Closed-loop Functional Electrical Stimulation Research Articles

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.

Resource-Efficient Neural Network Architectures for Classifying Nerve Cuff Recordings on Implantable Devices.

Closed-loop functional electrical stimulation can use recorded nerve signals to create implantable systems that make decisions regarding nerve stimulation in real-time. Previous work demonstrated convolutional neural network (CNN) discrimination of activity from different neural pathways recorded by a high-density multi-contact nerve cuff electrode, achieving state-of-the-art performance but requiring too much data storage and power for a practical implementation on surgically implanted hardware. To reduce resource utilization for an implantable implementation, with minimal performance loss for CNNs that can discriminate between neural pathways in multi-contact cuff electrode recordings. Neural networks (NNs) were evaluated using rat sciatic nerve recordings previously collected using 56-channel cuff electrodes to capture spatiotemporal neural activity patterns. NNs were trained to classify individual, natural compound action potentials (nCAPs) elicited by sensory stimuli. Three architectures were explored: the previously reported ESCAPE-NET, a fully convolutional network, and a recurrent neural network. Variations of each architecture were evaluated based on F1-score, number of weights, and floating-point operations (FLOPs). NNs were identified that, when compared to ESCAPE-NET, require 1,132-1,787x fewer weights, 389-995x less memory, and 6-11,073x fewer FLOPs, while maintaining macro F1-scores of 0.70-0.71 compared to a baseline of 0.75. Memory requirements range from 22.69 KB to 58.11 KB, falling within on-chip memory sizes from published deep learning accelerators fabricated in ASIC technology. Reduced versions of ESCAPE-NET require significantly fewer resources without significant accuracy loss, thus can be more easily incorporated into a surgically implantable device that performs closed-loop responsive neural stimulation.

Closed-loop Functional Electrical Stimulation (FES) – cycling rehabilitation with phase control Fuzzy Logic for fatigue reduction control strategies for stroke patients

Functional Electrical Stimulation (FES) cycling, or FES-Cycling, holds great therapeutic potential for individuals with paralysis, such as those with Spinal Cord Injury (SCI), traumatic brain injury, or stroke, aiming to restore mobility. However, the nonlinear nature of the musculoskeletal system poses a significant challenge in controlling FES-Cycling. To address this, an integrated closed-loop phase angle fuzzy-based system was developed. This system offers real-time control by adjusting stimulation intensity (pulse width) within the range of 50 to 200μs while maintaining a constant frequency of 35Hz, thereby ensuring precise pedaling trajectory and cadence patterns. An experimental study involved three healthy individuals (Cases A, B, and C) and one individual with hemiplegia stroke (Case D). Results showed that the proposed system consistently reduced average angle trajectory errors for Cases A, B, and C, with values of 2.6945, 3.2958, and 2.9922 degrees, respectively. Case D, affected by hemiplegia stroke, faced greater challenges and exhibited a higher error of 3.4562 degrees. Fatigue resistance, evaluated through fatigue indices, showed promising results for Cases A, B, and C with values of 0.10778, 0.06866, and 0.04603, respectively. However, Case D experienced higher fatigue (0.2304) due to the unique challenges of hemiplegia stroke. These findings highlight the effectiveness of the proposed control system in optimizing FES-Cycling, particularly for healthy individuals. For individuals with paralysis, like Case D, further research is needed to adapt the system to their specific conditions and cycling patterns. This system holds the potential for enhancing FES-Cycling as a therapeutic strategy and warrants additional investigation and customization for different patient populations.

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.

Adaptive Sliding Mode Feedback Control Algorithm for a Nonlinear Knee Extension Model

