Functional Electrical Stimulation Control Research Articles

FES Control of a Finger MP Joint with a Proxy-Based Super-Twisting Algorithm

To improve motion accuracy through functional electrical stimulation (FES) of forearm muscles, feedback control laws are applied to the index finger’s metacarpophalangeal (MP) joint. This paper introduces a proxy-based super-twisting algorithm (PSTA) for precise servo control of MP joints via FES. The PSTA combines first-order sliding mode control with a second-order super-twisting algorithm, effectively preventing windup during FES saturation and ensuring robust, accurate control. An implicit Euler method minimizes numerical chattering in the digital implementation. Experiments with Arduino and volunteers confirm the algorithm’s effectiveness.

Pole placement tuning of proportional integral derivative feedback controller for knee extension model

Functional electrical stimulation (FES) has shown potential in rehabilitative exercises for patients recovering from spinal cord injuries. In recent developments, conventional open-loop FES control techniques have evolved into closed-loop systems that employ feedback controllers for automation. However, closed-loop FES systems often face challenges due to muscle non-linear effects, such as fatigue, time delays, stiffness, and spasticity. Therefore, an accurate non-linear knee model is required during the design stage, and precise tuning of the feedback controller parameters is vital. A proportional– integral–derivative (PID) controller is commonly used as a feedback controller due to its simplicity and ease of implementation. However, most PID tuning methods are complex and time consuming. This paper investigates the viability of employing the pole placement technique for tuning a PID controller that regulates the non-linear knee extension model. The pole placement method aims to improve the control and adaptability of the PID controller in closed-loop FES systems, specifically by facilitating knee extension exercises. MATLAB Simulink was used to assess the effectiveness of this tuning approach. Results showed that the PID controller performed satisfactorily without non-linearities, but performance varied with the inclusion of specific non-linearities. The pole placement tuning method facilitated preliminary assessments of PID controller performance, preceding highly advanced optimization.

Closed-Loop Control of Functional Electrical Stimulation Using a Selectively Recording and Bidirectional Nerve Cuff Interface.

Discriminating recorded afferent neural information can provide sensory feedback for closed-loop control of functional electrical stimulation, which restores movement to paralyzed limbs. Previous work achieved state-of-the-art off-line classification of electrical activity in different neural pathways recorded by a multi-contact nerve cuff electrode, by applying deep learning to spatiotemporal neural patterns. The objective of this study was to demonstrate the feasibility of this approach in the context of closed-loop stimulation. Acute in vivo experiments were conducted on 11 Long Evans rats to demonstrate closed-loop stimulation. A 64-channel ( 8×8 ) nerve cuff electrode was implanted on each rat's sciatic nerve for recording and stimulation. A convolutional neural network (CNN) was trained with spatiotemporal signal recordings associated with 3 different states of the hindpaw (dorsiflexion, plantarflexion, and pricking of the heel). After training, firing rates were reconstructed from the classifier outputs for each of the three target classes. A rule-based closed-loop controller was implemented to produce ankle movement trajectories using neural stimulation, based on the classified nerve recordings. Closed-loop stimulation was successfully demonstrated in 6 subjects. The number of successful movement sequence trials per subject ranged from 1-17 and number of correct state transitions per trial ranged from 3-53. This work demonstrates that a CNN applied to multi-contact nerve cuff recordings can be used for closed-loop control of functional electrical stimulation.

Hybrid Cooperative Control of Functional Electrical Stimulation and Robot Assistance for Upper Extremity Rehabilitation

Hybrid Cooperative Control of Functional Electrical Stimulation and Robot Assistance for Upper Extremity Rehabilitation

An Adaptive Hammerstein Model for FES-Induced Torque Prediction Based on Variable Forgetting Factor Recursive Least Squares Algorithm.

