Archive for category Functional Electrical Stimulation (FES)
Check out these 5 easy foot drop exercises for beginners. If you suffer from drop foot, these exercises are a great tool for getting back on your feet. Presented by Dr. Scott Thompson OTD.
PhysioFunction are recognised as international experts in the use of Functional Electrical Stimulation (FES). We ensure our clients receive the most clinically correct rehabilitation technology suited to their needs. Jon Graham, Clinical Director at PhysioFunction talks about Foot Drop and Functional Electrical Stimulation.
[Abstract + References] EEG-controlled functional electrical stimulation rehabilitation for chronic stroke: system design and clinical application
Stroke is one of the most serious diseases that threaten human life and health. It is a major cause of death and disability in the clinic. New strategies for motor rehabilitation after stroke are undergoing exploration. We aimed to develop a novel artificial neural rehabilitation system, which integrates brain-computer interface (BCI) and functional electrical stimulation (FES) technologies, for limb motor function recovery after stroke. We conducted clinical trials (including controlled trials) in 32 patients with chronic stroke. Patients were randomly divided into the BCI-FES group and the neuromuscular electrical stimulation (NMES) group. The changes in outcome measures during intervention were compared between groups, and the trends of ERD values based on EEG were analyzed for BCI-FES group. Results showed that the increase in Fugl Meyer Assessment of the Upper Extremity (FMA-UE) and Kendall Manual Muscle Testing (Kendall MMT) scores of the BCI-FES group was significantly higher than that in the sham group, which indicated the practicality and superiority of the BCI-FES system in clinical practice. The change in the laterality coefficient (LC) values based on μ-ERD (ΔLCm-ERD) had high significant positive correlation with the change in FMA-UE(r = 0.6093, P = 0.012), which provides theoretical basis for exploring novel objective evaluation methods.
- 1.Benjamin EJ, Muntner P, Alonso A, Bittencourt MS, Callaway CW, Carson AP, Chamberlain AM, Chang AR, Cheng S, Das SR, Delling FN, Djousse L, Elkind MSV, Ferguson JF, Fornage M, Jordan LC, Khan SS, Kissela BM, Knutson KL, Kwan TW, Lackland DT, Lewis TT, Lichtman JH, Longenecker CT, Loop MS, Lutsey PL, Martin SS, Matsushita K, Moran AE, Mussolino ME, O’Flaherty M, Pandey A, Perak AM, Rosamond WD, Roth GA, Sampson UKA, Satou GM, Schroeder EB, Shah SH, Spartano NL, Stokes A, Tirschwell DL, Tsao CW, Turakhia MP, VanWagner LB, Wilkins JT, Wong SS, Virani SS; American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics—2019 update: a report from the American Heart Association. Circulation 2019; 139(10): e56–e528PubMed Article Google Scholar
- 2.Yang Q, Tong X, Schieb L, Vaughan A, Gillespie C, Wiltz JL, King SC, Odom E, Merritt R, Hong Y, George MG. Vital signs: recent trends in stroke death rates—United States, 2000–2015. MMWR Morb Mortal Wkly Rep 2017; 66(35): 933–939PubMed PubMed Central Article Google Scholar
- 3.Benjamin EJ, Blaha MJ, Chiuve SE, Cushman M, Das SR, Deo R, de Ferranti SD, Floyd J, Fornage M, Gillespie C, Isasi CR, Jiménez MC, Jordan LC, Judd SE, Lackland D, Lichtman JH, Lisabeth L, Liu S, Longenecker CT, Mackey RH, Matsushita K, Mozaffarian D, Mussolino ME, Nasir K, Neumar RW, Palaniappan L, Pandey DK, Thiagarajan RR, Reeves MJ, Ritchey M, Rodriguez CJ, Roth GA, Rosamond WD, Sasson C, Towfighi A, Tsao CW, Turner MB, Virani SS, Voeks JH, Willey JZ, Wilkins JT, Wu JH, Alger HM, Wong SS, Muntner P; American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics—2017 update: a report from the American Heart Association. Circulation 2017; 135(10): e146–e603PubMed PubMed Central Article Google Scholar
