The Time-Varying Nature of Electromechanical Delay and Muscle Control Effectiveness in Response to Stimulation-Induced Fatigue

Hybrid Robotic and Electrical Stimulation Assistance Can Enhance Performance and Reduce Mental Demand

Combining functional electrical stimulation (FES) and robotics may enhance recovery after stroke, by providing neural feedback with the former while improving quality of motion and minimizing muscular fatigue with the latter. Here, we explored whether and how FES, robot assistance and their combination, affect users' performance, effort, fatigue and user experience. 15 healthy participants performed a wrist flexion/extension tracking task with FES and/or robotic assistance. Tracking performance improved during the hybrid FES-robot and the robot-only assistance conditions in comparison to no assistance, but no improvement is observed when only FES is used. Fatigue, muscular and voluntary effort are estimated from electromyographic recording. Total muscle contraction and volitional activity are lowest with robotic assistance, whereas fatigue level do not change between the conditions. The NASA-Task Load Index answers indicate that participants found the task less mentally demanding during the hybrid and robot conditions than the FES condition. The addition of robotic assistance to FES training might thus facilitate an increased user engagement compared to robot training and allow longer motor training session than with FES assistance.

ICEP: An Instrumented Cycling Ergometer Platform for the Assessment of Advanced FES Strategies

BACKGROUND

Functional electrical stimulation (FES) cycling has seen an upsurge in interest over the last decade. The present study describes the novel instrumented cycling ergometer platform designed to assess the efficiency of electrical stimulation strategies. The capabilities of the platform are showcased in an example determining the adequate stimulation patterns for reproducing a cycling movement of the paralyzed legs of a spinal cord injury (SCI) subject.

METHODS

Two procedures have been followed to determine the stimulation patterns: (1) using the EMG recordings of the able-bodied subject; (2) using the recordings of the forces produced by the SCI subject's stimulated muscles.

RESULTS

the stimulation pattern derived from the SCI subject's force output was found to produce 14% more power than the EMG-derived stimulation pattern.

CONCLUSIONS

the cycling platform proved useful for determining and assessing stimulation patterns, and it can be used to further investigate advanced stimulation strategies.

Natural grasping movement recognition and force estimation using electromyography

Electromyography (EMG) generated by human hand movements is usually used to decode different action types with high accuracy. However, the classifications of the gestures rarely consider the impact of force, and the estimation of the grasp force when performing natural grasping movements is so far overlooked. Decoding natural grasping movements and estimating the force generated by the associated movements can help patients to improve the accuracy of prosthesis control. This study mainly focused on two aspects: the classification of four natural grasping movements and the force estimation of these actions. For this purpose, we designed an experimental platform where subjects could perform four common natural grasping movements in daily life, including pinch, palmar, twist, and plug grasp, to complete target profiles. On the one hand, the results showed that, for natural grasping movements with different levels of force (three levels at 20, 50, and 80%), the average accuracy could reach from 91.43 to 97.33% under five classification schemes. On the other hand, the feasibility of force estimation for natural grasping movements was demonstrated. Furthermore, in the process of force estimation, we confirmed that the regression performance about plug grasp was the best, and the average R2 could reach 0.9082. Besides, we found that the regression results were affected by the speed of force application. These findings contribute to the natural control of myoelectric prosthesis and the EMG-based rehabilitation training system, improving the user’s experience and acceptance.

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.

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.

Motion Analysis for Experimental Evaluation of an Event-Driven FES System

In this work, a system for controlling Functional Electrical Stimulation (FES) has been experimentally evaluated. The peculiarity of the system is to use an event-driven approach to modulate stimulation intensity, instead of the typical feature extraction of surface ElectroMyoGraphic (sEMG) signal. To validate our methodology, the system capability to control FES was tested on a population of 17 subjects, reproducing 6 different movements. Limbs trajectories were acquired using a gold standard motion tracking tool. The implemented segmentation algorithm has been detailed, together with the designed experimental protocol. A motion analysis was performed through a multi-parametric evaluation, including the extraction of features such as the trajectory area and the movement velocity. The obtained results show a median cross-correlation coefficient of 0.910 and a median delay of 800 ms, between each couple of voluntary and stimulated exercise, making our system comparable w.r.t. state-of-the-art works. Furthermore, a 97.39% successful rate on movement replication demonstrates the feasibility of the system for rehabilitation purposes.

