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

Low-cost stimulation resistant electromyography

Surface Electromyography (sEMG) is the non-invasive measurement of skeletal muscle contraction bio-potentials. Measuring sEMG of a stimulated muscle can prove particularly difficult due to large scale and long lasting stimulation-induced artefacts: if an sEMG device does not account for such artefacts, its measurements can be swamped and components damaged. sEMG has been used in a wide range of clinical and biomedical fields, providing measures such as muscular fatigue and subject intent. The recording of sEMG can prove difficult due to signal contamination such as movement artefact and mains interference. There are very few commercial sEMG devices that contain protection against large stimulation voltages or measures to reduce artefact transient times. Furthermore, most commercial or research level designs are not open source; these designs are effectively an inflexible black box to researchers and developers. This research presents the design, test and validation of an open source sEMG design, able to record muscle bio-potentials concurrently to electrical stimulation. The open source, low-cost nature of the design provides accessibility to researchers without the time and cost associated with design development. The design has been tested on the forearms of four able-bodied subjects during 25 Hz constant current stimulation, and has been shown to record subject volitional sEMG and M-wave without saturation.

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.

Ultra-High-Frame-Rate Ultrasound Monitoring of Muscle Contractility Changes Due to Neuromuscular Electrical Stimulation

The quick onset of muscle fatigue is a critical issue when applying neuromuscular electrical stimulation (NMES) to generate muscle contractions for functional limb movements, which were lost/impaired due to a neurological disorder or an injury. For in situ assessment of the effect of NMES-induced muscle fatigue, a novel noninvasive sensor modality that can quantify the degraded contractility of a targeted muscle is required. In this study, instantaneous strain maps of a contracting muscle were derived from ultra-high-frame-rate (2 kHz) ultrasound images to quantify the contractility. A correlation between strain maps and isometric contraction force values was investigated. When the muscle reached its maximum contraction, the maximum and the mean values of the strain map were correlated with the force values and were further used to stage the contractility change. During the muscle activation period, a novel methodology based on the principal component regression (PCR) was proposed to explore the strain-force correlation. The quadriceps muscle of 3 able-bodied human participants was investigated during NMES-elicited isometric knee extension experiments. Strong to very strong correlation results were obtained and indicate that the proposed measurements from ultrasound images are promising to quantify the muscle contractility changes during NMES.

Quadriceps mechanomyography reflects muscle fatigue during electrical stimulus-sustained standing in adults with spinal cord injury - a proof of concept

This study investigates whether mechanomyography (MMG) produced from contracting muscles as a measure of their performance could be a proxy of muscle fatigue during a sustained functional electrical stimulation (FES)-supported standing-to-failure task. Bilateral FES-evoked contractions of quadriceps and glutei muscles, of four adults with motor-complete spinal cord injury (SCI), were used to maintain upright stance using two different FES frequencies: high frequency (HF - 35 Hz) and low frequency (LF - 20 Hz). The time at 30° knee angle reduction was taken as the point of critical "fatigue failure", while the generated MMG characteristics were used to track the pattern of force development during stance. Quadriceps fatigue, which was primarily responsible for the knee buckle, was characterized using MMG-root mean square (RMS) amplitude. A double exponential decay model fitted the MMG fatigue data with good accuracy [R2 = 0.85-0.99; root mean square error (RMSE) = 2.12-8.10] implying changes in the mechanical activity performance of the muscle's motor units. Although the standing duration was generally longer for the LF strategy (31-246 s), except in one participant, when compared to the HF strategy, such differences were not significant (p > 0.05) but suggested a faster muscle fatigue onset during HF stimulation. As MMG could discriminate between different stimulation frequencies, we speculate that this signal can quantify muscle fatigue characteristics during prolonged FES applications.

EEG Sensorimotor Correlates of Speed During Forearm Passive Movements

Although passive movement therapy has been widely adopted to recover lost motor functions of impaired body parts, the underlying neural mechanisms are still unclear. In this context, fully understanding how the proprioceptive input modulates the brain activity may provide valuable insights. Specifically, it has not been investigated how the speed of motions, passively guided by a haptic device, affects the sensorimotor rhythms (SMR). On the grounds that faster passive motions elicit larger quantity of afferent input, we hypothesize a proportional relationship between localized SMR features and passive movement speed. To address this hypothesis, we conducted an experiment where healthy subjects received passive forearm oscillations at different speed levels while their electroencephalogram was recorded. The mu and beta event related desynchronization (ERD) and beta rebound of both left and right sensorimotor areas are analyzed by linear mixed-effects models. Results indicate that passive movement speed is correlated with the contralateral beta rebound and ipsilateral mu ERD. The former has been previously linked with the processing of proprioceptive afferent input quantity, while the latter with speed-dependent inhibitory processes. This suggests the existence of functionally-distinct frequency-specific neuronal populations associated with passive movements. In future, our findings may guide the design of novel rehabilitation paradigms.