Modeling the muscle response to functional electrical stimulation (FES) is an important step during model-based FES control system design. The Hammerstein structure is widely used in simulating this nonlinear biomechanical response. However, a fixed relationship cannot cope well with the time-varying property of muscles and muscle fatigue. In this paper, we proposed an adaptive Hammerstein model to predict ankle joint torque induced by electrical stimulation, which used variable forgetting factor recursive least squares (VFFRLS) method to update the model parameters. To validate the proposed model, ten healthy individuals were recruited for short-duration FES experiments, ten for long-duration FES experiments, and three stroke patients for both. The isometric ankle dorsiflexion torque induced by FES was measured, and then the test performance of the fixed-parameter Hammerstein model, the adaptive Hammerstein model based on fixed forgetting factor recursive least squares (FFFRLS) and the adaptive Hammerstein model based on VFFRLS was compared. The goodness of fit, root mean square error, peak error and success rate were applied to evaluate the accuracy and stability of the model. The results indicate a significant improvement in both the accuracy and stability of the proposed adaptive model compared to the fixed-parameter model and the adaptive model based on FFFRLS. The proposed adaptive model enhances the ability of the model to cope with muscle changes.

Knee extension tracking and fatigue regulation results using a robust MPC approach in a hybrid exoskeleton

A nonlinear model predictive controller (NMPC) is a promising approach for cooperative control of functional electrical stimulation (FES) and an exoskeleton to assist limb function in people with spinal cord injury (SCI). Potentially, the cooperative control can mitigate the effects of FES-induced muscle fatigue, a significant technical challenge impeding the clinical translation of FES. However, previous NMPC-based cooperative designs have been limited to limb regulation and may not include fatigue regulation in the control objective. In this paper, we design a new NMPC law for limb tracking and fatigue regulation and present experimental results from participants with no disability and a participant with SCI. For robust tracking, this paper augments the NMPC with a Lyapunov-based feedback controller. A terminal controller and terminal constraint regions are specifically derived to show the robust NMPC scheme’s recursive feasibility and to satisfy the input constraints. The experimental results of continuous knee tracking show dynamic allocation of exoskeleton assistance despite decreasing control effectiveness of FES due to muscle fatigue. The robust tube-based MPC reduced the knee angle’s root mean square tracking error by 34% compared to a nominal MPC. It elicited cooperative behavior when it automatically modulated (increased or decreased) FES and motor inputs for different desired fatigue levels while maintaining desired knee angle tracking performance. The statistical t-test showed that the FES input was significantly reduced in the post-fatigue trial (with the desired fatigue level of 1), and the muscle was significantly less fatigued compared to the pre-fatigue experiment (with the desired fatigue level of 0.5).

Hybrid and adaptive control of functional electrical stimulation to correct hemiplegic gait for patients after stroke.

Introduction: Gait, as a fundamental human movement, necessitates the coordination of muscles across swing and stance phases. Functional electrical stimulation (FES) of the tibialis anterior (TA) has been widely applied to foot drop correction for patients with post-stroke during the swing phase. Although the gastrocnemius (GAS) during the stance phase is also affected, the Functional electrical stimulation of the gastrocnemius received less attention. Methods: To address this limitation, a timing- and intensity-adaptive Functional electrical stimulation control strategy was developed for both the TA and GAS. Each channel incorporates a speed-adaptive (SA) module to control stimulation timing and an iterative learning control (ILC) module to regulate the stimulation intensity. These modules rely on real-time kinematic or kinetic data during the swing or stance phase, respectively. The orthotic effects of the system were evaluated on eight patients with post-stroke foot drop. Gait kinematics and kinetics were assessed under three conditions: no stimulation (NS), Functional electrical stimulation to the ankle dorsiflexor tibialis anterior (SA-ILC DS) and FES to the tibialis anterior and the ankle plantarflexor gastrocnemius (SA-ILC DPS). Results: The ankle plantarflexion angle, the knee flexion angle, and the anterior ground reaction force (AGRF) in the SA-ILC DPS condition were significantly larger than those in the NS and SA-ILC DS conditions (p < 0.05). The maximum dorsiflexion angle during the swing phase in the SA-ILC DPS condition was similar to that in the SA-ILC DS condition, with both being significantly larger than the angle observed in the NS condition (p < 0.05). Furthermore, the angle error and force error relative to the set targets were minimized in the SA-ILC DPS condition. Discussion: The observed improvements can be ascribed to the appropriate stimulation timing and intensity provided by the SA-ILC DPS strategy. This study demonstrates that the hybrid and adaptive control strategy of functional electrical stimulation system offers a significant orthotic effect, and has considerable potential for future clinical application.

