Effect of Obesity on Masticatory Muscle Activity and Rhythmic Jaw Movements Evoked by Electrical Stimulation of Different Cortical Masticatory Areas

This study investigates rhythmic jaw movement (RJM) patterns and masticatory muscle activities during electrical stimulation in two cortical masticatory areas in obese male Zucker rats (OZRs), compared to their counterparts-lean male Zucker rats (LZRs) (seven each). At the age of 10 weeks, electromyographic (EMG) activity of the right anterior digastric muscle (RAD) and masseter muscles, and RJMs were recorded during repetitive intracortical micro-stimulation in the left anterior and posterior parts of the cortical masticatory area (A-area and P-area, respectively). Only P-area-elicited RJMs, which showed a more lateral shift and slower jaw-opening pattern than A-area-elicited RJMs, were affected by obesity. During P-area stimulation, the jaw-opening duration was significantly shorter (p < 0.01) in OZRs (24.3 ms) than LZRs (27.9 ms), the jaw-opening speed was significantly faster (p < 0.05) in OZRs (67.5 mm/s) than LZRs (50.8 mm/s), and the RAD EMG duration was significantly shorter (p < 0.01) in OZRs (5.2 ms) than LZR (6.9 ms). The two groups had no significant difference in the EMG peak-to-peak amplitude and EMG frequency parameters. This study shows that obesity affects the coordinated movement of masticatory components during cortical stimulation. While other factors may be involved, functional change in digastric muscle is partly involved in the mechanism.

Hybrid Human-Machine Interface for Gait Decoding Through Bayesian Fusion of EEG and EMG Classifiers

Despite the advances in the field of brain computer interfaces (BCI), the use of the sole electroencephalography (EEG) signal to control walking rehabilitation devices is currently not viable in clinical settings, due to its unreliability. Hybrid interfaces (hHMIs) represent a very recent solution to enhance the performance of single-signal approaches. These are classification approaches that combine multiple human-machine interfaces, normally including at least one BCI with other biosignals, such as the electromyography (EMG). However, their use for the decoding of gait activity is still limited. In this work, we propose and evaluate a hybrid human-machine interface (hHMI) to decode walking phases of both legs from the Bayesian fusion of EEG and EMG signals. The proposed hHMI significantly outperforms its single-signal counterparts, by providing high and stable performance even when the reliability of the muscular activity is compromised temporarily (e.g., fatigue) or permanently (e.g., weakness). Indeed, the hybrid approach shows a smooth degradation of classification performance after temporary EMG alteration, with more than 75% of accuracy at 30% of EMG amplitude, with respect to the EMG classifier whose performance decreases below 60% of accuracy. Moreover, the fusion of EEG and EMG information helps keeping a stable recognition rate of each gait phase of more than 80% independently on the permanent level of EMG degradation. From our study and findings from the literature, we suggest that the use of hybrid interfaces may be the key to enhance the usability of technologies restoring or assisting the locomotion on a wider population of patients in clinical applications and outside the laboratory environment.

Electrically evoked wrist extensor muscle fatigue throughout repetitive motion as measured by mechanomyography and near-infrared spectroscopy

Repetitive electrically-evoked muscle contraction leads to accelerated muscle fatigue. This study assessed electrically-evoked fatiguing muscle with changes to mechanomyography root mean square percentage (%RMS-MMG) and tissue saturation index (%TSI) in extensor carpi radialis. Forty healthy volunteers (n=40) performed repetitive electrical-evoked wrist extension to fatigue and results were analyzed pre- and post-fatigue, i.e. 50% power output (%PO) drop. Responses of %PO, %TSI and %RMS-MMG were correlated while the relationships between %RMS-MMG and %TSI were investigated using linear regression. The %TSI for both groups were negatively correlated with declining %PO as the ability of the muscle to take up oxygen became limited due to fatigued muscle. The %RMS-MMG behaved in two different patterns post-fatigue against declining %PO whereby; (i) group A showed positive correlation (%RMS-MMG decreased) throughout the session and (ii) group B demonstrated negative correlation (%RMS-MMG increased) with declining %PO until the end of the session. Regression analysis showed %TSI was inversely proportional to %RMS-MMG during post-fatigue in group A. Small gradients in both groups suggested that %TSI was not sensitive to the changes in %RMS-MMG and they were mutually exclusive. Most correlation and regression changed significantly post-fatigue indicating that after fatigue, the condition of muscle had changed mechanically and physiologically.

Quantitative Assessment of Changes in Muscle Contractility Due to Fatigue During NMES: An Ultrasound Imaging Approach

OBJECTIVE

This paper investigates an ultrasound imaging-based non-invasive methodology to quantitatively assess changes in muscle contractility due to the fatigue induced by neuromuscular electrical stimulation (NMES).

METHODS

Knee extension experiments on human participants were conducted to record synchronized isometric knee force data and ultrasound images of the electrically stimulated quadriceps muscle. The data were first collected in a pre-fatigue stage and then in a post-fatigue stage. Ultrasound images were processed using a contraction rate adaptive speckle tracking algorithm. A two-dimensional strain measure field was constructed based on the muscle displacement tracking results to quantify muscle contractility.

