Posts Tagged FES

[ARTICLE] The Integration of Brain-Computer Interface (BCI) as Control Module for Functional Electrical Stimulation (FES) Intervention in Post-Stroke Upper Extremity Rehabilitation – Full Text

ABSTRACT

One of the prevalent disabilities after stroke is the loss of upper extremity motor function, leading survivors to suffer from an increased dependency in their activities of daily living and a general decrease in their overall quality of life. Therefore, the restoration of upper extremity function to improve survivors’ independency is crucial. Conventional stroke rehabilitation interventions, while effective, fall short of helping individuals achieve maximum recovery potential. Functional Electrical Stimulation (FES), both with passive and active approaches, has been found to moderately increase function in the affected limbs. This paper discusses a novel EEG-Based BCI-FES system that provides FES stimulation to the affected limbs based on the brain activity patterns of the patient specifically in the sensory motor cortex, while the patient imagines moving the affected limb. This system allows the synchronization of brain activity with peripheral movements, which may lead to brain reorganization and restoration of motor function by affecting motor learning or re-learning, and therefore induce brain plasticity to restore normal-like brain function.

INTRODUCTION

Stroke is one of the leading causes of severe motor disability, with approximately 800,000 individuals each year are experiencing a new or recurrent stroke in the US alone (1). Advances in healthcare and medical technology, and the high incidence of stroke and its increasing rate in the growing elderly population, have contributed to a relatively large population of stroke survivors currently estimated at 4 million individuals in the United States alone (1). These survivors are left with several devastating long-term neurological impairments.

The most apparent defect after a stroke is motor impairments, with impairment of upper extremity (UE) functions standing as the most disabling motor deficit. Approximately 80% of survivors suffering from UE paresis, and only about one-tenth of the them regain complete functional recovery (2). Stroke survivors generally suffer from a decrease in their quality of life, and an increase dependency in their activities of daily living. Statistically, close to one quarter of the stroke survivors become dependent in activities of daily living (3). Thus, the optimal restoration of arm and hand function is crucial to improve their independence.

Recently, several remarkable advancements in the medical management of stroke have been made. However, a widely applicable or effective medical treatment is still missing, and most post-stroke care will continue to depend on rehabilitation interventions (4). The available UE stroke rehabilitation interventions can be categorized as: conventional physical and occupational therapy, constraint-induced movement therapy (CIT), functional electrical stimulation (FES), and robotic-aided and sensor-based therapy systems (5). Although an increased effort has been made to enhance the recovery process following a stroke, survivors generally do not reach their full recovery potential. Thus, the growing population of stroke survivors is in a vital need for innovative strategies in stroke rehabilitation, especially in the domain of UE motor rehabilitation. This paper presents an innovative integration of a brain-computer interface (BCI) system to actively control the delivery of FES. Early research and product development activities are advancing the reality of this becoming a mainstream intervention option.

PASSIVE VS. ACTIVE DELIVERY OF FES

The use of FES on the impaired arm is an accepted intervention for stroke rehabilitation aiming to improve motor function. A systematic review with meta-analysis of 18 randomized control trials found that FES had a moderate effect on activity compared with no intervention or placebo and a large effect on UE activity compared to control groups, suggesting that FES should be used in stroke rehabilitation to improve the ability to perform activities (6). Specifically, improvements in UE motor function after intensive FES intervention can be ascribed to the increased ability to voluntarily contract impaired muscles, the reduction in spasticity and improved muscle tone in the stimulated muscles, and the increased range of motion in all joints (7). These improvements in UE after FES could be attributable to multiple neural mechanisms, with one mechanism suggesting that proprioceptive sensory input and visual perception of the movement could promote neural reorganization and motor learning (8). A potential limiting factor to the application of FES is that the stimulation is administered manually, usually from a therapist, without any regard to the concurrent brain activity of the patient. This makes the delivery a passive process with no to minimal coordination with the mental task required to happen concurrently from the patient.

On the other hand, electromyography (EMG)-triggered FES systems made the delivery of FES an active process. Such systems are activated through detecting a preset electrical threshold in certain muscles, which provide the user (patient) the ability to actively control the delivery of FES and make the delivery concurrent with the patient’s brain activity. However, a systematic review of 8 randomized controlled trials (n=157) that assessed the effects of EMG-triggered neuromuscular electrical stimulation for improving hand function in stroke patients found no statistically significant differences in effects when compared to patients receiving usual care (9). A possibility to explain the shortcoming of EMG-triggered FES systems, is that the ability of the brain to generate and send efficient neural signals to the peripheral nervous system is disrupted after stroke, which could affect the control mechanism of these systems. Thus, the synchronization of FES with brain activity maybe critical for the optimization of recovery.