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.

Real-Time Detection of Seven Phases of Gait in Children with Cerebral Palsy Using Two Gyroscopes

A recently designed gait phase detection (GPD) system, with the ability to detect all seven phases of gait in healthy adults, was modified for GPD in children with cerebral palsy (CP). A shank-attached gyroscope sent angular velocity to a rule-based algorithm in LabVIEW to identify the distinct characteristics of the signal. Seven typically developing children (TD) and five children with CP were asked to walk on treadmill at their self-selected speed while using this system. Using only shank angular velocity, all seven phases of gait (Loading Response, Mid-Stance, Terminal Stance, Pre-Swing, Initial Swing, Mid-Swing and Terminal Swing) were reliably detected in real time. System performance was validated against two established GPD methods: (1) force-sensing resistors (GPD-FSR) (for typically developing children) and (2) motion capture (GPD-MoCap) (for both typically developing children and children with CP). The system detected over 99% of the phases identified by GPD-FSR and GPD-MoCap. Absolute values of average gait phase onset detection deviations relative to GPD-MoCap were less than 100 ms for both TD children and children with CP. The newly designed system, with minimized sensor setup and low processing burden, is cosmetic and economical, making it a viable solution for real-time stand-alone and portable applications such as triggering functional electrical stimulation (FES) in rehabilitation systems. This paper verifies the applicability of the GPD system to identify specific gait events for triggering FES to enhance gait in children with CP.

Electrical Stimulation Frequency and Skeletal Muscle Characteristics: Effects on Force and Fatigue

This investigation aimed to determine the force and muscle surface electromyography (EMG) responses to different frequencies of electrical stimulation (ES) in two groups of muscles with different size and fiber composition (fast- and slow-twitch fiber proportions) during a fatigue-inducing protocol. Progression towards fatigue was evaluated in the abductor pollicis brevis (APB) and vastus lateralis (VL) when activated by ES at three frequencies (10, 35, and 50Hz). Ten healthy adults (mean age: 23.2 ± 3.0 years) were recruited; participants signed an IRB approved consent form prior to participation. Protocols were developed to 1) identify initial ES current intensity required to generate the 25% maximal voluntary contraction (MVC) at each ES frequency and 2) evaluate changes in force and EMG activity during ES-induced contraction at each frequency while progressing towards fatigue. For both muscles, stimulation at 10Hz required higher current intensity of ES to generate the initial force. There was a significant decline in force in response to ES-induced fatigue for all frequencies and for both muscles (p<0.05). However, the EMG response was not consistent between muscles. During the progression towards fatigue, the APB displayed an initial drop in force followed by an increase in EMG activity and the VL displayed a decrease in EMG activity for all frequencies. Overall, it appeared that there were some significant interactions between muscle size and fiber composition during progression towards fatigue for different ES frequencies. It could be postulated that muscle characteristics (size and fiber composition) should be considered when evaluating progression towards fatigue as EMG and force responses are not consistent between muscles.

Functional electrical stimulation-induced muscular fatigue: Effect of fiber composition and stimulation frequency on rate of fatigue development

This investigation evaluated the progression towards fatigue in two muscles of differing fast- and slow-twitch fiber proportions (abductor pollicis brevis (APB) and vastus lateralis (VL)) when activated by functional electrical stimulation (FES) at three frequencies (10, 35, and 50 Hz). Fatigue was defined as a 50% drop from the initial FES-induced force of 25% maximal voluntary contraction (MVC). Ten healthy adults (mean age: 23.2 ± 3.0 years) were recruited; participants signed an IRB approved consent form prior to participation. Protocols were developed to evaluate the effects of muscle size, fiber type and FES frequency on total time to fatigue. Results indicated that the predominantly fast-twitch VL fatigued more quickly than the slow-twitch APB at the higher frequencies (p < 0.05), but did not significantly differ with stimulation at 10 Hz. Overall, muscle size and FES frequencies showed some significant interactions when generating a defined force and during fatigue development. Furthermore, it appears that to reduce fatigue, FES treatments should not extend past ∼14-16 min for large and small muscle groups, respectively, when the muscle group's optimal stimulation frequency is applied.

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.