Functional electrical stimulation (FES) has been widely used to treat spinal cord injury (SCI) patients. Many research studies employ a closed-loop FES system to monitor the stimulated muscle response and provide a precise amount of charge to the muscle. However, most closed-loop FES devices perform poorly and sometimes fail when muscle nonlinearity effects such as fatigue, time delay response, stiffness, spasticity, and subject change happen. The poor performance of the closed-loop FES device is mainly due to discrepancies in the feedback control algorithms. Most of the existing feedback control algorithms were not designed to adapt to new changes in patients with different nonlinearity effects, resulting in early muscle fatigue. Therefore, this research proposes an adaptive sliding mode (SM) feedback control algorithm that could adapt and fine-tune internal control settings in real-time according to the current subject’s nonlinear and time-varying muscle response during the rehabilitation (knee extension) exercise. The adaptive SM feedback controller consists mainly of system identification, direct torque control, and tunable feedback control settings. Employing the system identification unit in the feedback control algorithm enables real-time self-tuning and adjusting of the SM internal control settings according to the current patient’s condition. Initially, the patient’s knee trajectory response was identified by extracting meaningful information, which included time delay, rise time, overshoot, and steady-state error. The extracted information was used to fine-tune and update the internal SM control settings. Finally, the performance of the proposed adaptive SM feedback control algorithm in terms of system response time, stability, and rehabilitation time was analysed using a nonlinear knee model. The findings from the simulation results indicate that the adaptive SM feedback controller demonstrated the best control performance in accurately tracking the desired knee angle movement by having faster response times, smaller overshoots, and lower steady-state errors when compared with the conventional SM across four reference angle settings (20°, 30°, 40°, and 76°). The adaptive feedback SM controller was also observed to compensate for muscle nonlinearities, including fatigue, stiffness, spasticity, time delay, and other disturbances.

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.

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.

Design of an FPGA-Based Fuzzy Feedback Controller for Closed-Loop FES in Knee Joint Model.

Functional electrical stimulation (FES) device has been widely used by spinal cord injury (SCI) patients in their rehab exercises to restore motor function to their paralysed muscles. The major challenge of muscle contraction induced by FES is early muscle fatigue due to the open-loop stimulation strategy. To reduce the early muscle fatigue phenomenon, a closed-loop FES system is proposed to track the angle of the limb’s movement and provide an accurate amount of charge according to the desired reference angle. Among the existing feedback controllers, fuzzy logic controller (FLC) has been found to exhibit good control performance in handling complex non-linear systems without developing any complex mathematical model. Recently, there has been considerable interest in the implementation of FLC in hardware embedded systems. Therefore, in this paper, a digital fuzzy feedback controller (FFC) embedded in a field-programmable gate array (FPGA) board was proposed. The digital FFC mainly consists of an analog-to-digital converter (ADC) Data Acquisition and FLC sub-modules. The FFC was designed to monitor and control the progress of knee extension movement by regulating the stimulus pulse width duration to meet the target angle. The knee is expected to extend to a maximum reference angle setting (70°, 40° or 30°) from its normal position of 0° once the stimulus charge is applied to the muscle by the FES device. Initially, the FLC was modelled using MATLAB Simulink. Then, the FLC was hardcoded into digital logic using hardware description language (HDL) Verilog codes. Thereafter, the performance of the digital FLC was tested with a knee extension model using the HDL co-simulation technique in MATLAB Simulink. Finally, for real-time verification, the designed digital FFC was downloaded to the Intel FPGA (DE2-115) board. The digital FFC utilized only 4% of the total FPGA (Cyclone IV E) logic elements (LEs) and required 238 µs to regulate stimulus pulse width data, including 3 µs for the FLC computation. The high processing speed of the digital FFC enables the stimulus pulse width duration to be updated every stimulation cycle. Furthermore, the implemented digital FFC has demonstrated good control performance in accurately controlling the stimulus pulse width duration to reach the desired reference angle with very small overshoot (1.4°) and steady-state error (0.4°). These promising results are very useful for a real-world closed-loop FES application.

FES Cycling and Closed-Loop Feedback Control for Rehabilitative Human–Robot Interaction

For individuals with movement impairments due to neurological injuries, rehabilitative therapies such as functional electrical stimulation (FES) and rehabilitation robots hold vast potential to improve their mobility and activities of daily living. Combining FES with rehabilitation robots results in intimately coordinated human–robot interaction. An example of such interaction is FES cycling, where motorized assistance can provide high-intensity and repetitive practice of coordinated limb motion, resulting in physiological and functional benefits. In this paper, the development of multiple FES cycling testbeds and safeguards is described, along with the switched nonlinear dynamics of the cycle–rider system. Closed-loop FES cycling control designs are described for cadence and torque tracking. For each tracking objective, the authors’ past work on robust and adaptive controllers used to compute muscle stimulation and motor current inputs is presented and discussed. Experimental results involving both able-bodied individuals and participants with neurological injuries are provided for each combination of controller and tracking objective. Trade-offs for the control algorithms are discussed based on the requirements for implementation, desired rehabilitation outcomes and resulting rider performance. Lastly, future works and the applicability of the developed methods to additional technologies including teleoperated robotics are outlined.