AN ACTIVE EEG-BASED BCI-FES SYSTEM

BCI technology can be used to actively control the FES application through detecting the brain neural activity directly when imagining or attempting a movement. Performing or mentally imagining (or as it commonly called motor imagery) a movement results in the generation of neurophysiological phenomena called event-related desynchronization or synchronization (ERD or ERS). ERD or ERS can be observed from Mu (9–13 Hz) or Beta rhythms (22–29 Hz) over the primary sensorimotor area contralateral to the imagined part of the body (10). These rhythms can be detected using electroencephalography (EEG). Therefore, an EEG based BCI system can be utilized to provide automated FES neurofeedback through detecting either actual movement or motor imagery (MI) and can be used to train the voluntary modulation of these rhythms. The ability to modulate these rhythms alongside the real-time neurofeedback from the FES application may induce neuroplastic change in a disrupted motor system to allow for more normal motor-related brain activity, and thus promote functional recovery. Figure 1 provides an overview of the BCI-FES system.

Any BCI-FES intervention session includes two screening tasks: an open-loop screening followed by a closed-loop task. The open-loop screening task is used to identify appropriate EEG-based control features to guide all subsequent closed-loop tasks. In the open-loop screening task, subjects are instructed to perform attempted movement of either hand by following on-screen cues of “right”, “left”, and “rest”. The attempted movement can vary across subjects, depending on the subject’s baseline abilities and recovery goals. For example, subjects can perform opening and closing of the hand or wrist flexion/extension movements. During this screening task, no feedback is provided to the subject.

figure 2 shows a screenshot of the closed-loop task interface, with a ball at the center and a target to the right, in order to provide a cue for the user to move his/her right hand.

Figure 2. Screenshot of Closed-loop Task

Data from the open-loop screening task will then be analyzed to identify appropriate EEG-based control features by determine the EEG channels the presents the largest r-squared values within the frequency ranges of the Mu and Beta rhythms for each attempted movement using left or right hand (11). The identified channels and the specific frequency bins will then be used to control the signals for the closed-loop neurofeedback task.

In the closed-loop screening task, a real-time visual feedback is given to the subject in a form of a game. A ball appears on the center of a computer monitor with a vertical rectangle (target) to either the right or left side of the screen (Figure 2). The movement of the ball is controlled by the BCI system in which the detection of an attempted movement in either hand will be translated into moving the ball toward the same side. For example, if the target appeared on the left side of the screen and the BCI system detected a movement attempt of the user’s left hand, the ball then moves toward the left. Users are instructed to perform or attempt the same movement that they used during the open-loop task. The FES electrodes are placed on the subject’s affected side over a specific muscle of the forearm. The selection of which muscle to be innervated with FES is dependent on the rehabilitation goal for the subject. For example, if a subject is having a difficulty extending his/her wrist, the FES electrodes are placed over the extensor muscles of the impaired forearm.

The FES neurofeedback is triggered when cortical activity related to attempted movement of the impaired limb is detected by the BCI system, and the subject is cued to attempt movement of the impaired hand. Thus, since both ball movement and FES are controlled by the same set of EEG signals, FES is only applied when the ball moves correctly toward the target on the affected side of the body. This triggering of the FES ensures that only consistent, desired patterns of brain activity associated with attempted movement of the impaired hand are rewarded with feedback from the FES device.

DISCUSSION

The growing population of stroke survivors constitutes an increasing need for new strategies in stroke rehabilitation. Thus, it is imperative to explore novel intervention technologies that present promise to aid in the recovery process of this population. Some studies suggest that noninvasive EEG-based BCI systems hold a potential for facilitating upper extremities motor recovery after stroke (12,13). Although several groups had gave up on the idea of using non-invasive EEG-based BCI systems for control, there might be several implementations of these systems in the context of rehabilitation that yet need to be explored. The active EEG-based BCI-FES system is one example. However, more research and clinical studies are needed to investigate the efficacy of the system in order to be accepted and integrated into regular stroke rehabilitation practice.

REFERENCES

(1) Norrving B, Kissela B. The global burden of stroke and need for a continuum of care. Neurology 2013 Jan 15;80(3 Suppl 2):S5-12.

(2) Langhorne P, Coupar F, Pollock A. Motor recovery after stroke: a systematic review. The Lancet Neurology 2009;8(8):741-754.

(3) Sanchez RJ, Liu J, Rao S, Shah P, Smith R, Rahman T, et al. Automating arm movement training following severe stroke: functional exercises with quantitative feedback in a gravity-reduced environment. IEEE Transactions on neural systems and rehabilitation engineering 2006;14(3):378-389.

(4) Langhorne P, Bernhardt J, Kwakkel G. Stroke rehabilitation. The Lancet 2011;377(9778):1693-1702.

(5) Loureiro RC, Harwin WS, Nagai K, Johnson M. Advances in upper limb stroke rehabilitation: a technology push. Med Biol Eng Comput 2011;49(10):1103.