Closed-Loop Torque and Kinematic Control of a Hybrid Lower-Limb Exoskeleton for Treadmill Walking

Restoring and improving the ability to walk is a top priority for individuals with movement impairments due to neurological injuries. Powered exoskeletons coupled with functional electrical stimulation (FES), called hybrid exoskeletons, exploit the benefits of activating muscles and robotic assistance for locomotion. In this paper, a cable-driven lower-limb exoskeleton is integrated with FES for treadmill walking at a constant speed. A nonlinear robust controller is used to activate the quadriceps and hamstrings muscle groups via FES to achieve kinematic tracking about the knee joint. Moreover, electric motors adjust the knee joint stiffness throughout the gait cycle using an integral torque feedback controller. For the hip joint, a robust sliding-mode controller is developed to achieve kinematic tracking using electric motors. The human-exoskeleton dynamic model is derived using Lagrangian dynamics and incorporates phase-dependent switching to capture the effects of transitioning from the stance to the swing phase, and vice versa. Moreover, low-level control input switching is used to activate individual muscles and motors to achieve flexion and extension about the hip and knee joints. A Lyapunov-based stability analysis is developed to ensure exponential tracking of the kinematic and torque closed-loop error systems, while guaranteeing that the control input signals remain bounded. The developed controllers were tested in real-time walking experiments on a treadmill in three able-bodied individuals at two gait speeds. The experimental results demonstrate the feasibility of coupling a cable-driven exoskeleton with FES for treadmill walking using a switching-based control strategy and exploiting both kinematic and force feedback.

Association Between Neuromuscular Variables and Graft Harvest in Soft Tissue Quadriceps Tendon Versus Bone-Patellar Tendon-Bone Anterior Cruciate Ligament Autografts

Background:

Quadriceps tendon (QT) autografts are increasingly popular for anterior cruciate ligament reconstruction (ACLR). However, no study has compared QT autografts with bone–patellar tendon–bone (BTB) autografts regarding the electromechanical delay (EMD), the peak torque (PT), and the rate of force development (RFD) in the superficial quadriceps muscles (rectus femoris [RF], vastus medialis [VM], and vastus lateralis [VL]).

Hypotheses:

We hypothesized (1) there would be a significantly lower PT, lower RFD, and longer quadriceps EMD of the operative limb for the QT versus the BTB autograft; (2) the PT, the RFD, and the quadriceps EMD of the operative limb would be significantly depressed compared with those of the nonoperative limb, regardless of the surgical technique; and (3) there would be greater increases in the RF EMD than in the VM or the VL EMD.

Study Design:

Cohort study; Level of evidence, 3.

Methods:

A total of 34 patients (age, 18-40 years), who had undergone ACLR (QT, n = 17; BTB, n = 17) at least 1 year before testing and performed 3 perceived maximal effort isometric tests, which were time synchronized with surface electromyography (EMG) on their operative and nonoperative limbs, were included in this study. EMD, PT, and RFD data were analyzed using a 2 (limb) × 2 (graft) × 3 (repetition) mixed repeated-measures analysis of variance.

Results:

The EMD, the PT, and the RFD were not significantly affected by graft choice. For the VL, a significant repetition × graft × limb interaction was detected for the VL EMD (P = .027; ηp = 0.075), with repetition 3 having longer EMD than repetition 2 (mean difference [MD], 16 milliseconds; P = .039). For the RF EMD, there was a significant repetition × limb interaction (P = .027; ηp = 0.074), with repetition 3 being significantly longer on the operative versus the nonoperative limb (MD, 24 milliseconds; P = .004). Further, the operative limb EMD was significantly longer for repetition 3 versus repetition 2 (MD, 17 milliseconds; P = .042). For the PT, there was a significant effect for repetition (P = .003; ηp = 0.114), with repetition 1 being significantly higher than both repetitions 2 (MD, 8.52 N·m; P = .001) and 3 (MD, 7.79 N·m; P = .031). For the RFD, significant limb (P = .034; ηp = 0.092) and repetition (P = .010; ηp = 0.093) effects were seen, with the nonoperative limb being significantly faster than the operative limb (MD, 23.7 N·m/s; P = .034) and repetition 1 being significantly slower than repetitions 2 (MD, -20.46 N·m/s; P = .039) or 3 (MD, −29.85 N·m/s; P = .002).