Robust Cadence Tracking for Switched FES-Cycling With an Unknown Time-Varying Input Delay

For an individual affected by a neuromuscular condition (NC), functional electrical stimulation (FES)-induced cycling provides a means of functional restoration and therapeutic exercise. Although FES-cycling has been shown to have numerous benefits, there are challenges to implementing closed-loop FES control for coordinated motion. For example, there exists a potentially destabilizing input delay between the application (or removal) of stimulation and the resulting muscle force. Moreover, switching between multiple actuators (such as FES or motor control) can also be destabilizing. This brief develops delay-dependent switching conditions and a robust control method to account for an unknown time-varying input delay of a switched system. A Lyapunov-like analysis is performed to yield semiglobal exponential cadence tracking to an ultimate bound. Experiments were performed on six able-bodied participants and four participants with NCs to validate the developed controller. The proposed controller resulted in an average cadence error of 0.01 ± 2.00 revolutions per minute (RPM) for the able-bodied participants and 0.01 ± 2.72 RPM for participants with NCs.

Shared control for switched motorized FES-cycling on a split-crank cycle accounting for muscle control input saturation

Closed-loop functional electrical stimulation (FES) control methods are developed to enable motorized assistive split-crank (i.e., a cycle without mechanical coupling between the lower limbs) cycling for rehabilitation efforts for people with lower limb movement disorders. The non-dominant side tracks a desired range of cadence and the dominant side tracks a range of position offsets centered around the position of the non-dominant side. A multi-level switched system with switched control objectives is applied to both sides of the cycle-rider system. Assistive, uncontrolled, and resistive modes for the dominant and non-dominant subsystems are based on position and cadence, respectively. Global exponential tracking to upper and lower bounds of an uncontrolled desired region is proven for each side via Lyapunov-based analysis using switched system methods. Experiments on both able-bodied participants and participants with neuromuscular conditions show the performance of the switched control system for split-crank FES-cycling. From volitional to controlled pedaling in able-bodied participants, average RMS cadence error of the non-dominant, RMS position error of the dominant, and cadence differential between the two legs improved by 76.2%, 65.3%, and 58.0%, respectively. On average, experiments on participants with neuromuscular conditions resulted in RMS errors that were 45.8%, 92.6%, and 52.0% higher than controlled trials on able-bodied participants, but 65.3%, 33.3%, and 36.3% lower than volitional-only trials of able-bodied participants.

Characterization of the Time-Varying Nature of Electromechanical Delay During FES-Cycling.

Functional electrical stimulation (FES) induced cycling is a common rehabilitative technique for people with neuromuscular disorders. A challenge for closed-loop FES control is that there exists a potentially destabilizing time-varying input delay, termed electromechanical delay (EMD), between the application of the electric field and the corresponding muscle contraction. In this article, the FES-induced torque production and EMD are quantified on an FES-cycle for the quadriceps femoris and gluteal muscle groups. Experiments were performed on five able-bodied individuals and five individuals with neurological conditions. Closed-loop FES-cycling was applied to induce fatigue and torque and EMD measurements were made during isometric conditions before and after each minute of cycling to quantify the effect of fatigue on EMD and torque production. A multiple linear regression and other descriptive statistics were performed to establish a range of expected EMD values and bounds on the rate of change of the EMD across a diverse population. The results from these experiments can be used to assist in the development of closed-loop controllers for FES-cycling that are robust to time-varying EMD and changes in torque production.

Voluntary and FES-Induced Finger Movement Estimation Using Muscle Deformation Features

It is practically crucial to estimate dexterous hand movement for applications such as prosthetics and rehabilitation, especially scenarios actively assisted with functional electrical stimulation (FES). This paper attempts to estimate hand finger movement through forearm muscle deformation for both clinical scenarios with voluntary and FES-induced muscle contractions based on A-mode ultrasound sensing approaches. Six healthy subjects participated in the experiment of middle finger movement. In order to evaluate the sensing approach, a least-squares support-vector-machine (LS-SVM) model and a linear-ridge (LR) model were developed to estimate the hand finger movement for datasets captured from the flexor digitorum superficialis (FDS) muscle belly, extensor digitorum muscle belly with or without FES. The average results of using FDS datasets as inputs revealed excellent evaluation performance ( $R^2>0.9$ and $\text{RMSE} ) both for LR and LS-SVM regression technologies. Furthermore, finger-movements-related muscle deformation features corresponding to voluntary and FES-induced contraction were proved to be different. These results demonstrated the potential application of the forearm muscle deformation in wearable A-mode ultrasound-based prosthesis finger movements control and optimizing the closed-loop FES system for motor function rehabilitation.