(6) Howlett OA, Lannin NA, Ada L, McKinstry C. Functional electrical stimulation improves activity after stroke: a systematic review with meta-analysis. Arch Phys Med Rehabil 2015;96(5):934-943.

(7) Kawashima N, Popovic MR, Zivanovic V. Effect of intensive functional electrical stimulation therapy on upper-limb motor recovery after stroke: case study of a patient with chronic stroke. Physiotherapy Canada 2013;65(1):20-28.

(8) Wang R. Neuromodulation of effects of upper limb motor function and shoulder range of motion by functional electric stimulation (FES). Operative Neuromodulation: Springer; 2007. p. 381-385.

(9) Meilink A, Hemmen B, Seelen H, Kwakkel G. Impact of EMG-triggered neuromuscular stimulation of the wrist and finger extensors of the paretic hand after stroke: a systematic review of the literature. Clin Rehabil 2008;22(4):291-305.

(10) Ang KK, Guan C. EEG-Based Strategies to Detect Motor Imagery for Control and Rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2017;25(4):392-401.

(11) Wilson JA, Schalk G, Walton LM, Williams JC. Using an EEG-based brain-computer interface for virtual cursor movement with BCI2000. J Vis Exp 2009 Jul 29;(29). pii: 1319. doi(29):10.3791/1319.

(12) Caria A, Weber C, Brötz D, Ramos A, Ticini LF, Gharabaghi A, et al. Chronic stroke recovery after combined BCI training and physiotherapy: a case report. Psychophysiology 2011;48(4):578-582.

(13) Young BM, Nigogosyan Z, Remsik A, Walton LM, Song J, Nair VA, et al. Changes in functional connectivity correlate with behavioral gains in stroke patients after therapy using a brain-computer interface device. Frontiers in neuroengineering 2014;7:25.

ACKNOWLEDGMENT

This project is supported in part by UW-Madison Institute for Clinical and Translational Research, and College of Health Sciences, UW-Milwaukee.

 

via The Integration of Brain-Computer Interface (BCI) as Control Module for Functional Electrical Stimulation (FES) Intervention in Post-Stroke Upper Extremity Rehabilitation

, , , , , , , ,

Leave a comment

[ARTICLE] A portable assist-as-need upper-extremity hybrid exoskeleton for FES-induced muscle fatigue reduction in stroke rehabilitation – Full Text

Abstract

Background

Hybrid exoskeletons are a recent development which combine Functional Electrical Stimulation with actuators to improve both the mental and physical rehabilitation of stroke patients. Hybrid exoskeletons have been shown capable of reducing the weight of the actuator and improving movement precision compared to Functional Electrical Stimulation alone. However little attention has been given towards the ability of hybrid exoskeletons to reduce and manage Functional Electrical Stimulation induced fatigue or towards adapting to user ability. This work details the construction and testing of a novel assist-as-need upper-extremity hybrid exoskeleton which uses model-based Functional Electrical Stimulation control to delay Functional Electrical Stimulation induced muscle fatigue. The hybrid control is compared with Functional Electrical Stimulation only control on a healthy subject.

Results

The hybrid system produced 24° less average angle error and 13.2° less Root Mean Square Error, than Functional Electrical Stimulation on its own and showed a reduction in Functional Electrical Stimulation induced fatigue.

Conclusion

As far as the authors are aware, this is the study which provides evidence of the advantages of hybrid exoskeletons compared to use of Functional Electrical Stimulation on its own with regards to the delay of Functional Electrical Stimulation induced muscle fatigue.

Background

Stroke is the second largest cause of disability worldwide after dementia [1]. Temporary hemiparesis is common among stroke survivors. Regaining strength and movement in the affected side takes time and can be improved with the use of rehabilitation therapy involving repetitive and function-specific tasks [2]. Muscle atrophy is another common issue that occurs after a stroke due to lack of use of the muscle. For each day a patient is in hospital lying in bed with minimal activity approximately 13% of muscular strength is lost (Ellis. Liam, Jackson. Samuel, Liu. Cheng-Yueh, Molloy. Peter, Paterson. Kelsey, Lower Limb Exoskeleton Final Report, unpublished). Electromechanically actuated exoskeletons offer huge advantages in their ability to repetitively and precisely provide assistance/resistance to a user. However electromechanical actuators which provide the required forces are often heavy in weight and have high power requirements which limits portability. Furthermore, muscle atrophy can only be prevented by physically working the muscles either through the patient’s own volition or the use of Functional Electrical Stimulation (FES).

FES is the application of high frequency electrical pulses to the nerves or directly to the muscle belly in order to elicit contractions in the muscle. FES devices are typically lightweight and FES is well suited to reducing muscle atrophy in patients with no or extremely limited movement. The trade off to this is that precise control of FES is extremely difficult and controlling specific, repetitive, and functional movement is not easily accomplished. Furthermore, extended use of FES is limited by the introduction of muscle fatigue caused by the unnatural motor unit recruitment order [3]. The forces required for large movements, such as shoulder abduction, are too great to be provided by the use of FES which is much better suited to smaller movements such as finger extension [45]. Some patients also find the use of FES painful.

