariably have difficulty, resulting in the removal of frequencies in the same spectrum as the m-wave. With the new technique the signal is sampled at a high frequency to ensure optimum fidelity. Instrumentation saturation effects due to the artifact can be avoided with careful electrode placement. The technique was fully tested with a wide variety of electrical stimulation parameters (frequency and pulse width) applied to the common peroneal nerve to elicit contraction in the tibialis anterior. The program was also developed to allow batch processing of multiple files, using closed loop feedback correction. The two-stage peak detection artifact removal algorithm is demonstrated as an efficient post-processing technique for acquiring artifact free m-waves.
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Affiliation(s)
- D T O'Keeffe
- Biomedical Electronics Laboratory, Department of Electronic and Computer Engineering, University of Limerick, Limerick, Ireland.
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