RESULTS

Analysis of the strain images showed that, between the pre-fatigue and post-fatigue stages, there was a reduction in the strain peaks, a change in the strain peak distribution, and a decrease in an area occupied by the large positive strain.

CONCLUSION

The results indicate changes in muscle contractility due to the NMES-induced muscle fatigue.

SIGNIFICANCE

Ultrasound imaging with the proposed methodology is a promising tool for a direct NMES-induced fatigue assessment and facilitates new strategies to alleviate the effects of the NMES-induced fatigue.

Mechanomyography responses characterize altered muscle function during electrical stimulation-evoked cycling in individuals with spinal cord injury

BACKGROUND

Investigation of muscle fatigue during functional electrical stimulation (FES)-evoked exercise in individuals with spinal cord injury using dynamometry has limited capability to characterize the fatigue state of individual muscles. Mechanomyography has the potential to represent the state of muscle function at the muscle level. This study sought to investigate surface mechanomyographic responses evoked from quadriceps muscles during FES-cycling, and to quantify its changes between pre- and post-fatiguing conditions in individuals with spinal cord injury.

METHODS

Six individuals with chronic motor-complete spinal cord injury performed 30-min of sustained FES-leg cycling exercise on two days to induce muscle fatigue. Each participant performed maximum FES-evoked isometric knee extensions before and after the 30-min cycling to determine pre- and post- extension peak torque concomitant with mechanomyography changes.

FINDINGS

Similar to extension peak torque, normalized root mean squared (RMS) and mean power frequency (MPF) of the mechanomyography signal significantly differed in muscle activities between pre- and post-FES-cycling for each quadriceps muscle (extension peak torque up to 69%; RMS up to 80%, and MPF up to 19%). Mechanomyographic-RMS showed significant reduction during cycling with acceptable between-days consistency (intra-class correlation coefficients, ICC = 0.51-0.91). The normalized MPF showed a weak association with FES-cycling duration (ICC = 0.08-0.23). During FES-cycling, the mechanomyographic-RMS revealed greater fatigue rate for rectus femoris and greater fatigue resistance for vastus medialis in spinal cord injured individuals.

INTERPRETATION

Mechanomyographic-RMS may be a useful tool for examining real time muscle function of specific muscles during FES-evoked cycling in individuals with spinal cord injury.

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.

Mechanomyography and Torque during FES-Evoked Muscle Contractions to Fatigue in Individuals with Spinal Cord Injury

A mechanomyography muscle contraction (MC) sensor, affixed to the skin surface, was used to quantify muscle tension during repetitive functional electrical stimulation (FES)-evoked isometric rectus femoris contractions to fatigue in individuals with spinal cord injury (SCI). Nine persons with motor complete SCI were seated on a commercial muscle dynamometer that quantified peak torque and average torque outputs, while measurements from the MC sensor were simultaneously recorded. MC-sensor-predicted measures of dynamometer torques, including the signal peak (SP) and signal average (SA), were highly associated with isometric knee extension peak torque (SP: r = 0.91, p < 0.0001), and average torque (SA: r = 0.89, p < 0.0001), respectively. Bland-Altman (BA) analyses with Lin's concordance (ρC) revealed good association between MC-sensor-predicted peak muscle torques (SP; ρC = 0.91) and average muscle torques (SA; ρC = 0.89) with the equivalent dynamometer measures, over a range of FES current amplitudes. The relationship of dynamometer torques and predicted MC torques during repetitive FES-evoked muscle contraction to fatigue were moderately associated (SP: r = 0.80, p < 0.0001; SA: r = 0.77; p < 0.0001), with BA associations between the two devices fair-moderate (SP; ρC = 0.70: SA; ρC = 0.30). These findings demonstrated that a skin-surface muscle mechanomyography sensor was an accurate proxy for electrically-evoked muscle contraction torques when directly measured during isometric dynamometry in individuals with SCI. The novel application of the MC sensor during FES-evoked muscle contractions suggested its possible application for real-world tasks (e.g., prolonged sit-to-stand, stepping,) where muscle forces during fatiguing activities cannot be directly measured.

Evoked Electromyographically Controlled Electrical Stimulation

Time-variant muscle responses under electrical stimulation (ES) are often problematic for all the applications of neuroprosthetic muscle control. This situation limits the range of ES usage in relevant areas, mainly due to muscle fatigue and also to changes in stimulation electrode contact conditions, especially in transcutaneous ES. Surface electrodes are still the most widely used in noninvasive applications. Electrical field variations caused by changes in the stimulation contact condition markedly affect the resulting total muscle activation levels. Fatigue phenomena under functional electrical stimulation (FES) are also well known source of time-varying characteristics coming from muscle response under ES. Therefore, it is essential to monitor the actual muscle state and assess the expected muscle response by ES so as to improve the current ES system in favor of adaptive muscle-response-aware FES control. To deal with this issue, we have been studying a novel control technique using evoked electromyography (eEMG) signals to compensate for these muscle time-variances under ES for stable neuroprosthetic muscle control. In this perspective article, I overview the background of this topic and highlight important points to be aware of when using ES to induce the desired muscle activation regardless of the time-variance. I also demonstrate how to deal with the common critical problem of ES to move toward robust neuroprosthetic muscle control with the Evoked Electromyographically Controlled Electrical Stimulation paradigm.