Combining the use of FES and an electromechanical actuator within an exoskeleton can potentially overcome the limitations of each individual system. Despite the potential advantages of hybrid exoskeletons, so far only limited studies have been done on their effectiveness. A recent review was conducted into upper-extremity hybrid exoskeletons [6] which highlighted the advantages hybrid exoskeletons (exoskeletons which combine FES with an actuator) have with regards to improving the precision of FES induced movements. However, little attention has been given towards reduction and management of FES-induced fatigue. FES control systems used for upper-extremity hybrid exoskeletons simply manually ramp up stimulation intensity when fatigue is observed.

This work describes the design and testing of an assist-as-need upper-extremity hybrid exoskeleton which uses model-based control of FES with a focus on reducing FES-induced muscle fatigue. The control system is described in Section “Theory”, and the results are presented in Section “Results”. A discussion of the results is given in Section “Discussion”. Conclusions are summarised in Section “Conclusion”. Methods, physical structure of the exoskeleton, and the sensing system is described in Section “Material and methods”.[…]

 

Continue —->  A portable assist-as-need upper-extremity hybrid exoskeleton for FES-induced muscle fatigue reduction in stroke rehabilitation | BMC Biomedical Engineering | Full Text

figure10

Fig. 10 The Powered Exoskeleton (Right Arm)

, , , , , , , , ,

Leave a comment

[Abstract] The effects of a robot-assisted arm training plus hand functional electrical stimulation on recovery after stroke: a randomized clinical trial

Abstract

Objective

To compare the effects of unilateral, proximal arm robot-assisted therapy combined with hand functional electrical stimulation to intensive conventional therapy for restoring arm function in subacute stroke survivors.

Design

This was a single blinded, randomized controlled trial.

Setting

Inpatient Rehabilitation University Hospital.

Participants

Forty patients diagnosed with ischemic stroke (time since stroke <8 weeks) and upper limb impairment were enrolled.

Interventions

Participants randomized to the experimental group received 30 sessions (5 sessions/week) of robot-assisted arm therapy and hand functional electrical stimulation (RAT + FES). Participants randomized to the control group received a time-matched intensive conventional therapy (ICT).

Main outcome measures

The primary outcome was arm motor recovery measured with the Fugl-Meyer Motor Assessment. Secondary outcomes included motor function, arm spasticity and activities of daily living. Measurements were performed at baseline, after 3 weeks, at the end of treatment and at 6-month follow-up. Presence of motor evoked potentials (MEPs) was also measured at baseline.

Results

Both groups significantly improved all outcome measures except for spasticity without differences between groups. Patients with moderate impairment and presence of MEPs who underwent early rehabilitation (<30 days post stroke) demonstrated the greatest clinical improvements.

Conclusions

A robot-assisted arm training plus hand functional electrical stimulation was no more effective than intensive conventional arm training. However, at the same level of arm impairment and corticospinal tract integrity, it induced a higher level of arm recovery.

 

via The effects of a robot-assisted arm training plus hand functional electrical stimulation on recovery after stroke: a randomized clinical trial – ScienceDirect

, , , , , , , , , ,

Leave a comment

[Abstract] Effect of functional electrical stimulation plus body weight-supported treadmill training for gait rehabilitation in patients with poststroke – a retrospective case-matched study.

Abstract

BACKGROUND:

Functional electrical stimulation (FES) plus body weight-supported treadmill training (BWSTT) provide effective gait training for poststroke patients with abnormal gait. These features promote a successful active motor relearning of ambulation in stroke survivors.

AIM:

This is a retrospective study to assess the effect of FES plus BWSTT for gait rehabilitation in patients poststroke.

DESIGN:

A retrospective case-matched study.

SETTING:

Participants were recruited from a rehabilitation department in an acute university-affiliated hospital.

POPULATION:

Ninety patients poststroke from Yue Bei People’s Hospital underwent BWSTT (A: control group) were compared to an equal number of cross-matched patients who received FES plus BWSTT (B: FES plus BWSTT group).

METHODS:

While B group received FES for 45 minutes plus BSWTT for 30 minutes in the program, group A received time-matched BWSTT alone. The walking speed, step length, step cadence, Fugl-Meyer lower-limb scale (LL-FMA), composite spasticity scale (CSS), 10-Meter Walk Test (10MWT), Tinetti Balance Test (TBT) and nerve physiology testing were collected before and after intervention.