- 4.Nudo RJ, Wise BM, SiFuentes F, Milliken GW. Neural substrates for the effects of rehabilitative training on motor recovery after ischemic infarct. Science 1996; 272(5269): 1791–1794CAS PubMed Article Google Scholar
- 5.Taub E, Uswatte G, Elbert T. New treatments in neurorehabilitation founded on basic research. Nat Rev Neurosci 2002; 3(3): 228–236CAS PubMed Article Google Scholar
- 6.Dimyan MA, Cohen LG. Neuroplasticity in the context of motor rehabilitation after stroke. Nat Rev Neurol 2011; 7(2): 76–85PubMed PubMed Central Article Google Scholar
- 7.Nelson ME, Rejeski WJ, Blair SN, Duncan PW, Judge JO, King AC, Macera CA, Castaneda-Sceppa C. Physical activity and public health in older adults: recommendation from the American College of Sports Medicine and the American Heart Association. Med Sci Sports Exerc 2007; 39(8): 1435–1445PubMed Article Google Scholar
- 8.Takeshima N, Rogers NL, Rogers ME, Islam MM, Koizumi D, Lee S. Functional fitness gain varies in older adults depending on exercise mode. Med Sci Sports Exerc 2007; 39(11): 2036–2043PubMed Article Google Scholar
- 9.Yu W, An C, Kang H. Effects of resistance exercise using Theraband on balance of elderly adults: a randomized controlled trial. J Phys Ther Sci 2013; 25(11): 1471–1473PubMed PubMed Central Article Google Scholar
- 10.Takahashi T, Koizumi D, Islam MM, Watanabe M, Narita M, Takeshima N. Effects of passive exercise machine-based training on day care service user frail elderly. Rigakuryoho Kagaku 2011; 26: 209–213Article Google Scholar
- 11.Takahashi T, Islam MM, Koizumi D, Narita M, Takeshima N. The effects of low intensity exercises performed by community-dwelling chronic stroke patients on passive movement-type machines. Rigakuryoho Kagaku 2012; 27(5): 545–551Article Google Scholar
- 12.Rushton DN. Functional electrical stimulation and rehabilitation—an hypothesis. Med Eng Phys 2003; 25(1): 75–78CAS PubMed Article Google Scholar
- 13.Granat MH, Ferguson AC, Andrews BJ, Delargy M. The role of functional electrical stimulation in the rehabilitation of patients with incomplete spinal cord injury—observed benefits during gait studies. Paraplegia 1993; 31(4): 207–215CAS PubMed Google Scholar
- 14.Wassermann EM. Changes in motor representation with recovery of motor function after stroke: combined electrophysiological and imaging studies. EEG Clin Neurophysiol 1995; 97(4): S26Google Scholar
- 15.Hochberg LR, Serruya MD, Friehs GM, Mukand JA, Saleh M, Caplan AH, Branner A, Chen D, Penn RD, Donoghue JP. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 2006; 442(7099): 164–171CAS PubMed Article Google Scholar
- 16.Silvoni S, Ramos-Murguialday A, Cavinato M, Volpato C, Cisotto G, Turolla A, Piccione F, Birbaumer N. Brain-computer interface in stroke: a review of progress. Clin EEG Neurosci 2011; 42(4): 245–252PubMed Article Google Scholar
- 17.Cervera MA, Soekadar SR, Ushiba J, Millán JDR, Liu M, Birbaumer N, Garipelli G. Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis. Ann Clin Transl Neurol 2018; 5(5): 651–663PubMed PubMed Central Article Google Scholar
- 18.Qiu S, Yi W, Xu J, Qi H, Du J, Wang C, He F, Ming D. Event-related β EEG changes during active, passive movement and functional electrical stimulation of the lower limb. IEEE Trans Neural Syst Rehabil Eng 2016; 24(2): 283–290PubMed Article Google Scholar