Real-time estimation of FES-induced joint torque with evoked EMG : Application to spinal cord injured patients

Background

Functional electrical stimulation (FES) is a neuroprosthetic technique for restoring lost motor function of spinal cord injured (SCI) patients and motor-impaired subjects by delivering short electrical pulses to their paralyzed muscles or motor nerves. FES induces action potentials respectively on muscles or nerves so that muscle activity can be characterized by the synchronous recruitment of motor units with its compound electromyography (EMG) signal is called M-wave. The recorded evoked EMG (eEMG) can be employed to predict the resultant joint torque, and modeling of FES-induced joint torque based on eEMG is an essential step to provide necessary prediction of the expected muscle response before achieving accurate joint torque control by FES.

Methods

Previous works on FES-induced torque tracking issues were mainly based on offline analysis. However, toward personalized clinical rehabilitation applications, real-time FES systems are essentially required considering the subject-specific muscle responses against electrical stimulation. This paper proposes a wireless portable stimulator used for estimating/predicting joint torque based on real time processing of eEMG. Kalman filter and recurrent neural network (RNN) are embedded into the real-time FES system for identification and estimation.

Results

Prediction results on 3 able-bodied subjects and 3 SCI patients demonstrate promising performances. As estimators, both Kalman filter and RNN approaches show clinically feasible results on estimation/prediction of joint torque with eEMG signals only, moreover RNN requires less computational requirement.

Conclusion

The proposed real-time FES system establishes a platform for estimating and assessing the mechanical output, the electromyographic recordings and associated models. It will contribute to open a new modality for personalized portable neuroprosthetic control toward consolidated personal healthcare for motor-impaired patients.

Potential of M-Wave Elicited by Double Pulse for Muscle Fatigue Evaluation in Intermittent Muscle Activation by Functional Electrical Stimulation for Motor Rehabilitation

Clinical studies on application of functional electrical stimulation (FES) to motor rehabilitation have been increasing. However, muscle fatigue appears early in the course of repetitive movement production training by FES. Although M-wave variables were suggested to be reliable indices of muscle fatigue in long lasting constant electrical stimulation under the isometric condition, the ability of M-wave needs more studies under intermittent stimulation condition, because the intervals between electrical stimulations help recovery of muscle activation level. In this paper, M-waves elicited by double pulses were examined in muscle fatigue evaluation during repetitive movements considering rehabilitation training with surface electrical stimulation. M-waves were measured under the two conditions of repetitive stimulation: knee extension force production under the isometric condition and the dynamic movement condition by knee joint angle control. Amplitude of M-wave elicited by the 2nd pulse of a double pulse decreased during muscle fatigue in both measurement conditions, while the change in M-waves elicited by single pulses in a stimulation burst was not relevant to muscle fatigue in repeated activation with stimulation interval of 1 s. Fatigue index obtained from M-waves elicited by 2nd pulses was suggested to provide good estimation of muscle fatigue during repetitive movements with FES.

The effectiveness of FES-evoked EMG potentials to assess muscle force and fatigue in individuals with spinal cord injury

The evoked electromyographic signal (eEMG) potential is the standard index used to monitor both electrical changes within the motor unit during muscular activity and the electrical patterns during evoked contraction. However, technical and physiological limitations often preclude the acquisition and analysis of the signal especially during functional electrical stimulation (FES)-evoked contractions. Hence, an accurate quantification of the relationship between the eEMG potential and FES-evoked muscle response remains elusive and continues to attract the attention of researchers due to its potential application in the fields of biomechanics, muscle physiology, and rehabilitation science. We conducted a systematic review to examine the effectiveness of eEMG potentials to assess muscle force and fatigue, particularly as a biofeedback descriptor of FES-evoked contractions in individuals with spinal cord injury. At the outset, 2867 citations were identified and, finally, fifty-nine trials met the inclusion criteria. Four hypotheses were proposed and evaluated to inform this review. The results showed that eEMG is effective at quantifying muscle force and fatigue during isometric contraction, but may not be effective during dynamic contractions including cycling and stepping. Positive correlation of up to r = 0.90 (p < 0.05) between the decline in the peak-to-peak amplitude of the eEMG and the decline in the force output during fatiguing isometric contractions has been reported. In the available prediction models, the performance index of the eEMG signal to estimate the generated muscle force ranged from 3.8% to 34% for 18 s to 70 s ahead of the actual muscle force generation. The strength and inherent limitations of the eEMG signal to assess muscle force and fatigue were evident from our findings with implications in clinical management of spinal cord injury (SCI) population.