Conclusion:

The EMD, the PT, and the RFD were not significantly affected by graft type when comparing QT and BTB autografts for ACLR; however, all neuromuscular variables were affected regardless of the QT or the BTB harvest.

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.

Electromechanical delay during functional electrical stimulation induced cycling is a function of lower limb position

INTRODUCTION

Functional electrical stimulation (FES) induced cycling has been shown to be an effective rehabilitation for those with lower limb movement disorders. However, a consequence of FES is an electromechanical delay (EMD) existing between the stimulation input and the onset of muscle force. The objective of this study is to determine if the cycle crank angle has an effect on the EMD.

METHODS

Experiments were performed on 10 participants, five healthy and five with neurological conditions resulting in movement disorders. A motor fixed the crank arm of a FES-cycle in 10° increments and at each angle stimulation was applied in a random sequence to a combination of the quadriceps femoris and gluteal muscle groups. The EMD was examined by considering the contraction delay (CD) and the residual delay (RD), where the CD (RD) is the time latency between the start (end) of stimulation and the onset (cessation) of torque. Two different measurements were used to examine the CD and RD. Further, two multiple linear regressions were performed on each measurement, one for the left and one for the right muscle groups.

RESULTS

The crank angle was determined to be statistically relevant for both the CD and RD.

CONCLUSIONS

Since the crank angle has a significant effect on both the CD and RD, the angle has a significant effect on the EMD. Therefore, future efforts should consider the importance of the crank angle when modelling or estimating the EMD to improve control designs and ultimately improve rehabilitative treatments.Implications for rehabilitationNew model predicts the delayed response of muscle torque production to electrical stimulation as a function of limb position during FES cycling.The model can inform closed-loop electrical stimulation induced rehabilitative cycling.

Modeling Ankle Torque and Stiffness Induced by Functional Electrical Stimulation

Functional electrical stimulation (FES) is commonly used for individuals with neuromuscular impairments to generate muscle contractions. Both joint torque and stiffness play important roles in maintaining stable posture and resisting external disturbance. However, most previous studies only focused on the modulation of joint torque using FES while ignoring the joint stiffness. A model that can simultaneously modulate both ankle torque and stiffness induced by FES was investigated in this study. This model was composed of four subparts including an FES-to-activation model, a musculoskeletal geometry model, a Hill-based muscle-tendon model, and a joint stiffness model. The model was calibrated by the maximum voluntary contraction test of the tibialis anterior (TA) and gastrocnemius medial (GAS) muscles. To validate the model, the estimated torque and stiffness by the model were compared with the measured torque and stiffness induced by FES, respectively. The results showed that the proposed model can estimate torque and stiffness with electrically stimulated TA or/and GAS, which was significantly correlated to the measured torque and stiffness. The proposed model can modulate both joint torque and stiffness induced by FES in the isometric condition, which can be potentially extended to modulate the joint torque and stiffness during FES-assisted walking.

An EMG-Driven Musculoskeletal Model for Estimating Continuous Wrist Motion

EMG-based continuous wrist joint motion estimation has been identified as a promising technique with huge potential in assistive robots. Conventional data-driven model-free methods tend to establish the relationship between the EMG signal and wrist motion using machine learning or deep learning techniques, but cannot interpret the functional relationship between neuro-commands and relevant joint motion. In this paper, an EMG-driven musculoskeletal model is proposed to estimate continuous wrist joint motion. This model interprets the muscle activation levels from EMG signals. A muscle-tendon model is developed to compute the muscle force during the voluntary flexion/extension movement, and a joint kinematic model is established to estimate the continuous wrist motion. To optimize the subject-specific physiological parameters, a genetic algorithm is designed to minimize the differences of joint motion prediction from the musculoskeletal model and joint motion measurement using motion data during training. Results show that mean root-mean-square-errors are 10.08°, 10.33°, 13.22° and 17.59° for single flexion/extension, continuous cycle and random motion trials, respectively. The mean coefficient of determination is over 0.9 for all the motion trials. The proposed EMG-driven model provides an accurate tracking performance based on user's intention.

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.