Effects of auditory stimuli on electrical activity in the brain during cycle ergometry

The present study sought to further understanding of the brain mechanisms that underlie the effects of music on perceptual, affective, and visceral responses during whole-body modes of exercise. Eighteen participants were administered light-to-moderate intensity bouts of cycle ergometer exercise. Each exercise bout was of 12-min duration (warm-up [3min], exercise [6min], and warm-down [3min]). Portable techniques were used to monitor the electrical activity in the brain, heart, and muscle during the administration of three conditions: music, audiobook, and control. Conditions were randomized and counterbalanced to prevent any influence of systematic order on the dependent variables. Oscillatory potentials at the Cz electrode site were used to further understanding of time-frequency changes influenced by voluntary control of movements. Spectral coherence analysis between Cz and frontal, frontal-central, central, central-parietal, and parietal electrode sites was also calculated. Perceptual and affective measures were taken at five timepoints during the exercise bout. Results indicated that music reallocated participants' attentional focus toward auditory pathways and reduced perceived exertion. The music also inhibited alpha resynchronization at the Cz electrode site and reduced the spectral coherence values at Cz-C4 and Cz-Fz. The reduced focal awareness induced by music led to a more autonomous control of cycle movements performed at light-to-moderate-intensities. Processing of interoceptive sensory cues appears to upmodulate fatigue-related sensations, increase the connectivity in the frontal and central regions of the brain, and is associated with neural resynchronization to sustain the imposed exercise intensity.

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.

Cerebral mechanisms underlying the effects of music during a fatiguing isometric ankle-dorsiflexion task

The brain mechanisms by which music-related interventions ameliorate fatigue-related symptoms during the execution of fatiguing motor tasks are hitherto under-researched. The objective of the present study was to investigate the effects of music on brain electrical activity and psychophysiological measures during the execution of an isometric fatiguing ankle-dorsiflexion task performed until the point of volitional exhaustion. Nineteen healthy participants performed two fatigue tests at 40% of maximal voluntary contraction while listening to music or in silence. Electrical activity in the brain was assessed by use of a 64-channel EEG. The results indicated that music downregulated theta waves in the frontal, central, and parietal regions of the brain during exercise. Music also induced a partial attentional switching from associative thoughts to task-unrelated factors (dissociative thoughts) during exercise, which led to improvements in task performance. Moreover, participants experienced a more positive affective state while performing the isometric task under the influence of music.

Brain mechanisms that underlie the effects of motivational audiovisual stimuli on psychophysiological responses during exercise

Motivational audiovisual stimuli such as music and video have been widely used in the realm of exercise and sport as a means by which to increase situational motivation and enhance performance. The present study addressed the mechanisms that underlie the effects of motivational stimuli on psychophysiological responses and exercise performance. Twenty-two participants completed fatiguing isometric handgrip-squeezing tasks under two experimental conditions (motivational audiovisual condition and neutral audiovisual condition) and a control condition. Electrical activity in the brain and working muscles was analyzed by use of electroencephalography and electromyography, respectively. Participants were asked to squeeze the dynamometer maximally for 30s. A single-item motivation scale was administered after each squeeze. Results indicated that task performance and situational motivational were superior under the influence of motivational stimuli when compared to the other two conditions (~20% and ~25%, respectively). The motivational stimulus downregulated the predominance of low-frequency waves (theta) in the right frontal regions of the cortex (F8), and upregulated high-frequency waves (beta) in the central areas (C3 and C4). It is suggested that motivational sensory cues serve to readjust electrical activity in the brain; a mechanism by which the detrimental effects of fatigue on the efferent control of working muscles is ameliorated.

Evoked EMG versus muscle torque during fatiguing functional electrical stimulation-evoked muscle contractions and short-term recovery in individuals with spinal cord injury

This study investigated whether the relationship between muscle torque and m-waves remained constant after short recovery periods, between repeated intervals of isometric muscle contractions induced by functional electrical stimulation (FES). Eight subjects with spinal cord injury (SCI) were recruited for the study. All subjects had their quadriceps muscles group stimulated during three sessions of isometric contractions separated by 5 min of recovery. The evoked-electromyographic (eEMG) signals, as well as the produced torque, were synchronously acquired during the contractions and during short FES bursts applied during the recovery intervals. All analysed m-wave variables changed progressively throughout the three contractions, even though the same muscle torque was generated. The peak to peak amplitude (PtpA), and the m-wave area (Area) were significantly increased, while the time between the stimulus artefact and the positive peak (PosT) were substantially reduced when the muscles became fatigued. In addition, all m-wave variables recovered faster and to a greater extent than did torque after the recovery intervals. We concluded that rapid recovery intervals between FES-evoked exercise sessions can radically interfere in the use of m-waves as a proxy for torque estimation in individuals with SCI. This needs to be further investigated, in addition to seeking a better understanding of the mechanisms of muscle fatigue and recovery.