Surface EMG as a fatigue indicator during FES-induced isometric muscle contractions

The electromyogram (EMG) signal has potential as an indicator of stimulated muscle fatigue in applications of functional electrical stimulation (FES). In particular, it could be used to detect near lower limb collapse due to the associated muscle fatigue in FES-aided standing systems and thereby prevent falling. Surface EMG measurement, however, is hampered by stimulation artifact during FES. Modified surface stimulation and EMG detection equipment were designed and built to minimize this artifact and to permit detection of the electrical signal generated by the muscle during contraction. Artifact reduction techniques included shorting stimulator output leads between stimulus pulses and limiting and blanking slew rate in the EMG processing stage. Isometric fatigue experiments were performed by stimulating the quadriceps muscle of 20 able-bodied (a total of 125 trials) and three spinal cord injured (18 trials) subjects. Fatigue-tracking performance indicators were derived from the root-mean-square (RMS) of the EMG amplitude and from the median frequency (MF) of the EMG power spectral content. The results demonstrate that reliable fatigue tracking indicators for practical FES applications will be difficult to obtain, but that amplitude-based measures in spinal cord injured subjects show promise.

Detection and prediction of FES-induced fatigue

The estimation of externally elicited muscle forces is important for the better control of a functional electrical stimulation (FES)-assistive system. Various techniques of signal processing are presented, all with only one aim, to determine the correlation between the decrease of muscle force after continuous stimulation and surface recordings of the evoked potentials. Wrist flexor muscles were stimulated under isometric conditions, and surface electromyography (sEMG) was used to record wrist joint torque in both able-bodied and spinal cord injured volunteers. The joint torque was determined from recordings of the force generated by the wrist flexors, with the forearm immobilized. The sEMG was recorded utilizing a preamplifier with a stimulation artefact suppression circuitry. The signal was processed in the time and frequency domains, and analysed vs time, as well as in the state space formed by the wrist torque and evoked potential. The torque vs sEMG curves were used to establish the relationship that can be used for detection of the decrease of the force associated with FES-induced muscle fatigue. Among seven different techniques of sEMG processing the best correlation was found between the median frequency and force changes. The phase plane plot was fitted with an exponential curve, and the parameters obtained from the fitting were used to determine two events: prediction of the onset of fatigue and detection of fatigue. This suggests that it is possible to use the processed sEMG as a trigger signal to change the pattern of stimulation and allow the muscle to recover while resting, or to inform the user that the muscle force will soon drop rapidly. The recovery of the muscle force and sEMG was also analysed to learn more about the mechanisms that may be responsible for FES-induced fatigue. This technique offers simple on-off type feedback capability for fatigue detection in FES applications.

Truncation effects on muscular fatigue indexes based on M waves analysis

In this paper, we investigate muscular fatigue. We propose a new fatigue index based on the continuous wavelet transform (CWT) and compare it with the standard fatigue indexes from literature. Fatigue indexes are all based on the electrical activity of muscles (electromyogram) acquired during an electrically stimulated contraction (ES). The stimulator and electromyogram system, which were presented in a previous work, allows real-time analysis. The extracted fatigue parameters are compared between each other and their sensitivity to noise is studied. The effect of truncation of M waves is then investigated, enlightening the robustness of the index obtained using CWT.

Evoked EMG-based torque prediction under muscle fatigue in implanted neural stimulation

In patients with complete spinal cord injury, fatigue occurs rapidly and there is no proprioceptive feedback regarding the current muscle condition. Therefore, it is essential to monitor the muscle state and assess the expected muscle response to improve the current FES system toward adaptive force/torque control in the presence of muscle fatigue. Our team implanted neural and epimysial electrodes in a complete paraplegic patient in 1999. We carried out a case study, in the specific case of implanted stimulation, in order to verify the corresponding torque prediction based on stimulus evoked EMG (eEMG) when muscle fatigue is occurring during electrical stimulation. Indeed, in implanted stimulation, the relationship between stimulation parameters and output torques is more stable than external stimulation in which the electrode location strongly affects the quality of the recruitment. Thus, the assumption that changes in the stimulation-torque relationship would be mainly due to muscle fatigue can be made reasonably. The eEMG was proved to be correlated to the generated torque during the continuous stimulation while the frequency of eEMG also decreased during fatigue. The median frequency showed a similar variation trend to the mean absolute value of eEMG. Torque prediction during fatigue-inducing tests was performed based on eEMG in model cross-validation where the model was identified using recruitment test data. The torque prediction, apart from the potentiation period, showed acceptable tracking performances that would enable us to perform adaptive closed-loop control through implanted neural stimulation in the future.

Evoked EMG and muscle fatigue during isokinetic FES-cycling in individuals with SCI

PURPOSE

This study investigated whether muscle fatigue during functional electrical stimulation (FES)-induced cycling was associated with changes occurring in evoked electromyographic signals (eEMG, M-waves) in individuals with spinal cord injury. We also explored the effects of recovery intervals between exercise sessions on the relationship between eEMG and muscle torque.

METHODS

Eight individuals with spinal cord injury performed three FES-cycling sessions of 15-min duration, with 5 min of recovery between them. The quadriceps muscles were electrically stimulated as the prime agonist to produce cycling. Pedal torques and surface eEMG signals were synchronously processed and recorded for offline analysis.