An Omnidirectional Assistive Platform Integrated With Functional Electrical Stimulation for Gait Rehabilitation: A Case Study.

This paper presents a novel omnidirectional platform for gait rehabilitation of people with hemiparesis after stroke. The mobile platform, henceforth the "walker", allows unobstructed pelvic motion during walking, helps the user maintain balance and prevents falls. The system aids mobility actively by combining three types of therapeutic intervention: forward propulsion of the pelvis, controlled body weight support, and functional electrical stimulation (FES) for compensation of deficits in angular motion of the joints. FES is controlled using gait data extracted from a set of inertial measurement units (IMUs) worn by the user. The resulting closed-loop FES system synchronizes stimulation with the gait cycle phases and automatically adapts to the variations in muscle activation caused by changes in residual muscle activity and spasticity. A pilot study was conducted to determine the potential outcomes of the different interventions. One chronic stroke survivor underwent five sessions of gait training, each one involving a total of 30 minutes using the walker and FES system. The patient initially exhibited severe anomalies in joint angle trajectories on both the paretic and the non-paretic side. With training, the patient showed progressive increase in cadence and self-selected gait speed, along with consistent decrease in double-support time. FES helped correct the paretic foot angle during swing phase, and likely was a factor in observed improvements in temporal gait symmetry. Although the experiments showed favorable changes in the paretic trajectories, they also highlighted the need for intervention on the non-paretic side.

Reference Trajectory Generation for Closed-Loop Control of Electrical Stimulation for Rehabilitation of Upper Limb

Functional movements in the paralyzed upper limb can be restored with the help of brain-computer-interface (BCI). A BCI system typically adopts a functional electrical stimulation (FES) system that activates weakened muscles that are otherwise responsible for actuating finger movements. A BCI-FES system can enable muscle contraction through the delivery of electrical stimulation pulses. The control of voltage or current stimulation parameters such as pulse width, frequency, and amplitude along with feedback signals from finger joints positions are essential for stable grasping. For the design of a closed-loop functional electrical stimulation controller, it is obligatory to set standard reference trajectories of finger joints’ angular positions and velocities for controlling stimulation parameters in neuroprosthetics and rehabilitation. This study proposes a new closed-loop control architecture targeted for achieving successful and stable grasping of an upper limb paralyzed subject. This can be achieved by characterizing each of the finger joints’ instantaneous angular position and velocity, through reference trajectories. These reference trajectories are generated corresponding to various types of grasping for feeding to the controller, responsible for stimulation of muscles. Hence, to generate such trajectories, first, grasping classification has been implemented using standard machine learning algorithms on a large set of existing real-time data of different types of objects’ grasping such as various diameter, abducted thumb and other types of objects, from many healthy subjects. The results demonstrate the successful implementation of fairly accurate classifications and trajectory generations which are crucial for closed-loop control towards stable grasping.

A Method for Suppressing Electrical Stimulation Artifacts from Electromyography.

When surface electromyography (EMG) signal is used in a real-time functional electrical stimulation (FES) system for feedback control, the artifact from electrical stimulation is a key challenge for EMG signal processing. To address this challenge, this study proposes a novel method to suppress stimulation artifacts in the EMG-driven closed-loop FES system. The proposed method is inspired by an experimental study that compares artifacts generated by electrical stimulations with different current intensities. It is found that (1) spikes of stimulation artifacts are susceptible to the current intensity and (2) tailing components are similar under different current intensities. Based on these observations, the proposed method combines the blanking and template subtracting strategies for suppressing stimulation artifact. The length of blanking window for suppressing the stimulation spike is adaptively determined by a spike detection algorithm and the first-order derivative analysis of signal. An autoregressive model is used to estimate the tailing part of stimulation artifact, which is an adaptive template for subtracting the artifact. The proposed method is evaluated on both semi-synthetic and experimental datasets. Verified on the semi-synthetic dataset, the proposed method achieves better performance than the classic blanking method. Validated on the experimental dataset, the proposed method substantially decreases the power of stimulation artifact in the EMG. These results indicate that the proposed method can effectively suppress the stimulation artifact while retains the useful EMG signal for an EMG-driven FES system.

Development of a Closed-Loop Stimulator for Laryngeal Reanimation: Part 2. Device Testing in the Canine Model of Laryngeal Paralysis.