- 19.Young BM, Williams J, Prabhakaran V. BCI-FES: could a new rehabilitation device hold fresh promise for stroke patients? Expert Rev Med Devices 2014; 11(6): 537–539CAS PubMed PubMed Central Article Google Scholar
- 20.Stinear CM. Prediction of motor recovery after stroke: advances in biomarkers. Lancet Neurol 2017; 16(10): 826–836PubMed Article Google Scholar
- 21.Looned R, Webb J, Xiao ZG, Menon C. Assisting drinking with an affordable BCI-controlled wearable robot and electrical stimulation: a preliminary investigation. J Neuroeng Rehabil 2014; 11(1): 51PubMed PubMed Central Article Google Scholar
- 22.Biasiucci A, Leeb R, Iturrate I, Perdikis S, Al-Khodairy A, Corbet T, Schnider A, Schmidlin T, Zhang H, Bassolino M, Viceic D, Vuadens P, Guggisberg AG, Millán JDR. Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke. Nat Commun 2018; 9(1): 2421CAS PubMed PubMed Central Article Google Scholar
- 23.Do AH, Wang PT, King CE, Abiri A, Nenadic Z. Brain-computer interface controlled functional electrical stimulation system for ankle movement. J Neuroeng Rehabil 2011; 8(1): 49PubMed PubMed Central Article Google Scholar
- 24.McCrimmon CM, King CE, Wang PT, Cramer SC, Nenadic Z, Do AH. Brain-controlled functional electrical stimulation therapy for gait rehabilitation after stroke: a safety study. J Neuroeng Rehabil 2015; 12(1): 57PubMed PubMed Central Article Google Scholar
- 25.Zhang X, Elnady AM, Randhawa BK, Boyd LA, Menon C. Combining mental training and physical training with goal-oriented protocols in stroke rehabilitation: a feasibility case study. Front Hum Neurosci 2018; 12: 125CAS PubMed PubMed Central Article Google Scholar
- 26.Kim T, Kim S, Lee B. Effects of action observational training plus brain-computer interface-based functional electrical stimulation on paretic arm motor recovery in patient with stroke: a randomized controlled trial. Occup Ther Int 2016; 23(1): 39–47PubMed Article Google Scholar
- 27.Chung E, Park SI, Jang YY, Lee BH. Effects of brain-computer interface-based functional electrical stimulation on balance and gait function in patients with stroke: preliminary results. J Phys Ther Sci 2015; 27(2): 513–516PubMed PubMed Central Article Google Scholar
- 28.Chung E, Kim JH, Park DS, Lee BH. Effects of brain-computer interface-based functional electrical stimulation on brain activation in stroke patients: a pilot randomized controlled trial. J Phys Ther Sci 2015; 27(3): 559–562PubMed PubMed Central Article Google Scholar
- 29.Jang YY, Kim TH, Lee BH. Effects of brain-computer interface-controlled functional electrical stimulation training on shoulder subluxation for patients with stroke: a randomized controlled trial. Occup Ther Int 2016; 23(2): 175–185PubMed Article Google Scholar
- 30.Abduallatif NA, Elsherbini SG, Boshra BS, Yassine IA. Brain-computer interface controlled functional electrical stimulation system for paralyzed arm. 2016 8th Cairo International Biomedical Engineering Conference (CIBEC), Cairo, 2016. 48–51
- 31.Bockbrader M, Annetta N, Friedenberg D, Schwemmer M, Skomrock N, Colachis S 4th, Zhang M, Bouton C, Rezai A, Sharma G, Mysiw WJ. Clinically significant gains in skillful grasp coordination by an individual with tetraplegia using an implanted brain-computer interface with forearm transcutaneous muscle stimulation. Arch Phys Med Rehabil 2019; 100(7): 1201–1217PubMed Article Google Scholar
- 32.Likitlersuang J, Koh R, Gong X, Jovanovic L, Bolivar-Tellería I, Myers M, Zariffa J, Márquez-Chin C. EEG-controlled functional electrical stimulation therapy with automated grasp selection: a proof-of-concept study. Top Spinal Cord Inj Rehabil 2018; 24(3): 265–274PubMed PubMed Central Article Google Scholar