RESULTS

Large Torque decreases (20-44%) were observed in the first 5 min of cycling during the three exercise bouts, while changes of similar magnitude did not occur on any of the M-wave time-series (less than 19%). Between 5 and 15 min of cycling, muscle fatigue lowered the plateau baselines of Torque (ranging from 41% to 62%), M-wave peak-to-peak amplitude (PtpA) and Area (ranging from 60% to 98%) time-series, yet the magnitudes of these reductions were not consistent between them.

CONCLUSION

We concluded that muscle fatigue during FES-cycling was not associated with, nor could be predicted by, eEMG signals. Nonetheless, the consistency between M-waves and Torque time-curves in their direction of change clearly warrants further investigation.

A mixed FES/EMG system for real time analysis of muscular fatigue

In this article, we present a functional electrical stimulator allowing the extraction in real time of M-wave characteristics from resulting EMG recodings in order to quantify muscle fatigue. This system is composed of three parts. A Labview software managing the stimulation output and electromyogram (EMG) input signal, a hardware part amplifying the output and input signal and a link between the two previous parts which is made up from input/output module (NIdaq USB 6251). In order to characterize the fatigue level, the Continuous Wavelet Transform is applied yielding a local maxima detection. The fatigue is represented on a scale from 0 for a fine shaped muscle to 100 for a very tired muscle. Premilary results are given.

Torque prediction using stimulus evoked EMG and its identification for different muscle fatigue states in SCI subjects

Muscle fatigue is an unavoidable problem when electrical stimulation is applied to paralyzed muscles. The detection and compensation of muscle fatigue is essential to avoid movement failure and achieve desired trajectory. This work aims to predict ankle plantar-flexion torque using stimulus evoked EMG (eEMG) during different muscle fatigue states. Five spinal cord injured patients were recruited for this study. An intermittent fatigue protocol was delivered to triceps surae muscle to induce muscle fatigue. A hammerstein model was used to capture the muscle contraction dynamics to represent eEMG-torque relationship. The prediction of ankle torque was based on measured eEMG and past measured or past predicted torque. The latter approach makes it possible to use eEMG as a synthetic force sensor when force measurement is not available in daily use. Some previous researches suggested to use eEMG information directly to detect and predict muscle force during fatigue assuming a fixed relationship between eEMG and generated force. However, we found that the prediction became less precise with the increase of muscle fatigue when fixed parameter model was used. Therefore, we carried out the torque prediction with an adaptive parameters using the latest measurement. The prediction of adapted model was improved with 16.7%-50.8% comparing to the fixed model.

Force output during fatigue with progressively increasing stimulation frequency

There is currently a controversy over whether stimulation frequencies should increase or decrease to optimize force output over time. This study compared changes in thenar muscle force and M-wave amplitude during progressively increasing (20-40 Hz), decreasing (40-20 Hz) and constant (20 Hz) frequency stimulation of the median nerve continuously for 3 min. Twenty-three individuals participated in three sets of experiments. There was no significant difference in the force-time integrals between the three fatigue tasks. The rate of fatigue was not correlated to the number of stimulation pulses delivered (20 Hz: 3,600, 20-40 and 40-20 Hz: 5,400). All fatigue tasks caused a significant reduction in M-wave amplitude and the reduction was largest for the 20-40 Hz protocol. However, multiple linear regression analysis revealed that the M-wave amplitude could not predict the changes in force over time for the 20 Hz or 20-40 Hz protocols. Thus during sustained evoked contractions with stimulation frequencies within the physiological range, frequencies can vary significantly without changing the overall force-time integral.

Leg joint power output during progressive resistance FES-LCE cycling in SCI subjects: developing an index of fatigue

Background

The purpose of this study was to investigate the biomechanics of the hip, knee and ankle during a progressive resistance cycling protocol in an effort to detect and measure the presence of muscle fatigue. It was hypothesized that knee power output can be used as an indicator of fatigue in order to assess the cycling performance of SCI subjects.

Methods

Six spinal cord injured subjects (2 incomplete, 4 complete) between the ages of twenty and fifty years old and possessing either a complete or incomplete spinal cord injury at or below the fourth cervical vertebra participated in this study. Kinematic data and pedal forces were recorded during cycling at increasing levels of resistance. Ankle, knee and hip power outputs and resultant pedal force were calculated. Ergometer cadence and muscle stimulation intensity were also recorded.

Results

The main findings of this study were: (a) ankle and knee power outputs decreased, whereas hip power output increased with increasing resistance, (b) cadence, stimulation intensity and resultant pedal force in that combined order were significant predictors of knee power output and (c) knowing the value of these combined predictors at 10 rpm, an index of fatigue can be developed, quantitatively expressing the power capacity of the knee joint with respect to a baseline power level defined as fatigue.