RESULTS:

One hundred and eighty patients with poststroke abnormal gait were chosen. There were significant differences in walking speed, step length, step cadence, LL-FMA, CSS, TBT, and 10MWT between baseline and post-intervention (P<0.05). There were significant differences in walking speed, step length, step cadence, LL-FMA, CSS, TBT, and 10MWT between two groups at the end of the eighth week (P<0.05), but not at baseline (P>0.05). In comparison with group A, the peak of somatosensory evoked potential (SEP) and motor evoked potential (MEP) amplitude increased, the latency was shortened, and the conduction velocity of sensory nerve (SCV) and motor nerve (MCV) was significantly increased in the group B (P < 0.05). No adverse events occurred during the study.

CONCLUSIONS:

This study suggests that FES plus BWSTT could be more effective than BWSTT alone in the improvement of gait, balance, spasticity, and function of the lower limb in patients poststroke.

CLINICAL REHABILITATION IMPACT:

Introduce effective rehabilitation strategies for poststroke patients with abnormal gait.

 

via Effect of functional electrical stimulation plus body weight-supported treadmill training for gait rehabilitation in patients with poststroke-a ret… – PubMed – NCBI

, , , , , , , ,

Leave a comment

[Abstract + References] A Wireless BCI-FES Based on Motor Intent for Lower Limb Rehabilitation

Abstract

Recent investigations have proposed brain computer interfaces combined with functional electrical stimulation as a novel approach for upper limb motor recovery. These systems could detect motor intention movement as a power decrease of the sensorimotor rhythms in the electroencephalography signal, even in people with damaged brain cortex. However, these systems use a large number of electrodes and wired communication to be employed for gait rehabilitation. In this paper, the design and development of a wireless brain computer interface combined with functional electrical stimulation aimed at lower limb motor recovery is presented. The design requirements also account the dynamic of a rehabilitation therapy by allowing the therapist to adapt the system during the session. A preliminary evaluation of the system in a subject with right lower limb motor impairment due to multiple sclerosis was conducted and as a performance metric, the true positive rate was computed. The developed system evidenced a robust wireless communication and was able to detect lower limb motor intention. The mean of the performance metric was 75%. The results encouraged the possibility of testing the developed system in a gait rehabilitation clinical study.

References

  1. 1.
    Pfurtscheller, G., Mcfarland, D.: BCIs that use sensorimotor rhythms. In: Wolpaw, J.R., Wolpaw, E. (eds.) Brain-Computer Interfaces: Principles and Practice, pp. 227–240. Oxford University Press (2012)Google Scholar
  2. 2.
    Carrere, L.C., Tabernig, C.B.: Detection of foot motor imagery using the coefficient of determination for neurorehabilitation based on BCI technology. IFMBE Proc. 49, 944–947 (2015).  https://doi.org/10.1007/978-3-319-13117-7_239CrossRefGoogle Scholar
  3. 3.
    Sannelli, C., Vidaurre, C., Müller, K.R., Blankertz, B.: A large scale screening study with a SMR-based BCI: categorization of BCI users and differences in their SMR activity (2019)Google Scholar
  4. 4.
    Do, A.H., Wang, P.T., King, C.E., Schombs, A., Cramer, S.C., Nenadic, Z.: Brain-computer interface controlled functional electrical stimulation device for foot drop due to stroke, pp. 6414–6417 (2012)Google Scholar
  5. 5.
    Ramos-Murguialday, A., Broetz, D., Rea, M., Yilmaz, Ö., Brasil, F.L., Liberati, G., Marco, R., Garcia-cossio, E., Vyziotis, A., Cho, W., Cohen, L.G., Birbaumer, N.: Brain-Machine-interface in chronic stroke rehabilitation: a controlled study. Ann. Neurol. 74, 100–108 (2014).  https://doi.org/10.1002/ana.23879.Brain-Machine-InterfaceCrossRefGoogle Scholar
  6. 6.
    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, A.G., Millán, J.D.R.: Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke. Nat. Commun. 9, 1–13 (2018).  https://doi.org/10.1038/s41467-018-04673-zCrossRefGoogle Scholar
  7. 7.
    Tabernig, C.B., Lopez, C.A., Carrere, L.C., Spaich, E.G., Ballario, C.H.: Neurorehabilitation therapy of patients with severe stroke based on functional electrical stimulation commanded by a brain computer interface. J. Rehabil. Assist. Technol. Eng. 5, 205566831878928 (2018).  https://doi.org/10.1177/2055668318789280CrossRefGoogle Scholar
  8. 8.
    McCrimmon, C.M., King, C.E., Wang, P.T., Cramer, S.C., Nenadic, Z., Do, A.H.: Brain-controlled functional electrical stimulation therapy for gait rehabilitation after stroke: a safety study. J. Neuroeng. Rehabil. 12 (2015).  https://doi.org/10.1186/s12984-015-0050-4
  9. 9.
    g.Nautilus wireless biosignal acquisition Homepage. http://www.gtec.at/Products/Hardware-and-Accessories/g.Nautilus-Specs-Features
  10. 10.
    Emotiv EpocFlex flexible wireless EEG system Homepage. https://www.emotiv.com/epoc-flex/
  11. 11.
    Vuckovic, A., Wallace, L., Allan, D.: Hybrid brain-computer interface and functional electrical stimulation for sensorimotor training in participants with tetraplegia: a proof-of-concept study. J. Neurol. Phys. Ther. 39, 3–14 (2015)CrossRefGoogle Scholar
  12. 12.
    Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N., Wolpaw, J.R.: BCI2000: a general-purpose brain-computer interface (BCI) system. IEEE Trans. Biomed. Eng. 51, 1034–1043 (2004).  https://doi.org/10.1109/TBME.2004.827072CrossRefGoogle Scholar
  13. 13.
    McCrimmon, C.M., Fu, J.L., Wang, M., Lopes, L.S., Wang, P.T., Karimi-Bidhendi, A., Liu, C.Y., Heydari, P., Nenadic, Z., Do, A.H.: Performance assessment of a custom, portable, and low-cost brain-computer interface platform. IEEE Trans. Biomed. Eng. 64, 2313–2320 (2017).  https://doi.org/10.1109/TBME.2017.2667579CrossRefGoogle Scholar