- 33.Osuagwu BC, Wallace L, Fraser M, Vuckovic A. Rehabilitation of hand in subacute tetraplegic patients based on brain computer interface and functional electrical stimulation: a randomised pilot study. J Neural Eng 2016; 13(6): 065002PubMed Article Google Scholar
- 34.Colachis SC 4th, Bockbrader MA, Zhang M, Friedenberg DA, Annetta NV, Schwemmer MA, Skomrock ND, Mysiw WJ, Rezai AR, Bresler HS, Sharma G. Dexterous control of seven functional hand movements using cortically-controlled transcutaneous muscle stimulation in a person with tetraplegia. Front Neurosci 2018; 12: 208PubMed PubMed Central Article Google Scholar
- 35.Pfurtscheller G, Müller GR, Pfurtscheller J, Gerner HJ, Rupp R. ‘Thought’—control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia. Neurosci Lett 2003; 351(1): 33–36CAS PubMed Article Google Scholar
- 36.Ramoser H, Müller-Gerking J, Pfurtscheller G. Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans Rehabil Eng 2000; 8(4): 441–446CAS PubMed Article Google Scholar
- 37.Makeig S. Auditory event-related dynamics of the EEG spectrum and effects of exposure to tones. Electroencephalogr Clin Neurophysiol 1993; 86(4): 283–293CAS PubMed Article Google Scholar
- 38.Lioi G, Fleury M, Butet S, Lécuyer A, Barillot C, Bonan I. Bimodal EEG-fMRI neurofeedback for stroke rehabilitation: a case report. Ann Phys Rehabil Med 2018; 61: e482–e483Article Google Scholar
- 39.Wang T, Mantini D, Gillebert CR. The potential of real-time fMRI neurofeedback for stroke rehabilitation: a systematic review. Cortex 2018; 107: 148–165PubMed PubMed Central Article Google Scholar
- 40.Perronnet L, Lécuyer A, Mano M, Bannier E, Lotte F, Clerc M, Barillot C. Unimodal versus bimodal EEG-fMRI neurofeedback of a motor imagery task. Front Hum Neurosci 2017; 11: 193PubMed PubMed Central Article Google Scholar
- 41.Savelov AA, Shtark MB, Mel’nikov ME, Kozlova LI, Bezmaternykh DD, Verevkin EG, Petrovskii ED, Pokrovskii MA, Tsirkin GM, Rudych PD. Dynamics of fMRI and EEG parameters in a stroke patient assessed during a neurofeedback course focused on Brodmann Area 4 (M1). Bull Exp Biol Med 2019; 166(3): 394–398CAS PubMed Article Google Scholar
- 42.Bönstrup M, Schulz R, Cheng B, Feldheim J, Thomalla G, Hummel F, Gerloff C. P108. The effect of task effort on recovery-related brain activity following motor stroke assessed with fMRI and EEG. Clin Neurophysiol 2015; 126(8): e102Article Google Scholar
- 43.Pfurtscheller G, Neuper C, Andrew C, Edlinger G. Foot and hand area mu rhythms. Int J Psychophysiol 1997; 26(1–3): 121–135CAS PubMed Article Google Scholar
- 44.Hobson HM, Bishop DVM. Mu suppression—a good measure of the human mirror neuron system? Cortex 2016; 82: 290–310PubMed PubMed Central Article Google Scholar
- 45.Perry A, Stein L, Bentin S. Motor and attentional mechanisms involved in social interaction—evidence from mu and alpha EEG suppression. Neuroimage 2011; 58(3): 895–904PubMed Article Google Scholar
- 46.Grefkes C, Fink GR. Connectivity-based approaches in stroke and recovery of function. Lancet Neurol 2014; 13(2): 206–216PubMed Article Google Scholar
- 47.Ray AM, Figueiredo TDC, López-Larraz E, Birbaumer N, Ramos-Murguialday A. Brain oscillatory activity as a biomarker of motor recovery in chronic stroke. Hum Brain Mapp 2020; 41(5): 1296–1308PubMed Article Google Scholar
- 48.Bushnell C, Bettger JP, Cockroft KM, Cramer SC, Edelen MO, Hanley D, Katzan IL, Mattke S, Nilsen DM, Piquado T, Skidmore ER, Wing K, Yenokyan G. Chronic stroke outcome measures for motor function intervention trials: expert panel recommendations. Circ Cardiovasc Qual Outcomes 2015; 8(6 Suppl 3): S163–S169PubMed PubMed Central Article Google Scholar