Conclusion

An index of fatigue was successfully developed, proportionalizing knee power capacity during cycling to a predetermined value of fatigue. The fatigue index value at 0/8^th kp, measured 90 seconds into active, unassisted pedaling was 1.6. This indicates initial power capacity at the knee to be 1.6 times greater than fatigue. The fatigue index decreased to 1.1 at 2/8^th kp, representing approximately a 30% decrease in the knee's power capacity within a 4 minute timespan. These findings suggest that the present cycling protocol is not sufficient for a rider to gain the benefits of FES and thus raises speculation as to whether or not progressive resistance cycling is an appropriate protocol for SCI subjects.

Measurement and modeling of stimulus-evoked electromyography in lengthened and shortened muscles for spinal cord injured subjects during an electrically-elicited fatigue process

This study compares the amplitude and temporal features of stimulus-evoked electromyography (EMG) of paralyzed muscle, rectus femoris (RF), in both lengthened and shortened positions of six spinal cord injured (SCI) subjects during an electrically elicited fatigue process. The torque output and evoked EMG were fitted by hyperbolic tangent functions from which their amplitude residual levels and temporal inflection times can be extracted. Furthermore, a structural EMG model of Fuglevand et al (1992 Biol. Cybern. 67 143-53) was modified to include type I (slow twitch) and type II (fast twitch) of motor unit (MU) fibers with viable parameters obtained from paralyzed muscles to observe their amplitude and temporal changes. Our results showed that the amplitude of stimulus-evoked EMG decreased earlier in the lengthened muscle with a shorter inflection time (48.53 +/- 8.7 s versus 55.13 +/- 4.03 s) than that of the shortened position during 120 s of stimulation time (p < 0.05). Similarly, the peak-to-peak duration (PTPd) of the evoked EMG increased faster at an earlier time to a higher asymptotical value in lengthened muscle (2.23 +/- 0.74 versus 1.77 +/- 0.54), compared to that of a shortened one (p < 0.05). These observations coincided with the higher rising rate and larger final value of the temporal coefficients, i.e., longer duration, in both type I and II MUs of lengthened muscles. From the observation of all parameters, the fatigue process in lengthened muscle proceeds faster than that in shortened muscle.

Fatigue compensation during FES using surface EMG

Muscle fatigue limits the effectiveness of FES when applied to regain functional movements in spinal cord injured (SCI) individuals. The stimulation intensity must be manually increased to provide more force output to compensate for the decreasing muscle force due to fatigue. An artificial neural network (ANN) system was designed to compensate for muscle fatigue during functional electrical stimulation (FES) by maintaining a constant joint angle. Surface electromyography signals (EMG) from electrically stimulated muscles were used to determine when to increase the stimulation intensity when the muscle's output started to drop. In two separate experiments on able-bodied subjects seated in hard back chairs, electrical stimulation was continuously applied to fatigue either the biceps (during elbow flexion) or the quadriceps muscle (during leg extension) while recording the surface EMG. An ANN system was created using processed surface EMG as the input, and a discrete fatigue compensation control signal, indicating when to increase the stimulation current, as the output. In order to provide training examples and test the systems' performance, the stimulation current amplitude was manually increased to maintain constant joint angles. Manual stimulation amplitude increases were required upon observing a significant decrease in the joint angle. The goal of the ANN system was to generate fatigue compensation control signals in an attempt to maintain a constant joint angle. On average, the systems could correctly predict 78.5% of the instances at which a stimulation increase was required to maintain the joint angle. The performance of these ANN systems demonstrates the feasibility of using surface EMG feedback in an FES control system.

Detection of fatigue in the isometric electrical activation of paralyzed hand muscles of persons with tetraplegia

Paralyzed muscle fatigue is the eventual depression of force due to either prolonged or repetitive electrical stimulation of motor units. The robustness and safety of future functional electrical stimulation (FES) systems will rely on their ability to detect the onset of muscle fatigue. The relative degree of muscle activation can be estimated by monitoring the M-wave. The aim of this study was to test a proposed method of quantitative fatigue assessment that detects muscle force output and its corresponding M-wave measured concurrently. The detection of force and M-wave concurrently allows any reduction in muscle force output to be attributed to either changes in the fatigue state of the stimulated muscle or changes in the degree of stimulus activation of that muscle. The fatigue assessment scheme can thereby accommodate the corresponding changes in muscle force caused by an alteration in the stimulation intensity during fatigue. The Extensor Digitorum Communis (EDC), Extensor Pollicis Longus (EPL), and Flexor Pollicis Longus (FPL) muscles of two C5/C6 tetraplegic men were studied. Stimulation recruitment tests over the pulsewidth range from 0 to 200 micros, were performed at intervals during 20 min of maximal stimulation (200 micro/s). Muscle force correlated to the M-wave parameter, second phase area, with mean correlation coefficients of greater than 0.82, when the muscle was in either a nonfatigued or fatiguing state. After the initial force, likely to be primarily due to the fast glycolytic (FG) motor units, had declined the M-wave demonstrated only minor changes throughout the fatigue of muscle force during 20 min of constant maximal stimulation. The second phase area and root-mean-square (rms) of the M-wave [see Fig. 2(a) reflected muscle activation during modulated stimulation and also remained relatively constant during the fatigue-related force decline when the muscle was stimulated at a constant intensity. This detection of M-wave parameters satisfies the defined requirement for a myoelectric parameter that indicates electrical activation, but is relatively invariant to muscular fatigue. Index Terms-Electrical stimulation, electromyography (EMG), functional electrical stimulation (FES), muscle fatigue, spinal cord injury, tetraplegia.