via A Wireless BCI-FES Based on Motor Intent for Lower Limb Rehabilitation | SpringerLink

, , , , , , ,

Leave a comment

[Abstract + References] Functional Electrical Stimulation for Gait Rehabilitation – Conference paper

Abstract

Conditions that can lead to a full or partial motor function loss, such as stroke or multiple sclerosis, leave people with disabilities that may interfere severely with lower body movements, such as gait. Drop Foot (DF) is a gait disorder that results in a reduced ability or total inability to contract the Tibialis Anterior (TA) muscle, causing an inability to raise the foot during gait. One of the most effective methods to correct DF is Functional Electrical Stimulation (FES). FES is a technique used to reproduce the activation patterns of functional muscles, in order to create muscular contractions through electrical stimulation of the muscle’s nervous tissue.

FES has first been introduced in 1961. However, the available commercial FES systems still do not take into account the fact that the gait differs from subject to subject, depending on their physical conditionmuscular fatigue and rehabilitation stage. Therefore, they are unable to provide a personalized assistance to the user, delivering constant stimulation pulses that are only based on gait events. Consequently, they promote the early onset of fatigue and generate coarse movements. This dissertation aims to tackle the aforementioned issues by developing a FES system for personalized DF correction, tailored to each individual user’s needs through the use of a Neural Network (NN).

A Non-Linear Autoregressive Neural Network with Exogenous inputs (NARX Neural Network) was used to model the dynamics of the electrically stimulated TA muscle, in a novel approach that uses both the foot angle and the foot velocity. The model was combined with a Proportional Derivative controller to help compensate for any external disturbances. In order to create more natural movements, reference trajectories were obtained by recording the foot angle and velocity of healthy subjects walking at different speeds.

The system has been validated with a healthy subject walking at 3 different speeds on a treadmill: 1 km/h, 1.5 km/h and 2 km/h. It was able to track the desired trajectory for every speed, thus creating a more natural movement and effectively correcting DF gait.