- 49.Pandian S, Arya KN. Stroke-related motor outcome measures: do they quantify the neurophysiological aspects of upper extremity recovery? J Bodyw Mov Ther 2014; 18(3): 412–423PubMed Article Google Scholar
- 50.Boord P, Barriskill A, Craig A, Nguyen H. Brain-computer interface-FES integration: towards a hands-free neuroprosthesis command system. Neuromodulation 2004; 7(4): 267–276PubMed Article Google Scholar
[GUIDELINE] A Clinical Practice Guideline for the Use of Ankle-Foot Orthoses and Functional Electrical Stimulation Post-Stroke
Level of ambulation following stroke is a long-term predictor of participation and disability. Decreased lower extremity motor control can impact ambulation and overall mobility. The purpose of this clinical practice guideline (CPG) is to provide evidence to guide clinical decision-making for the use of either ankle-foot orthosis (AFO) or functional electrical stimulation (FES) as an intervention to improve body function and structure, activity, and participation as defined by the International Classification of Functioning, Disability and Health (ICF) for individuals with poststroke hemiplegia with decreased lower extremity motor control.
A review of literature published through November 2019 was performed across 7 databases for all studies involving stroke and AFO or FES. Data extracted included time post-stroke, participant characteristics, device types, outcomes assessed, and intervention parameters. Outcomes were examined upon initial application and after training. Recommendations were determined on the basis of the strength of the evidence and the potential benefits, harm, risks, or costs of providing AFO or FES.
One-hundred twenty-two meta-analyses, systematic reviews, randomized controlled trials, and cohort studies were included. Strong evidence exists that AFO and FES can each increase gait speed, mobility, and dynamic balance. Moderate evidence exists that AFO and FES increase quality of life, walking endurance, and muscle activation, and weak evidence exists for improving gait kinematics. AFO or FES should not be used to decrease plantarflexor spasticity. Studies that directly compare AFO and FES do not indicate overall superiority of one over the other. But evidence suggests that AFO may lead to more compensatory effects while FES may lead to more therapeutic effects. Due to the potential for gains at any phase post-stroke, the most appropriate device for an individual may change, and reassessments should be completed to ensure the device is meeting the individual’s needs.
This CPG cannot address the effects of one type of AFO over another for the majority of outcomes, as studies used a variety of AFO types and rarely differentiated effects. The recommendations also do not address the severity of hemiparesis, and most studies included participants with varied baseline ambulation ability.
This CPG suggests that AFO and FES both lead to improvements post-stroke. Future studies should examine timing of provision, device types, intervention duration and delivery, longer term follow-up, responders versus nonresponders, and individuals with greater impairments.
These recommendations are intended as a guide for clinicians to optimize rehabilitation outcomes for people with poststroke hemiplegia who have decreased lower extremity motor control that impacts ambulation and overall mobility.
A Video Abstract is available as supplemental digital content from the authors (available at: http://links.lww.com/JNPT/A335).