Selective activation of muscle groups in the feline hindlimb through electrical microstimulation of the ventral lumbo-sacral spinal cord

Selective activation of muscle groups in the feline hindlimb by electrical stimulation of the ventral lumbo-sacral spinal cord was investigated. Spinal cord segments L5 to S1 were mapped using a penetrating tungsten needle electrode. Locations that produced isolated contraction of quadriceps, tibialis anterior or triceps surae/plantaris muscles when stimulated with a current of 40 microA or less, and in which spread of activity to other muscles was not detected after increasing the stimulus to at least twice the threshold level, were defined as belonging to the target muscle's "activation pool." The quadriceps activation pool was found to extend from the beginning of L5 to the middle of L6. The tibialis anterior activation pool extended from the beginning of L6 to the middle of L7, and the triceps surae/plantaris activation pool extended from the caudal end of L6 to the beginning of S1. The three activation pools were located in Rexed motor lamina IX and their spatial organization was found to correspond well with that of the anatomically defined motor pools innervating the same muscles. The spatial and functional segregation of motor pools manifested at the spinal cord level can have direct applications in the areas of functional electrical stimulation and motor control.

Muscle recruitment through electrical stimulation of the lumbo-sacral spinal cord

The goal of this study was to determine the feasibility of producing graded muscle contraction in individual muscles or muscle groups by electrically stimulating motor neurons in the lumbo-sacral spinal cord. Recruitment curves were obtained for quadriceps, tibialis anterior and triceps surae/plantaris by stimulating their activation pools in the ventral horn of the feline spinal cord. Mean twitch times-to-peak for quadriceps, tibialis anterior and triceps surae/plantaris were 33.0, 41.0, and 36.0 ms, respectively. Twitch duration as a function of stimulus strength demonstrated a mixed motor unit recruitment order, distinctively different from the inverse recruitment order exhibited by conventional methods of electrical stimulation of peripheral nerve. The recruitment curve slopes (expressed as a percentage of maximum force per nanocurrent of delivered charge) were shallow: 7.9 for quadriceps, 2.6 for tibialis anterior and 8.5 for triceps surae/plantaris. These results show that graded control of force in individual muscles or muscle groups can be obtained through spinal cord stimulation, and suggest that spinal cord stimulation could be used for functional neuromuscular stimulation applications.

Transcutaneous FES of the paralyzed quadriceps: is knee torque affected by unintended activation of the hamstrings?

This study addresses the question whether unintended response of the knee flexors (hamstrings) accompanies transcutaneous functional electrical stimulation (FES) of the quadriceps and whether the knee torque is hereby affected. Transcutaneous FES of the right quadriceps of two paraplegic subjects was applied and measurements were made of the net torque and of the myoelectric activities of the quadriceps and hamstrings muscles of the right leg. A low correlation was obtained between the peak-to-peak amplitudes of the M-waves of the two muscles. This correlation decreased further with the development of fatigue, which indicated that the electromyography (EMG) signals from the hamstrings were not the result of cross-talk between adjacent recording sites. The force profile of each muscle was determined from a developed model incorporating EMG-based activation, muscle anthropometry as obtained from in vivo magnetic resonance imaging of the thigh, and metabolic fatigue function, based on data acquired by 31P nuclear magnetic resonance spectroscopy. A sensitivity analysis revealed that the muscle specific tension and the muscle moment arms have a major influence on the resulting muscle forces and should therefore be accurately provided. The results show that during the unfatigued phase of contraction the estimated maximal force in the hamstrings was lower than 20% of that in the quadriceps and could be considered to be practically negligible. As fatigue progressed the hamstrings-to-quadriceps force ratio increased, reaching up to 45%, and the effect of co-activation on the torque partition between the two muscles was no longer negligible.

EMG and metabolite-based prediction of force in paralyzed quadriceps muscle under interrupted stimulation