A Tool to Select FES Parameters for chronic SCI

Functional electrical stimulation has been used in rehabilitation programs for patients with chronic spinal cord injury. When used correctly it is able to improve the well-being of patients. However, when the stimulus is not adequate it can accelerate the process of fatigue, reducing the time available for training the programmed motor activity. To optimize the configuration of the stimulatory parameters, we developed a tool capable of simulating the muscle strength performance in response to different stimulatory profiles. The tool was able to reproduce the behavior of motoneurons in chronic spinal cord injury and to estimate the muscular strength resulting from the application of different stimuli. We consider that this FES Simulator is a promising tool to design and simulate different profiles of electrical stimulation, optimizing the decision process of the stimulation parameters.

Lactate production without hypoxia in skeletal muscle during electrical cycling: Crossover study of femoral venous-arterial differences in healthy volunteers

BACKGROUND

Aim of the study was to compare metabolic response of leg skeletal muscle during functional electrical stimulation-driven unloaded cycling (FES) to that seen during volitional supine cycling.

METHODS

Fourteen healthy volunteers were exposed in random order to supine cycling, either volitional (10-25-50 W, 10 min) or FES assisted (unloaded, 10 min) in a crossover design. Whole body and leg muscle metabolism were assessed by indirect calorimetry with concomitant repeated measurements of femoral venous-arterial differences of blood gases, glucose, lactate and amino acids.

RESULTS

Unloaded FES cycling, but not volitional exercise, led to a significant increase in across-leg lactate production (from -1.1±2.1 to 5.5±7.4 mmol/min, p<0.001) and mild elevation of arterial lactate (from 1.8±0.7 to 2.5±0.8 mM). This occurred without widening of across-leg veno-arterial (VA) O2 and CO2 gaps. Femoral SvO2 difference was directly proportional to VA difference of lactate (R2 = 0.60, p = 0.002). Across-leg glucose uptake did not change with either type of exercise. Systemic oxygen consumption increased with FES cycling to similarly to 25W volitional exercise (138±29% resp. 124±23% of baseline). There was a net uptake of branched-chain amino acids and net release of Alanine from skeletal muscle, which were unaltered by either type of exercise.

CONCLUSIONS

Unloaded FES cycling, but not volitional exercise causes significant lactate production without hypoxia in skeletal muscle. This phenomenon can be significant in vulnerable patients' groups.

Evaluating Muscle Activation Models for Elbow Motion Estimation

Adoption of wearable assistive technologies relies heavily on improvement of existing control system models. Knowing which models to use and how to improve them is difficult to determine due to the number of proposed solutions with relatively little broad comparisons. One type of these models, muscle activation models, describes the nonlinear relationship between neural inputs and mechanical activation of the muscle. Many muscle activation models can be found in the literature, but no comparison is available to guide the community on limitations and improvements. In this research, an EMG-driven elbow motion model is developed for the purpose of evaluating muscle activation models. Seven muscle activation models are used in an optimization procedure to determine which model has the best performance. Root mean square errors in muscle torque estimation range from 1.67–2.19 Nm on average over varying input trajectories. The computational resource demand was also measured during the optimization procedure, as it is an important aspect for determining if a model is feasible for use in a particular wearable assistive device. This study provides insight into the ability of these models to estimate elbow motion and the trade-off between estimation accuracy and computational demand.

A Modified Dynamic Surface Controller for Delayed Neuromuscular Electrical Stimulation

A widely accepted model of muscle force generation during neuromuscular electrical stimulation (NMES) is a second-order nonlinear musculoskeletal dynamics cascaded to a delayed first-order muscle activation dynamics. However, most nonlinear NMES control methods have either neglected the muscle activation dynamics or used an ad hoc strategies to tackle the muscle activation dynamics, which may not guarantee control stability. We hypothesized that a nonlinear control design that includes muscle activation dynamics can improve the control performance. In this paper, a dynamic surface control (DSC) approach was used to design a PID-based NMES controller that compensates for EMD in the activation dynamics. Because the muscle activation is unmeasurable, a model based estimator was used to estimate the muscle activation in realtime. The Lyapunov stability analysis confirmed that the newly developed controller achieves semi-global uniformly ultimately bounded (SGUUB) tracking for the musculoskeletal system. Experiments were performed on two able-bodied subjects and one spinal cord injury subject using a modified leg extension machine. These experiments illustrate the performance of the new controller and compare it to a previous PID-DC controller that did not consider muscle activation dynamics in the control design. These experiments support our hypothesis that a control design that includes muscle activation improves the NMES control performance.