TABLE OF CONTENTS
INTRODUCTION AND METHODS
Levels of Evidence and Grades of Recommendations … 117
Summary of Action Statements … 118
Introduction … 119
Methods … 121
Action Statements and Research Recommendations … 128
Action Statement 1: Quality of Life … 128
Action Statement 2: Gait Speed … 129
Action Statement 3: Other Mobility … 135
Action Statement 4: Dynamic Balance … 139
Action Statement 5: Walking Endurance … 143
Action Statement 6: Plantarflexor Spasticity … 147
Action Statement 7: Muscle Activation … 149
Action Statement 8: Gait Kinematics … 152
Overall CPG Clinical Recommendations … 156
Summary of Research Recommendations … 157
Limitations … 158
Guideline Implementation Recommendations … 159
[Abstract] A Robotic System with EMG-Triggered Functional Eletrical Stimulation for Restoring Arm Functions in Stroke Survivors
Robotic systems combined with Functional Electrical Stimulation (FES) showed promising results on upper-limb motor recovery after stroke, but adequately-sized randomized controlled trials (RCTs) are still missing.
To evaluate whether arm training supported by RETRAINER, a passive exoskeleton integrated with electromyograph-triggered functional electrical stimulation, is superior to advanced conventional therapy (ACT) of equal intensity in the recovery of arm functions, dexterity, strength, activities of daily living, and quality of life after stroke.
A single-blind RCT recruiting 72 patients was conducted. Patients, randomly allocated to 2 groups, were trained for 9 weeks, 3 times per week: the experimental group performed task-oriented exercises assisted by RETRAINER for 30 minutes plus ACT (60 minutes), whereas the control group performed only ACT (90 minutes). Patients were assessed before, soon after, and 1 month after the end of the intervention. Outcome measures were as follows: Action Research Arm Test (ARAT), Motricity Index, Motor Activity Log, Box and Blocks Test (BBT), Stroke Specific Quality of Life Scale (SSQoL), and Muscle Research Council.
All outcomes but SSQoL significantly improved over time in both groups (P < .001); a significant interaction effect in favor of the experimental group was found for ARAT and BBT. ARAT showed a between-group change of 11.5 points (P = .010) at the end of the intervention, which increased to 13.6 points 1 month after. Patients considered RETRAINER moderately usable (System Usability Score of 61.5 ± 22.8).
Hybrid robotic systems, allowing to perform personalized, intensive, and task-oriented training, with an enriched sensory feedback, was superior to ACT in improving arm functions and dexterity after stroke.
In this video we show how to model Electrical Stimulation (ES) for biomedical engineering applications using Electric Currents Interface. The Electrical Stimulation simply means: applying electrical pulses through human skin to the nerves inside muscles to activate the muscles. Electrical Stimulation is commonly used for the therapy and recovery from impairment due to a trauma or disease. The electrodes attached to skin are the interface where the electric pulses are applied to human body. First, we explain the concepts of Electrical Stimulation, the modeling assumptions, and expected outputs. Then, we perform a stationary study to analyze the steady-state condition in which we use a fixed current as the stimulation pulse. We consider both bipolar and monopolar configurations for the electrodes as well as anisotropic properties of muscle tissues. We investigate the effectiveness of the stimulation through a parameter known as Activating Function. We also compare our results with the results of a research article to verify our model and simulation. This video is very useful to understand the concepts of Electrical Stimulation which can further be used for the development of functional electrical stimulation systems and neural engineering studies
Lucinda Baker, PhD, PT, is a retired associate professor of biokinesiology and physical therapy at the University of Southern California.Her research has focused on electrical stimulation for wound healing for patients with spinal cord injury and diabetes, as well as rehabilitation of sensory and motor deficits for patients with stroke and traumatic brain injury.
Her talk summarizes efficacy neuromuscular electric simulation, the convenience of wearable technology, and the effectiveness of NMES for spasticity and strength.