A major issue associated with functional electrical stimulation (FES) of a paralyzed limb is the decay with time of the muscle force as a result of fatigue. A possible means to reduce fatigue during FES is by using interrupted stimulation, in which fatigue and recovery occur in sequence. In this study, we present a model which enables us to evaluate the temporal force generation capacity within the electrically activated muscle during first stimulation fatigue, i.e., when the muscle is activated from unfatigued initial conditions, and during postrest stimulation, i.e., after different given rest durations. The force history of the muscle is determined by the activation as derived from actually measured electromyogram (EMG) data, and by the metabolic fatigue function expressing the temporal changes of muscle metabolites, from existing data acquired by in vivo 31P MR spectroscopy in terms of the inorganic phosphorus variables, Pi or H2PO4-, and by the intracellular pH. The model was solved for supra-maximal stimulation in isometric contractions separated by rest periods, and compared to experimentally obtained measurements. EMG data were fundamental for prediction of the ascending force during its posttetanic response. On the other hand, prediction of the decaying phase of the force was possible only by means of the metabolite-based fatigue function. The prediction capability of the model was assessed by means of the error between predicted and measured force profiles. The predicted force obtained from the model in first stimulation fatigue fits well with the experimental one. In postrest stimulation fatigue, the different metabolites provided different prediction capabilities of the force, depending on the duration of the rest period. Following rest duration of 1 min, Pi provided the best prediction of force; H2PO4- extended the prediction capacity of the model to up to 6 min and pH provided a reliable prediction for rest durations longer than 12 min. The results presented shed light on the roles of EMG and of metabolites in prediction of the force history of a paralyzed muscle under conditions where fatigue and recovery occur in sequence.

An artefact suppressing fast-recovery myoelectric amplifier

An amplifier for recording myoelectric signals using surface electrodes has been developed. The special features are suppression of stimulation artefacts and motion artefacts from electrodes. It is designed for recording of myoelectric signals from a muscle that is being stimulated with short impulses. The artifact suppression is achieved by using fast-recovery instrumentation amplifiers and having a nonlinear feedback loop for automatic compensation of changes in DC-offset.

Neuromuscular propagation after fatiguing contractions of the paralyzed soleus muscle in humans

We analyzed the M wave and torque after repetitive activation and recovery of the human soleus muscle in individuals with spinal cord injury. Fifteen individuals with complete paralysis had the tibial nerve activated for 330 ms every second with a 20-Hz train. The M wave and torque were analyzed before fatigue, immediately after fatigue, and during recovery. The torque and three M-wave measurements (amplitude, duration, median frequency) changed significantly after fatigue in the chronic group, but the M-wave area was not changed. The M wave was completely recovered after 5 min of rest, even though the torque remained depressed during recovery. The M-wave changes appeared to contribute minimally to the reduced torque in individuals with chronic paralysis. The disassociation in the M-wave-torque relationship during fatigue and recovery suggests, that electrical stimulation under electromyography control is not an ideal method to optimize torque in paralyzed muscle.

The validity of stimulus-evoked EMG for studying muscle fatigue characteristics of paraplegic subjects during dynamic cycling movement

The fatigue characteristics of paralyzed muscles were investigated during dynamic cycling movement induced by functional electrical stimulation (FES). The peak-to-peak (PTP) amplitude of stimulus-evoked electromyogram (EMG), after suppression of stimulus artifact, was adopted as fatigue indicator. Compared to static contraction, the effects of dynamic movement factors on the stimulus-evoked EMG, such as the intermittent stimulation, joint angle, and contraction speed, were first evaluated in separate experiments. The results of isolated tests laid the foundation for interpreting the data obtained in two FES-cycling experiments, performed under maximum stimulation or in controlled cycling speeds. The effects of intermittent stimulation and joint angle caused periodic changes in PTP amplitude which can be alleviated by averaging the PTP amplitude of one cycle. Under the same stimulation intensity, our results indicated that slower muscle contraction speed would have larger PTP amplitude and vice versa. For the limited number of subjects with paraplegia studied, our results showed that the use of EMG PTP as reliable muscle fatigue indicator during dynamic movement is only valid at the same cycling speed or corresponding contraction speed. The decline of the PTP amplitude decreased with the decay of muscle force can be observed during cycling movement; however, reduction of cycling speed had the opposite effect on PTP amplitude. Observations from the hyperbolic modeling of fatigue process demonstrated that the EMG PTP of a fatigued muscle under dynamic movement decreased at a slower rate.

Perspectives on the role of afferent signals in control of motor neuroprostheses

K.O. Johnson reviews the architecture and low level neural mechanisms by which the external environment is transduced and encoded into the neural system, summarizing work that correlates neurophysiological and psychophysical testing with isolation of sensory components. The slowly adapting Type I afferent system is responsible for form and texture perception; the rapidly adapting afferent system is responsible for motion perception; and the Pacinian corpuscle system is responsible for vibratory sensation. R.R. Riso reviews the current level of understanding of the major factors to be considered in the design of a functional neuromuscular stimulation (FNS) grasp controller that uses cutaneous sensory feedback to detect slip. The elegant natural control scheme that matches the ratio of grip and lift forces to frictional conditions provides a model for implementing a slip-based control algorithm. D. Popović discusses the possible use of recordings from more proximal peripheral nerves to determine needed information for synthesis of locomotion. The discussion is illustrated with an animal model where rule-based closed-loop control is used for the ankle joint during treadmill locomotion. Neural signals from the tibial and superficial peroneal nerves were employed to substitute for missing afferent input from cutaneous and proprioceptive sensors. The feasibility of more invasive intraneural electrodes for distinguishing sensory from motor information in mixed nerves is considered. M. Koris raises surgical and functional issues relevant to developing clinical FNS systems. C. Van Doren suggests alternative neurophysiological and engineering approaches.