References

  1. 1.
    Melo, P.L., Silva, M.T., Martins, J.M., Newman, D.J.: Technical developments of functional electrical stimulation to correct drop foot: sensing, actuation and control strategies. Clin. Biomech. 30(2), 101–113 (2015)CrossRefGoogle Scholar
  2. 2.
    Kesar, T., Chou, L.W., Binder-Macleod, S.A.: Effects of stimulation frequency versus pulse duration modulation on muscle fatigue. J. Electromyogr. Kinesiol. 18(4), 662–671 (2008)CrossRefGoogle Scholar
  3. 3.
    Hunt, K.J., Munih, M., Donaldson, N.D.N., Barr, F.M.D.: Investigation of the hammerstein hypothesis in the modeling of electrically stimulated muscle. IEEE Trans. Biomed. Eng. 45(8), 998–1009 (1998)CrossRefGoogle Scholar
  4. 4.
    Johnson, C.A., Burridge, J.H., Strike, P.W., Wood, D.E., Swain, I.D.: The effect of combined use of botulinum toxin type A and functional electric stimulation in the treatment of spastic drop foot after stroke: a preliminary investigation. Arch. Phys. Med. Rehabil. 85(June), 902–909 (2004)CrossRefGoogle Scholar
  5. 5.
    Brend, O., Freeman, C., French, M.: Multiple-model adaptive control of functional electrical stimulation. IEEE Trans. Control Syst. Technol. 23(5), 1901–1913 (2015)CrossRefGoogle Scholar
  6. 6.
    Luzio de Melo, P.: A novel functional electrical stimulation system and strategies for motor rehabilitation. Ph.D thesis, Universidade de Lisboa – Instituto Superior Técnico (2014)Google Scholar
  7. 7.
    Luzio de Melo, P., da Silva, M.T., Martins, J., Newman, D.: A microcontroller platform for the rapid prototyping of functional electrical stimulation-based gait neuroprostheses. Artif. Organs 39(5), E56–E66 (2015)CrossRefGoogle Scholar
  8. 8.
    Science, I., Hospital, M.N.: Learning control of hand posture with neural network in FES for hemiplegics, vol. 20, no. 5, pp. 2588–2589 (1998)Google Scholar
  9. 9.
    Imatz-ojanguren, E., Irigoyen, E., Valencia-blanco, D., Keller, T.: Electrical Stimulation in Able-bodied and Hemiplegic Subjects, vol. 0, pp. 1–9 (2016)Google Scholar
  10. 10.
    Popov, N.S., Dozić, D.J., Stanković, M., Krajoski, G.M., Stanišić, D.: Development of a Closed Loop FES System Based on NARX Radial Based Network, pp. 70–74 (2015)CrossRefGoogle Scholar
  11. 11.
    Previdi, F.: Identification of black-box nonlinear models for lower limb movement control using functional electrical stimulation. Control Eng. Pract. 10(1), 91–99 (2002)CrossRefGoogle Scholar
  12. 12.
    Chang, G.C., Luh, J.J., Liao, G.D., Lai, J.S., Cheng, C.K., Kuo, B.L., Kuo, T.S.: A neuro-control system for the knee joint position control with quadriceps stimulation. IEEE Trans. Rehabil. Eng. 5(1), 2–11 (1997)CrossRefGoogle Scholar
  13. 13.
    Chen, Y.L., Chen, S.C., Chen, W.L., Hsiao, C.C., Kuo, T.S., Lai, J.S.: Neural network and fuzzy control in FES-assisted locomotion for the hemiplegic. J. Med. Eng. Technol. 28(1), 32–38 (2004)CrossRefGoogle Scholar
  14. 14.
    Azura, N., Bin, S., Kamaruddin, A., Mohamed, N., Mohamed, N.B.: The Quadriceps Muscle of Knee Joint Modelling Using Neural Network Approach: Part 1, pp. 52–57 (2016)Google Scholar
  15. 15.
    Yassin, I.M., Jailani, R., Syahirul, M., Megat, A., Baharom, R., Huzaifah, A.: Comparison Between Cascade Forward and Multi-Layer Perceptron Neural Networks for NARX Functional Electrical Stimulation (FES) -Based Muscle Model, vol. 7, no. 1, pp. 215–221 (2017)Google Scholar
  16. 16.
    Yilei, W., Qing, S., Xulei, Y., Li, L.: Recurrent Neural Network Control of Functional Electrical Stimulation Systems, pp. 400–404 (2006)Google Scholar
  17. 17.
    Ferrarin, M., D’Acquisto, E.: An experimental PID controller for knee movement restoration with closed loop FES system. In: Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 453–454 (1996)Google Scholar
  18. 18.
    Qiu, S., He, F., Tang, J., Xu, J., Zhang, L., Zhao, X., Qi, H., Zhou, P., Cheng, X., Wan, B., Ming, D.: Intelligent algorithm tuning PID method of function electrical stimulation using knee joint angle. In: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Annual Conference, vol. 2014, pp. 2561–2564 (2014)Google Scholar
  19. 19.
    Basith, A.L., Arifin, A., Arrofiqi, F., Watanabe, T., Nuh, M.: Embedded fuzzy logic controller for functional electrical stimulation system. In: 2016 International Seminar on Intelligent Technology and Its Applications (ISITIA), pp. 89–94 (2016)Google Scholar
  20. 20.
    Quintern, J., Riener, R., Rupprecht, S.: Comparison of simulation and experiments of different closed-loop strategies for functional electrical stimulation: experiments in paraplegics. Artif. Organs 21(3), 232–235 (1997)CrossRefGoogle Scholar
  21. 21.
    Tu, X., Li, J., Li, J., Su, C., Zhang, S., Li, H., Cao, J., He, J.: Model-based hybrid cooperative control of hip-knee exoskeleton and FES induced ankle muscles for gait rehabilitation. Int. J. Pattern Recognit. Artif. Intell. 31(09), 1759019 (2017)CrossRefGoogle Scholar

via Functional Electrical Stimulation for Gait Rehabilitation | SpringerLink

, , , , , ,

Leave a comment

[WEB PAGE] Treatments for foot drop compared

 

Continue —> Treatments for foot drop compared | MS Trust

, , , , ,

1 Comment

[Wikipedia audio article] Electrical stimulation

This is an audio version of the Wikipedia Article: https://en.wikipedia.org/wiki/Functio…

00:01:21 1 Principles

00:09:14 2 History

00:10:01 3 Common applications

00:10:11 3.1 Spinal cord injury

00:11:09 3.1.1 Walking in spinal cord injury

00:15:01 3.2 Stroke and upper limb recovery

00:16:21 3.3 Drop foot

00:18:08 3.4 Stroke

00:18:58 3.5 Multiple sclerosis

00:20:06 3.6 Cerebral palsy

00:21:07 3.7 National Institute for Health and Care Excellence Guidelines (NICE) (UK)

00:21:47 4 In popular culture

00:22:10 5 See also

Listening is a more natural way of learning, when compared to reading. Written language only began at around 3200 BC, but spoken language has existed long ago.