Dr. Baker is the author of many scientific papers and co-author of the leading book on the subject NeuroMuscular Electrical Stimulation – A Practical Guide.
[Abstract + References] Iterative Adjustment of Stimulation Timing and Intensity During FES-Assisted Treadmill Walking for Patients After Stroke
Functional electric stimulation (FES) is a common intervention to correct foot drop for patients after stroke. Due to the disturbances from internal time-varying muscle characteristics under electrical stimulation and external environmental uncertainties, most of the existing FES system used pre-set stimulation parameters and cannot achieve good gait performances during FES-assisted walking. Therefore, an adaptive FES control system, which used the iterative learning control to adjust the stimulation intensity based on kinematic data and a linear model to modulate the stimulation timing based on walking speed during FES-assisted treadmill walking, was designed and tested on ten patients with foot drop after stroke. In order to examine its orthotic effects, the kinematic data of the patients using the proposed control strategy were collected and compared with the data of the same patients walking using other three FES control strategies, including (1) constant pre-set stimulation intensity and timing, (2) constant pre-set stimulation intensity with speed-adaptive stimulation timing and (3) walking without FES intervention. The error between the maximum ankle dorsiflexion angle during swing phase and the target angle using the proposed control strategy was the smallest among the four conditions. Moreover, there was no significant difference in the ankle plantar flexion angle at the toe-off event and the maximum knee flexion angle during swing phase between the proposed control strategy and walking without FES. In summary, the proposed control strategy can improve FES-assisted walking performances through adaptive modulation of stimulation timing and intensity when coping with variation, and may have good potential in clinic.
[EU Project] Neuroprosthesis user interface based on residual motor skills and muscle activity in persons with upper limb disabilities
A novel neuroprosthesis control system
Neuroprosthetic devices employ electrodes to interface with the nervous system and attempt to restore loss of function or movement. Scientists of the EU-funded Neuroprosthesis-UI project propose to develop a user interface that will allow people with upper limb disabilities to control the neuroprosthesis using residual motor skills. This interface will comprise different sensors to capture muscle contraction, and with the help of machine learning, it will decode user input into movement intention. This hybrid system will assist patients with upper limb disabilities such as spinal cord injury, stroke and multiple sclerosis in performing activities of daily life independently.Hide the project objective
In this project, I will develop a user interface that will allow persons with upper limb disabilities to control neuroprosthesis using their residual motor skills. This interface will consist of inertial sensors (IMU) and electromyography (EMG) that are capable of capturing movements and muscle contraction that even persons with high tetraplegia still can control. The interface will also be able to learn different inputs, customizing the system for each user. This requires techniques of machine learning, making it flexible and indicated for users with different upper limb disabilities, such as spinal cord injury, stroke and multiple sclerosis. The machine learning techniques will classify the user inputs into desired commands, working as an intention decoder. The interface will be used to control a hybrid upper limb neuroprosthesis based on surface functional electrical stimulation (FES) and a semi passive mechanical orthosis. The system will allow users to perform activities of daily life independently. To my knowledge, such a hybrid system with FES, and controlled by an interface based on IMUs, EMG and machine learning techniques is novel. I will be working with Christine Coste, an expert in neuroprosthesis for disabled persons, and her interdisciplinary team, which consist of engineers and health professionals with vast experience in neurorehabilitation. This fellowship will enable the transfer of knowledge between her team and me through experiments with real patients and mutual training. I can contribute to the team with my expertise in machine learning and control, whereas they have vast access to patients, medical doctors, mechanical designers, electrical stimulators and sensors. This project is going to be an important step in my career as expand my network in Europe, develop my skills as a biomedical engineer and improve my research experience towards becoming a world-leading expert in neurorehabilitation engineering.
Hand-wrist orthosis and functional electrical stimulation can be used in association, so that the wrist and the palm of the hand are fixed in an optimal position, while the electrodes stimulate the extrinsic muscles to flex and extend the fingers.