Laryngeal paralysis of central or peripheral origin can potentially be treated using functional electrical stimulation (FES) of laryngeal muscles. Experiments in canines (dogs) were performed using implant prototypes capable of closed-loop FES to refine engineering designs and specifications, test surgical approaches for implantation, and better understand the in vivo effects of laryngeal muscle stimulation on short- and long-term glottic function. Prospective, laboratory. We designed and tested a series of microprocessor-based implantable devices that can stimulate glottic opening or closing based on input from physiological control signals (real-time processing of electromyographic [EMG] signals). After acute device testing experiments, 2 dogs were implanted for 8 and 24 months, with periodic testing of closed-loop laryngeal muscle stimulation triggered from EMG signals. In total, 5 dogs were tested for the effects of laryngeal muscle stimulation on vocal fold (VF) posturing in larynges with nerve supplies that were intact (7 VFs), synkinetically reinnervated (2 VFs), or chronically denervated (1 VF). In 3 cases, the stimulation was combined with airflow-driven phonation to study the consequent modulation of phonatory parameters. Initial device prototypes used inductive coupling for power and communication, while later iterations used battery power and infrared light communication (detailed descriptions are provided in the Part 1 companion paper). Two animals were successfully implanted with the inductively powered units, which operated until removed at 8 months in 1 animal or for more than 16 months in the second animal. Surgically, the encapsulated implants were well tolerated, and procedures for placing, attaching, and connecting the devices were developed. To simulate EMG control signals in anesthetized animals, we created 2 types of nerve/muscle signal sources. In one approach, a neck muscle had a cuff electrode placed on its motor nerve that was connected to transdermal electrical connection ports for periodic testing. In the second approach, the recurrent laryngeal nerve on one side of the larynx was stimulated to generate a VF EMG signal, which was then used to trigger FES of the paralyzed contralateral side (eg, restoring VF movement symmetry). Implant testing identified effective stimulation parameters and closed-loop stimulation artifact rejection techniques for FES of both healthy and paralyzed VFs. Stimulation levels effective for VF adduction did not cause signs of discomfort during awake testing. Our inductive and battery-powered prototypes performed effectively during in vivo testing, and the 2 units that were implanted for long-term evaluation held up well. As a proof of concept, we demonstrated that elicited neck strap muscle or laryngeal EMG potentials could be used as a control signal for closed-loop stimulation of laryngeal adduction and vocal pitch modulation, depending on electrode positioning, and that VFs were stimulable in the presence of synkinetic reinnervation or chronic denervation.

Closed-loop functional optogenetic stimulation

Optogenetics has been used to orchestrate temporal- and tissue-specific control of neural tissues and offers a wealth of unique advantages for neuromuscular control. Here, we establish a closed-loop functional optogenetic stimulation (CL-FOS) system to control ankle joint position in murine models. Using the measurement of either joint angle or fascicle length as a feedback signal, we compare the controllability of CL-FOS to closed-loop functional electrical stimulation (CL-FES) and demonstrate significantly greater accuracy, lower rise times and lower overshoot percentages. We demonstrate orderly recruitment of motor units and reduced fatigue when performing cyclical movements with CL-FOS compared with CL-FES. We develop and investigate a 3-phase, photo-kinetic model to elucidate the underlying mechanisms for temporal variations in optogenetically activated neuromusculature during closed-loop control experiments. Methods and insights from this study lay the groundwork for the development of closed-loop optogenetic neuromuscular stimulation therapies and devices for peripheral limb control.

SEMG Bias-Driven Functional Electrical Stimulation System for Upper-Limb Stroke Rehabilitation

It is evident that the dominant therapy of functional electrical stimulation (FES) for stroke rehabilitation suffers from heavy dependency on therapists experience and lack of feedback from patients’ status, which decrease the patients’ voluntary participation, reducing the rehabilitation efficacy. This paper proposes a closed loop FES system using surface electromyography (sEMG) bias feedback from bilateral arms for enhancing upper-limb stroke rehabilitation. This wireless portable system consists of sEMG data acquisition and FES modules, the former is used to measure and analyze the subject’s bilateral arm motion intention and neuromuscular states in terms of their sEMG, the latter of multi-channel FES output is controlled via the sEMG bias of the bilateral arms. The system has been evaluated with experiments proving that the system can achieve 39.9 dB signal-to-noise ratio in the lab environment, outperforming existing similar systems. The results also show that voluntary and active participation can be effectively employed to achieve different FES intensity for FES-assisted hand motions, demonstrating the potential for active stroke rehabilitation.

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.