Closed-Loop FES Control of a Hybrid Exoskeleton during Sit-to-Stand Exercises: Concept and First Evaluation

Rehabilitation of paralysis caused by a stroke or a spinal cord injury remains a complex and time-consuming task. This work proposes a hybrid exoskeleton approach combining a traditional exoskeleton and functional electrical stimulation (FES) as a promising method in rehabilitation. However, hybrid exoskeletons with a closed-loop FES control strategy are functionally challenging to achieve and have not been reported often. Therefore, this study aimed to investigate a powered lower-limb exoskeleton with a closed-loop FES control for Sit-to-Stand (STS) movements. A body motion capture system was applied to record precise hip and knee trajectories of references for establishing the human model. A closed-loop control strategy with allocation factors is proposed featuring a two-layer cascaded proportional–integral–derivative (PID) controller for both FES and exoskeleton control. Experiments were performed on two participants to examine the feasibility of the hybrid exoskeleton and the closed-loop FES control. Both open- and closed-loop FES control showed the desired performance with a relatively low root-mean-squared error (max 1.3∘ in open-loop and max 4.1∘ in closed-loop) in hip and knee trajectories. Notably, the closed-loop FES control strategy can achieve the same performance with nearly 60% of the electrical power input compared to the open-loop control, which reduced muscle fatigue and improved robustness during the training. This study provides novel insights into body motion capture application and proposes a closed-loop FES control for hybrid exoskeletons.

Ultrasound Imaging-Based Closed-Loop Control of Functional Electrical Stimulation for Drop Foot Correction

Open-or closed-loop functional electrical stimulation (FES) has been widely investigated to treat drop foot syndrome, which is typically caused by weakness or paralysis of ankle dorsiflexors. However, conventional closed-loop FES control mainly uses kinematic feedback, which does not directly capture time-varying changes in muscle activation. In this study, we explored the use of ultrasound (US) echogenicity as an indicator of FES-evoked muscle activation and hypothesized that including US-derived muscle activation, in addition to kinematic feedback, would improve the closed-loop FES control performance compared to the closed-loop control that relies only on the kinematic feedback. A sampled-data observer (SDO) was derived to continuously estimate FES-evoked muscle activations from low-sampled US echogenicity signals. In addition, a dynamic surface controller (DSC) and a delay compensation (DC) term were incorporated with the SDO, denoted as the US-based DSC-DC, to drive the actual ankle dorsiflexion trajectory to the desired profile. The trajectory tracking error convergence of the closed-loop system was proven to be uniformly ultimately bounded based on the Lyapunov–Krasovskii stability analysis. The US-based DSC-DC controller was validated on five participants with no disabilities to control their ankle dorsiflexion during walking on a treadmill. The US-based DSC-DC controller significantly reduced the root-mean-square error of the ankle joint trajectory tracking by 46.52% <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\pm$</tex-math> </inline-formula> 7.99% ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p&lt;0.001$</tex-math> </inline-formula> ) compared to the traditional DSC-DC controller with only kinematic feedback but no US measurements. The results also verified the disturbance rejection performance of the US-based DSC-DC controller when a plantarflexion disturbance was added. Our control design, for the first time, provides a methodology to integrate US in an FES control framework, which will likely benefit persons with drop foot and those with other mobility disorders.

Effect of Functional Electrical Stimulation on Muscle Mass, Fatigue, and Quality of Life in Older Patients With COVID-19: A Randomized Clinical Trial Study

ObjectiveThis study aimed to evaluate the effects of functional electrical stimulation (FES) on muscle strength, fatigue, muscle mass, and quality of life (QoL) in older patients with COVID-19. MethodsOlder patients with COVID-19 were randomly divided into the following 2 groups: real FES (intervention group, n = 20) and sham FES (control group, n = 20). These patients received FES concurrent with the voluntary contraction of muscles for 10 consecutive sessions. Ultrasound imaging, pressure biofeedback, Chalder fatigue scale, and QoL were utilized to measure muscle mass, muscle strength, chronic fatigue, and QoL, respectively. Evaluations were performed at the beginning, immediately, and 1 month after the end of intervention. ResultsAll variables showed statistically significant improvement immediately and 1 month after the intervention in the real FES group (P < .05). However, the tibialis anterior muscle mass and fatigue significantly improved immediately after the intervention in the sham FES group. However, the tibialis anterior and rectus femoris muscles strength and rectus femoris muscle mass were not significantly changed immediately and 1 month after the intervention (P > .05). There were significant differences in muscle mass, physical fatigue, muscle strength, and QoL between groups with more efficacy of real FES (P < .05). ConclusionFor this sample of patients, FES improved fatigue, muscle strength, muscle mass, and QoL in older adults with COVID-19.

Continuous Switching Control of an Input-Delayed Antagonistic Muscle Pair During Functional Electrical Stimulation

Existing controllers for functional electrical stimulation (FES) of upper limb muscles were initially designed to assist unilateral movements and may not be readily applicable to assist antagonistic muscle movements. Furthermore, it is yet unclear if electromechanical delays (EMDs) are present during the coactivation of muscles. In this article, a robust controller is designed to facilitate the FES of an antagonistic muscle pair during elbow flexion and extension. The controller uses a continuous switching law that maps a joint angle error to control the antagonistic muscle pair. Furthermore, the controller compensates for EMDs in the antagonistic muscle pair. A Lyapunov stability analysis yields uniformly ultimately bounded (UUB) tracking for the human limb joint. The experimental results on four participants without disabilities indicate that the controller is robust and effective in switching between antagonistic muscles. A separate set of experiments also showed that EMDs are indeed present in the coactivated muscle pair. The designed controller compensates for the EMDs and statistically improves root mean square error (RMSE) compared to a traditional linear controller with no EMD compensation. The proposed controller can be generalized to assist FES-elicited tasks that involve a weak antagonistic muscle pair.

Robust Cadence and Power Tracking on a Switched FES Cycle With an Unknown Electromechanical Delay

Functional electrical stimulation (FES) is commonly used to facilitate cycling tasks for people with lower-limb movement disorders. In this work, FES and motor controllers are designed to track a desired power and cadence, respectively, and a Lyapunov-based switched systems analysis is performed to guarantee uniformly ultimately bounded power tracking and global exponential cadence tracking for a switched, delayed, nonlinear, and uncertain FES-cycling system. A unique challenge in this problem is that there is an unknown time-varying input delay to produce force, and a different unknown time-varying residual input delay where force is still produced after stimulation is removed. These delays impact the dwell-time conditions that dictate stimulation timing, and if not properly accounted for can lead to undesired effects such as antagonistic muscles exerting force at the same time or potential instabilities. The proposed controllers were validated by experimental analysis of four participants with neurological conditions (NCs) and five able-bodied participants, and yielded average power and cadence tracking errors of 0.01 ± 0.09 W and −0.05 ± 0.65 revolutions per minute (RPM), respectively, for the able-bodied participants and 0.01 ± 1.11 W and −0.07 ± 1.17 RPM, respectively, for the participants with NCs.

Multi-channel FES gait rehabilitation assistance system based on adaptive sEMG modulation.

Functional electrical stimulation (FES) can be used to stimulate the lower-limb muscles to provide walking assistance to stroke patients. However, the existing surface electromyography (sEMG)-based FES control methods mostly only consider a single muscle with a fixed stimulation intensity and frequency. This study proposes a multi-channel FES gait rehabilitation assistance system based on adaptive myoelectric modulation. The proposed system collects sEMG of the vastus lateralis muscle on the non-affected side to predict the sEMG values of four targeted lower-limb muscles on the affected side using a bidirectional long short-term memory (BILSTM) model. Next, the proposed system modulates the real-time FES output frequency for four targeted muscles based on the predicted sEMG values to provide muscle force compensation. Fifteen healthy subjects were recruited to participate in an offline model-building experiment conducted to evaluate the feasibility of the proposed BILSTM model in predicting the sEMG values. The experimental results showed that the R2 value of the best-obtained prediction result reached 0.85 using the BILSTM model, which was significantly higher than that using traditional prediction methods. Moreover, two patients after stroke were recruited in the online assisted-walking experiment to verify the effectiveness of the proposed walking-assistance system. The experimental results showed that the activation of the target muscles of the patients was higher after FES, and the gait movement data were significantly different before and after FES. The proposed system can be effectively applied to walking assistance for stroke patients, and the experimental results can provide new ideas and methods for sEMG-controlled FES rehabilitation applications.

Robust tracking for functional electrical stimulation cycling with unknown time-varying input delays: A switched systems approach.

Motorized functional electrical stimulation (FES) cycling has been demonstrated to have numerous health benefits for individuals suffering from neurological disorders. FES-cycling is usually designed to track the desired trajectories in real time. However, there are input delays between the exertion of the stimulation and the corresponding muscle contraction that potentially destabilize the system and undermine training efforts. Meanwhile, muscle fatigue gives rise to a time-varying input delay and decreased force. Moreover, switching between FES and motor control can be chattering and destabilizing owing to the high frequency. This article constructs Lyapunov-Krasovskii functionals to analyze the stability and robustness of the nonlinear cycling system with time-varying input delay. A new average dwell time condition is then provided to ensure the input-to-state stability of the considered systems. Finally, numerical simulations are illustrated to verify the effectiveness of the developed controller.

An adaptive reflexive control strategy for walking assistance system based on functional electrical stimulation.

Functional electrical stimulation (FES) neuroprostheses have been regarded as an effective approach for gait rehabilitation and assisting patients with stroke or spinal cord injuries. A multiple-channel FES system was developed to improve the assistance and restoration of lower limbs. However, most neuroprostheses need to be manually adjusted and cannot adapt to individual needs. This study aimed to integrate the purely reflexive FES controller with an iterative learning algorithm while a multiple-channel FES walking assistance system based on an adaptive reflexive control strategy has been established. A real-time gait phase detection system was developed for accurate gait phase detection and stimulation feedback. The reflexive controller generated stimulation sequences induced by the gait events. These stimulation sequences were updated for the next gait cycle through the difference between the current and previous five gait cycles. Ten healthy young adults were enrolled to validate the multiple-channel FES system by comparing participants' gait performance to those with no FES controller and purely reflexive controller. The results showed that the proposed adaptive FES controller enabled the adaption to generate fitted stimulation sequences for each participant during various treadmill walking speeds. The maximum, minimum, and range of motion (ROM) of the hip, knee, and ankle joints were furtherly improved for most participants, especially for the hip and knee flexion and ankle dorsiflexion compared with the purely reflexive FES control strategy. The presented system has the potential to enhance motor relearning and promote neural plasticity.

A Therapeutic Strategy for Lower Motor Neuron Disease and Injury Integrating Neural Stem Cell Transplantation and Functional Electrical Stimulation in a Rat Model.

Promising treatments for upper motor neuron disease are emerging in which motor function is restored by brain–computer interfaces and functional electrical stimulation. At present, such technologies and procedures are not applicable to lower motor neuron disease. We propose a novel therapeutic strategy for lower motor neuron disease and injury integrating neural stem cell transplantation with our new functional electrical stimulation control system. In a rat sciatic nerve transection model, we transplanted embryonic spinal neural stem cells into the distal stump of the peripheral nerve to reinnervate denervated muscle, and subsequently demonstrated that highly responsive limb movement similar to that of a healthy limb could be attained with a wirelessly powered two-channel neurostimulator that we developed. This unique technology, which can reinnervate and precisely move previously denervated muscles that were unresponsive to electrical stimulation, contributes to improving the condition of patients suffering from intractable diseases of paralysis and traumatic injury.

Raspberry Pi based Modular System for Multichannel Event-Driven Functional Electrical Stimulation Control.

This paper describes the implementation and testing of a modular software for multichannel control of Functional Electrical Stimulation (FES). Moving towards an embedded scenario, the core of the system is a Raspberry Pi, whose different models (with different computing powers) best suit two different system use-cases: user-supervised and stand-alone. Given the need for real-time and reliable FES applications, software processing timings were analyzed for multiple configurations, along with hardware resources utilization. Among the results, the simultaneous use of eight channels has been functionally achieved (0% lost packets) while minimizing system timing failures (excessive processing latency). Further investigations included stressing the system using more constraining acquisition parameters, eventually limiting the usable channels (only for the stand-alone use-case).

A Co-driven Functional Electrical Stimulation Control Strategy by Dynamic Surface Electromyography and Joint Angle.

Functional electrical stimulation (FES) is widely used in neurorehabilitation to improve patients’ motion ability. It has been verified to promote neural remodeling and relearning, during which FES has to produce an accurate movement to obtain a good efficacy. Therefore, many studies have focused on the relationship between FES parameters and the generated movements. However, most of the relationships have been established in static contractions, which leads to an unsatisfactory result when applied to dynamic conditions. Therefore, this study proposed a FES control strategy based on the surface electromyography (sEMG) and kinematic information during dynamic contractions. The pulse width (PW) of FES was determined by a direct transfer function (DTF) with sEMG features and joint angles as the input. The DTF was established by combing the polynomial transfer functions of sEMG and joint torque and the polynomial transfer functions of joint torque and FES. Moreover, the PW of two FES channels was set based on the muscle synergy ratio obtained through sEMG. A total of six healthy right-handed subjects were recruited in this experiment to verify the validity of the strategy. The PW of FES applied to the left arm was evaluated based on the sEMG of the right extensor carpi radialis (ECR) and the right wrist angle. The coefficient of determination (R2) and the normalized root mean square error (NRMSE) of FES-included and voluntary wrist angles and torques were used to verify the performance of the strategy. The result showed that this study achieved a high accuracy (R2 = 0.965 and NRMSE = 0.047) of joint angle and a good accuracy (R2 = 0.701 and NRMSE = 0.241) of joint torque reproduction during dynamic movements. Moreover, the DTF in real-time FES system also had a nice performance of joint angle fitting (R2 = 0.940 and NRMSE = 0.071) and joint torque fitting (R2 = 0.607 and NRMSE = 0.303). It is concluded that the proposed strategy is able to generate proper FES parameters based on sEMG and kinematic information for dynamic movement reproduction and can be used in a real-time FES system combined with bilateral movements for better rehabilitation.

Force Tracking Control of Functional Electrical Stimulation via Hybrid Active Disturbance Rejection Control

Stroke is a worldwide disease with a high incidence rate. After surviving a stroke, most patients are left with impaired upper or lower limb. Muscle force training is vital for stroke patients to recover limb function and improve their quality of life. This paper proposes a force tracking control method for upper limb based on functional electrical stimulation (FES), which is a promising rehabilitation approach. A modified Hammerstein model is proposed to describe the nonlinear dynamics of biceps brachii, which consists of a nonlinear mapping function, linear dynamics and time delay component to represent the biochemical process of muscle contraction. A quick model identification method is presented based on the least square algorithm. To deal with the variation of muscle dynamics, a hybrid active disturbance rejection control (ADRC) is proposed to estimate and compensate for the model uncertainty and unmeasured disturbances. The parameter tuning process is given. In the end, the performance of the proposed methods is verified via simulations and experiments. Compared with the Proportional integral derivative controller (PID) method, the proposed methods could suppress the model uncertainty and improve the tracking precision.

Position and cadence tracking of a motorized FES-cycle with an unknown time-varying input delay using saturated FES control

Neurological conditions (NCs) can have debilitating effects on an individual and can impede a person’s ability to perform activities of daily living. A common rehabilitation for individuals with NCs is the use of functional electrical stimulation (FES) to induce muscle contractions to perform a functional task, such as cycling. In this paper, we develop a closed-loop FES cycling controller that yields exponential cadence tracking. Contributions of the work result from the design and analysis innovations to compensate for switching between the different muscles and motor control inputs, compensating for the inherent uncertain nonlinear dynamics, compensating for the time-varying unknown input delay resulting from the complex electrochemical FES muscle torque production process, and compensating for saturation in the evoked torque due to fatigue, available muscle mass, or stimulation sensitivity. The performance of the developed control system was examined through a series of experiments on nine participants (five able-bodied and four with NCs). The experiments resulted in a cadence tracking error of −0.03 ± 1.69 RPM for non-informed passive able-bodied participants and −0.04 ± 1.98 RPM for the participants with NCs, for a desired cadence of 50 RPM.