Learning by listening is a great way to:

  • – increases imagination and understanding
  • – improves your listening skills
  • – improves your own spoken accent
  • – learn while on the move
  • – reduce eye strain

Now learn the vast amount of general knowledge available on Wikipedia through audio (audio article). You could even learn subconsciously by playing the audio while you are sleeping! If you are planning to listen a lot, you could try using a bone conduction headphone, or a standard speaker instead of an earphone.

Listen on Google Assistant through Extra Audio: https://assistant.google.com/services…

Other Wikipedia audio articles at: https://www.youtube.com/results?searc…

Upload your own Wikipedia articles through: https://github.com/nodef/wikipedia-tts

Speaking Rate: 0.9170272343252982 Voice name: en-AU-Wavenet-B

“I cannot teach anybody anything, I can only make them think.” – Socrates

SUMMARY 

Functional electrical stimulation (FES) is a technique that uses low-energy electrical pulses to artificially generate body movements in individuals who have been paralyzed due to injury to the central nervous system. More specifically, FES can be used to generate muscle contraction in otherwise paralyzed limbs to produce functions such as grasping, walking, bladder voiding and standing. This technology was originally used to develop neuroprostheses that were implemented to permanently substitute impaired functions in individuals with spinal cord injury (SCI), head injury, stroke and other neurological disorders. In other words, a person would use the device each time he or she wanted to generate a desired function. FES is sometimes also referred to as neuromuscular electrical stimulation (NMES).FES technology has been used to deliver therapies to retrain voluntary motor functions such as grasping, reaching and walking. In this embodiment, FES is used as a short-term therapy, the objective of which is restoration of voluntary function and not lifelong dependence on the FES device, hence the name functional electrical stimulation therapy, FES therapy (FET or FEST). In other words, the FEST is used as a short-term intervention to help the central nervous system of the person to re-learn how to execute impaired functions, instead of making the person dependent on neuroprostheses for the rest of her or his life.

, , , , , , ,

Leave a comment

[VIDEO] Stroke Rehabilitation: Use of electrical stimulation to help arm and hand recovery

This video demonstrates how to use FES, Functional Electrical Stimulation, to engage the muscles of the arm to extend the fingers.

, , , , , ,

Leave a comment

[Abstract] Bi-cephalic transcranial direct current stimulation combined with functional electrical stimulation for upper-limb stroke rehabilitation: A double-blind randomized controlled trial

Highlights

Bi-cephalic transcranial direct current stimulation (tDCS) plus functional electrical stimulation (FES) slightly improves reaching motor performance after stroke.

Bi-cephalic tDCS plus FES does not enhance reaching movement quality after stroke.

Bi-cephalic tDCS plus FES improves handgrip strength after stroke.

Abstract

Background

Stroke survivors often present poor upper-limb (UL) motor performance and reduced movement quality during reaching tasks. Transcranial direct current stimulation (tDCS) and functional electrical stimulation (FES) are widely used strategies for stroke rehabilitation. However, the effects of combining these two therapies to rehabilitate individuals with moderate and severe impairment after stroke are still unknown.

Objective

Our primary aim was to evaluate the effects of concurrent bi-cephalic tDCS and FES on UL kinematic motor performance and movement quality. Our secondary aim was to verify the effects of the combined therapies on handgrip force and UL motor impairment.

Methods

We randomized 30 individuals with moderate and severe chronic hemiparesis after stroke into tDCS plus FES (n = 15) and sham tDCS plus FES (n = 15) groups. Participants were treated 5 times a week for 2 weeks. Kinematic UL motor performance (movement cycle time, velocity profile) and movement quality (smoothness, trunk contribution, joint angles), handgrip force and motor impairment were assessed before and after the intervention.

Results

For those participants allocated to the tDCS plus FES group, therapy was effective to improve movement cycle time (P = 0.039), mean reaching phase velocity (P = 0.022) and handgrip force (P = 0.034). Both groups showed improved mean returning phase velocity (P = 0.018), trunk contribution (P = 0.022), and movement smoothness (P = 0.001) as well as alleviated UL motor impairment (P = 0.002).

Conclusions

Concurrent bi-cephalic tDCS and FES slightly improved reaching motor performance and handgrip force of individuals with moderate and severe UL impairment after stroke.

via Bi-cephalic transcranial direct current stimulation combined with functional electrical stimulation for upper-limb stroke rehabilitation: A double-blind randomized controlled trial – ScienceDirect

, , , , , , ,

Leave a comment

%d bloggers like this: