The development of a potential optimized stimulation intensity envelope for drop foot applications

Machine-learning-based coordination of powered ankle-foot orthosis and functional electrical stimulation for gait control

This study proposes a novel gait rehabilitation method that uses a hybrid system comprising a powered ankle-foot orthosis (PAFO) and FES, and presents its coordination control. The developed system provides assistance to the ankle joint in accordance with the degree of volitional participation of patients with post-stroke hemiplegia. The PAFO adopts the desired joint angle and impedance profile obtained from biomechanical simulation. The FES patterns of the tibialis anterior and soleus muscles are derived from predetermined electromyogram patterns of healthy individuals during gait and personalized stimulation parameters. The CNN-based estimation model predicts the volitional joint torque from the electromyogram of the patient, which is used to coordinate the contributions of the PAFO and FES. The effectiveness of the developed hybrid system was tested on healthy individuals during treadmill walking with and without considering the volitional muscle activity of the individual. The results showed that consideration of the volitional muscle activity significantly lowers the energy consumption by the PAFO and FES while providing adaptively assisted ankle motion depending on the volitional muscle activities of the individual. The proposed system has potential use as an assist-as-needed rehabilitation system, where it can improve the outcome of gait rehabilitation by inducing active patient participation depending on the stage of rehabilitation.

DNN-Based FES Control for Gait Rehabilitation of Hemiplegic Patients

In this study, we proposed a novel machine-learning-based functional electrical stimulation (FES) control algorithm to enhance gait rehabilitation in post-stroke hemiplegic patients. The electrical stimulation of the muscles on the paretic side was controlled via deep neural networks, which were trained using muscle activity data from healthy people during gait. The performance of the developed system in comparison with that of a conventional FES control method was tested with healthy human subjects.

Human Gait Control Using Functional Electrical Stimulation Based on Controlling the Shank Dynamics

Introduction

Efficient gait control using Functional Electrical Stimulation (FES) is an open research problem. In this research, a new intermittent controller has been designed to control the human shank movement dynamics during gait.

Methods

In this approach, first, the three-dimensional phase space was constructed using the human shank movement data recorded from the healthy subjects. Then, three iterated sine-circle maps were extracted in the mentioned phase space. The three identified one-dimensional maps contained the essential information about the shank movement dynamics during a gait cycle. Next, an intermittent fuzzy controller was designed to control the shank angle. According to the adopted intermittent control strategy, the fuzzy controller is activated whenever the shank angle is far enough from the specific. The specific points are described using the identified iterated maps in the constructed phase space. In this manner, the designed controller is activated during a short-time fraction of the gait cycle time.

Results

The designed intermittent controller was evaluated through some simulation studies on a two-joint musculoskeletal model. The obtained results suggested that the pattern of the obtained hip and knee joint trajectories, the outputs of the musculoskeletal model, were acceptably similar to the joints' trajectories pattern of healthy subjects.

Conclusion

The intriguing similarity was observed between the dynamics of the recorded human data and those of the controlled musculoskeletal model. It supports the acceptable performance of the proposed control strategy.

Adaptive multichannel FES neuroprosthesis with learning control and automatic gait assessment

Background

FES (Functional Electrical Stimulation) neuroprostheses have long been a permanent feature in the rehabilitation and gait support of people who had a stroke or have a Spinal Cord Injury (SCI). Over time the well-known foot switch triggered drop foot neuroprosthesis, was extended to a multichannel full-leg support neuroprosthesis enabling improved support and rehabilitation. However, these neuroprostheses had to be manually tuned and could not adapt to the persons’ individual needs. In recent research, a learning controller was added to the drop foot neuroprosthesis, so that the full stimulation pattern during the swing phase could be adapted by measuring the joint angles of previous steps.

Methods

The aim of this research is to begin developing a learning full-leg supporting neuroprosthesis, which controls the antagonistic muscle pairs for knee flexion and extension, as well as for ankle joint dorsi- and plantarflexion during all gait phases. A method was established that allows a continuous assessment of knee and foot joint angles with every step. This method can warp the physiological joint angles of healthy subjects to match the individual pathological gait of the subject and thus allows a direct comparison of the two. A new kind of Iterative Learning Controller (ILC) is proposed which works independent of the step duration of the individual and uses physiological joint angle reference bands.

Results

In a first test with four people with an incomplete SCI, the results showed that the proposed neuroprosthesis was able to generate individually fitted stimulation patterns for three of the participants. The other participant was more severely affected and had to be excluded due to the resulting false triggering of the gait phase detection. For two of the three remaining participants, a slight improvement in the average foot angles could be observed, for one participant slight improvements in the averaged knee angles. These improvements where in the range of 4^circat the times of peak dorsiflexion, peak plantarflexion, or peak knee flexion.

Conclusions

Direct adaptation to the current gait of the participants could be achieved with the proposed method. The preliminary first test with people with a SCI showed that the neuroprosthesis can generate individual stimulation patterns. The sensitivity to the knee angle reset, timing problems in participants with significant gait fluctuations, and the automatic ILC gain tuning are remaining issues that need be addressed. Subsequently, future studies should compare the improved, long-term rehabilitation effects of the here presented neuroprosthesis, with conventional multichannel FES neuroprostheses.

Effects of timing parameter changes on the gait of functional electrical stimulation users with drop foot

Introduction

Functional electrical stimulation uses clinician-set parameters to modify stimulation. This study aimed to investigate whether timing parameters in the ODFS Pace functional electrical stimulation device have an effect on the gait of the general population of functional electrical stimulation users who have a foot drop.

Methods

Twelve functional electrical stimulation users with foot drop resulting from upper motor neurone disorders were recruited from the functional electrical stimulation Service in Leeds, UK. A crossover trial design was used, comparing adjusted values of rising ramp, delay and extension. Instrumented gait analysis was carried out to measure ankle dorsiflexion during the swing phase of gait, foot clearance from the ground, and speed of ankle plantarflexion at initial contact. The effect of timing parameters on gait kinematics was studied.

Results

No statistically significant effects on the measured parts of gait were found for any of the timing parameters. Trends were identified in average mid-swing ground clearance and dorsiflexion associated with the delay and rising ramp timing parameters.

Conclusions

Further work in this area should use larger numbers of participants. Based on these results, the effects of ramping and delay would be of particular interest for further study.

Real-Time Closed-Loop Functional Electrical Stimulation Control of Muscle Activation with Evoked Electromyography Feedback for Spinal Cord Injured Patients

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.

Football APP based on smart phone with FES in drop foot rehabilitation

BACKGROUND

Long-term, sustained progress is necessary in drop foot rehabilitation. The necessary inconvenient body training movements, the return trips to the hospital and repetitive boring training using functional electrical stimulation (FES) often results in the patient suspending their training. The patient's drop foot rehabilitation will not progress if training is suspended.

OBJECTIVE

A fast spread, highly portable drop foot rehabilitation training device based on the smart phone is presented. This device is combined with a self-made football APP and feedback controlled FES. The drop foot patient can easily engage in long term rehabilitation training that is more convenient and interesting.

METHODS

An interactive game is established on the smart phone with the Android system using the originally built-in wireless communications. The ankle angle information is detected by an external portable device as the game input signal. The electrical stimulation command to the external device is supplemented with FES stimulation for inadequate ankle efforts.

RESULTS

After six-weeks training using six cases, the results indicated that this training device showed significant performance improvement (p< 0.05) in the patient's ankle dorsiflexion strength, ankle dorsiflexion angle, control timing and Timed Up and Go.

CONCLUSIONS

Preliminary results show that this training device provides significant positive help to drop foot patients. Moreover, this device is based on existing and universally popular mobile processing, which can be rapidly promoted. The responses of clinical cases also show this system is easy to operate, convenient and entertaining. All of these features can improve the patient's willingness to engage in long term rehabilitation.

A Personalized Multi-Channel FES Controller Based on Muscle Synergies to Support Gait Rehabilitation after Stroke

It has been largely suggested in neuroscience literature that to generate a vast variety of movements, the Central Nervous System (CNS) recruits a reduced set of coordinated patterns of muscle activities, defined as muscle synergies. Recent neurophysiological studies have recommended the analysis of muscle synergies to finely assess the patient's impairment, to design personalized interventions based on the specific nature of the impairment, and to evaluate the treatment outcomes. In this scope, the aim of this study was to design a personalized multi-channel functional electrical stimulation (FES) controller for gait training, integrating three novel aspects: (1) the FES strategy was based on healthy muscle synergies in order to mimic the neural solutions adopted by the CNS to generate locomotion; (2) the FES strategy was personalized according to an initial locomotion assessment of the patient and was designed to specifically activate the impaired biomechanical functions; (3) the FES strategy was mapped accurately on the altered gait kinematics providing a maximal synchronization between patient's volitional gait and stimulation patterns. The novel intervention was tested on two chronic stroke patients. They underwent a 4-week intervention consisting of 30-min sessions of FES-supported treadmill walking three times per week. The two patients were characterized by a mild gait disability (walking speed > 0.8 m/s) at baseline. However, before treatment both patients presented only three independent muscle synergies during locomotion, resembling two different gait abnormalities. After treatment, the number of extracted synergies became four and they increased their resemblance with the physiological muscle synergies, which indicated a general improvement in muscle coordination. The originally merged synergies seemed to regain their distinct role in locomotion control. The treatment benefits were more evident for one patient, who achieved a clinically important change in dynamic balance (Mini-Best Test increased from 17 to 22) coupled with a very positive perceived treatment effect (GRC = 4). The treatment had started the neuro-motor relearning process also on the second subject, but twelve sessions were not enough to achieve clinically relevant improvements. This attempt to apply the novel theories of neuroscience research in stroke rehabilitation has provided promising results, and deserves to be further investigated in a larger clinical study.

Evoked Electromyographically Controlled Electrical Stimulation

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.

A Hybrid Functional Electrical Stimulation for Real-Time Estimation of Joint Torque and Closed-Loop Control of Muscle Activation

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.

A predictive model of muscle excitations based on muscle modularity for a large repertoire of human locomotion conditions

Humans can efficiently walk across a large variety of terrains and locomotion conditions with little or no mental effort. It has been hypothesized that the nervous system simplifies neuromuscular control by using muscle synergies, thus organizing multi-muscle activity into a small number of coordinative co-activation modules. In the present study we investigated how muscle modularity is structured across a large repertoire of locomotion conditions including five different speeds and five different ground elevations. For this we have used the non-negative matrix factorization technique in order to explain EMG experimental data with a low-dimensional set of four motor components. In this context each motor components is composed of a non-negative factor and the associated muscle weightings. Furthermore, we have investigated if the proposed descriptive analysis of muscle modularity could be translated into a predictive model that could: (1) Estimate how motor components modulate across locomotion speeds and ground elevations. This implies not only estimating the non-negative factors temporal characteristics, but also the associated muscle weighting variations. (2) Estimate how the resulting muscle excitations modulate across novel locomotion conditions and subjects. The results showed three major distinctive features of muscle modularity: (1) the number of motor components was preserved across all locomotion conditions, (2) the non-negative factors were consistent in shape and timing across all locomotion conditions, and (3) the muscle weightings were modulated as distinctive functions of locomotion speed and ground elevation. Results also showed that the developed predictive model was able to reproduce well the muscle modularity of un-modeled data, i.e., novel subjects and conditions. Muscle weightings were reconstructed with a cross-correlation factor greater than 70% and a root mean square error less than 0.10. Furthermore, the generated muscle excitations matched well the experimental excitation with a cross-correlation factor greater than 85% and a root mean square error less than 0.09. The ability of synthetizing the neuromuscular mechanisms underlying human locomotion across a variety of locomotion conditions will enable solutions in the field of neurorehabilitation technologies and control of bipedal artificial systems. Open-access of the model implementation is provided for further analysis at https://simtk.org/home/p-mep/.

Technical developments of functional electrical stimulation to correct drop foot: sensing, actuation and control strategies

This work presents a review on the technological advancements over the last decades of functional electrical stimulation based neuroprostheses to correct drop foot. Functional electrical stimulation is a technique that has been put into practice for several years now, and has been shown to functionally restore and rehabilitate individuals with movement disorders, such as stroke, multiple sclerosis and traumatic brain injury, among others. The purpose of this technical review is to bring together information from a variety of sources and shed light on the field's most important challenges, to help in identifying new research directions. The review covers the main causes of drop foot and its associated gait implications, along with several functional electrical stimulation-based neuroprostheses used to correct it, developed within academia and currently available in the market. These systems are thoroughly analyzed and discussed with particular emphasis on actuation, sensing and control of open- and closed-loop architectures. In the last part of this work, recommendations on future research directions are suggested.

Evoked electromyography-based closed-loop torque control in functional electrical stimulation

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.

Control of tibialis anterior FES envelop for unilateral drop foot gait correction using NARX neural network

In this paper a control methodology based on artificial neural networks (ANN) is proposed for control of ankle dorsiflexion in patients with unilateral drop foot. In the presented strategy, the electrical stimulation intensity for the disabled tibialis anterior (TA) muscle is controlled considering the existing coordination patterns between activities of the ipsilateral ankle dorsiflexor muscles and the contralateral ankle plantarflexor muscles during normal gait. Based on this coordination, in each gait cycle the TA muscle of one leg acts in close simultaneity with the calf muscle of the opposite leg. Therefore in this paper a dynamic ANN has been trained in a predictive manner, to forecast the disabled TA muscle activity based on the input from the healthy calf muscle of the opposite leg. The predicted TA activation is then used to control the TA muscle FES intensity in real time. Seven healthy volunteers participated in the experiments. Surface electromyogram was recorded from TA and calf muscle simultaneously on the opposite legs while walking in different gait frequencies. Results obtained from the controller are quite promising and show impressive generalization ability between subjects.

Design of a programmable multi-pattern FES system for restoring foot drop in stroke rehabilitation

A programmable and portable multi-pattern transcutaneous neuromuscular stimulator was developed and evaluated for correction of foot drop in stroke subjects. The stimulator unit was designed to optimize functionality while keeping its size and power consumption to a minimum. It had two channels of biphasic stimulation (charge-balanced and constant current), and all parameters were programmable to accommodate a range of stimulation profiles. The 'natural' electromyographic (EMG) pattern of tibialis anterior (TA) muscle stimulation envelope algorithms and constant amplitude stimulation envelope was provided for foot drop corrections in stroke patients. A foot-switch sensor was used to trigger the device in the swing phase of gait cycle. Various tests on prototype units were performed, including output power characteristics with a skin model, and tested with a stroke subject to validate the results. This paper provides a detailed description of the hardware and block-level functional electrical stimulation (FES) system design for applications in stroke rehabilitation.

Wireless distributed architecture for therapeutic functional electrical stimulation: a technology to design network-based muscle control

This paper presents a distributed Functional Electrical Stimulation architecture based on a wireless network, for therapeutic training of disabled patients. On this distributed architecture, a global controller can pilot a set of stimulation and acquisition units and modify dynamically stimulation and acquisition parameters. This solution intend to be a tool for researchers and therapist to develop closed-loop control algorithms and strategies for therapeutic rehabilitation applications with external FES, in a clinical context. In a wireless networkbased control, the variable delay introduced by the network must be taken into account to ensure the stability of the closed loop. Thus, in order to characterize the medium on which the control is performed, we carried out accurate measurements of the architecture performances (stack-crossing, round-trip time, etc.).

A biomechanical model to estimate corrective changes in muscle activation patterns for stroke patients

We have created a model to estimate the corrective changes in muscle activation patterns needed for a person who has had a stroke to walk with an improved gait-nearing that of an unimpaired person. Using this model, we examined how different functional electrical stimulation (FES) protocols would alter gait patterns. The approach is based on an electromyographically (EMG)-driven model to estimate joint moments. Different stimulation protocols were examined, which generated different corrective muscle activation patterns. These approaches grouped the muscles together into flexor and extensor groups (to simulate FES using surface electrodes) or left each muscle to vary independently (to simulate FES using intramuscular electrodes). In addition, we limited the maximal change in muscle activation (to reduce fatigue). We observed that with the two protocols (grouped and ungrouped muscles), the calculated corrective changes in muscle activation yielded improved joint moments nearly matching those of unimpaired subjects. The protocols yielded different muscle activation patterns, which could be selected based on practical condition. These calculated corrective muscle activation changes can be used in studying FES protocols, to determine the feasibility of gait retraining with FES for a given subject and to determine which protocols are most reasonable.

A programmable system of functional electrical stimulation (FES)

The development of a novel system intended to perform functional electrical stimulation (FES) is presented. A virtual instrument developed in Labview communicates with a PC through USB and controls the hardware compound of analog and digital circuits. The block diagram of the hardware and the main characteristics of the virtual instrument are presented, as well the results of the electrical safety tests and the errors associated to the programmed and real values of the amplitude, pulse width and frequency of the output current. The results point the equipment can be used in the therapy of paraplegic patients maintaining safety limits reported in the literature.

Effect of walking speed changes on tibialis anterior EMG during healthy gait for FES envelope design in drop foot correction

Functional electrical stimulation may be used to correct hemiplegic drop foot. An optimised stimulation envelope to reproduce the EMG pattern observed in the tibialis anterior (TA) during healthy gait has been proposed by O'Keeffe et al. [O'Keeffe, D.T., Donnelly, A.E., Lyons, G.M., 2003. The development of a potential optimised stimulation intensity envelope for drop foot applications. IEEE Transactions on Neural Systems and Rehabilitation Engineering]. However this envelope did not attempt to account for changes in TA activity with walking speed. The objective of this paper was to provide data to enable the specification of an algorithm to control the adaptation of an envelope with walking speed. Ten young healthy subjects walked on a treadmill at 11 different walking speeds while TA EMG was recorded. The results showed that TA EMG recorded around initial contact and at toe off changed with walking speed. At the slowest velocities, equivalent to hemiplegic walking, the toe-off burst (TOB) of EMG activity had larger peak amplitude than that of the heel-strike burst (HSB). The peak amplitude ratio of TOB:HSB was 1:0.69 at the slowest speed compared to, 1:1.18 and 1:1.5 for the self-selected and fastest speed, respectively. These results suggest that an FES envelope, which produces larger EMG amplitude for the TOB than the HSB, would be more appropriate at walking speeds typical of hemiplegic patients.