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.

A passive movement method for parameter estimation of a musculo-skeletal arm model incorporating a modified hill muscle model

In this paper we present an experimental method of parameterising the passive mechanical characteristics of the bicep and tricep muscles in vivo, by fitting the dynamics of a two muscle arm model incorporating anatomically meaningful and structurally identifiable modified Hill muscle models to measured elbow movements. Measurements of the passive flexion and extension of the elbow joint were obtained using 3D motion capture, from which the elbow angle trajectories were determined and used to obtain the spring constants and damping coefficients in the model through parameter estimation. Four healthy subjects were used in the experiments. Anatomical lengths and moment of inertia values of the subjects were determined by direct measurement and calculation. There was good reproducibility in the measured arm movement between trials, and similar joint angle trajectory characteristics were seen between subjects. Each subject had their own set of fitted parameter values determined and the results showed good agreement between measured and simulated data. The average fitted muscle parallel spring constant across all subjects was 143 N/m and the average fitted muscle parallel damping constant was 1.73 Ns/m. The passive movement method was proven to be successful, and can be applied to other joints in the human body, where muscles with similar actions are grouped together.

Hybrid FES-robot cooperative control of ambulatory gait rehabilitation exoskeleton

Robotic and functional electrical stimulation (FES) approaches are used for rehabilitation of walking impairment of spinal cord injured individuals. Although devices are commercially available, there are still issues that remain to be solved. Control of hybrid exoskeletons aims at blending robotic exoskeletons and electrical stimulation to overcome the drawbacks of each approach while preserving their advantages. Hybrid actuation and control have a considerable potential for walking rehabilitation but there is a need of novel control strategies of hybrid systems that adequately manage the balance between FES and robotic controllers. Combination of FES and robotic control is a challenging issue, due to the non-linear behavior of muscle under stimulation and the lack of developments in the field of hybrid control. In this article, a cooperative control strategy of a hybrid exoskeleton is presented. This strategy is designed to overcome the main disadvantages of muscular stimulation: electromechanical delay and change in muscle performance over time, and to balance muscular and robotic actuation during walking.

Experimental results in healthy subjects show the ability of the hybrid FES-robot cooperative control to balance power contribution between exoskeleton and muscle stimulation. The robotic exoskeleton decreases assistance while adequate knee kinematics are guaranteed. A new technique to monitor muscle performance is employed, which allows to estimate muscle fatigue and implement muscle fatigue management strategies. Kinesis is therefore the first ambulatory hybrid exoskeleton that can effectively balance robotic and FES actuation during walking. This represents a new opportunity to implement new rehabilitation interventions to induce locomotor activity in patients with paraplegia.

Acronym list: 10mWT: ten meters walking test; 6MWT: six minutes walking test; FSM: finite-state machine; t-FSM: time-domain FSM; c-FSM: cycle-domain FSM; FES: functional electrical stimulation; HKAFO: hip-knee-ankle-foot orthosis; ILC: iterative error-based learning control; MFE: muscle fatigue estimator; NILC: Normalized stimulation output from ILC controller; PID: Proportional-Integral-derivative Control; PW: Stimulation pulse width; QUEST: Quebec User Evaluation of Satisfaction with assistive Technology; SCI: Spinal cord injury; TTI: torque-time integral; VAS: Visual Analog Scale.

A new method for muscle fatigue assessment: Online model identification techniques

INTRODUCTION

The purpose of this study was to propose a method that allows extraction of the current muscle state under electrically induced fatigue.

METHODS

The triceps surae muscle of 5 subjects paralyzed by spinal cord injury was fatigued by intermittent electrical stimulation (5 × 5 trains at 30 Hz). Classical fatigue indices representing muscle contractile properties [peak twitch (Pt) and half-relaxation time (HRT)] were assessed before and after each 5-train series and were used to identify 2 relevant parameters (Fm , Ur ) of a previously developed mathematical model using the Sigma-Point Kalman Filter.

RESULTS

Pt declined significantly during the protocol, whereas HRT remained unchanged. Identification of the model parameters with experimental data yielded a model-based fatigue assessment that gave a more stable evaluation of fatigue than classical parameters.

CONCLUSIONS

This work reinforces clinical research by providing a tool that clinicians can use to monitor fatigue development during stimulation.

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.

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.

Differentiation of power and endurance athletes based on their muscle fatigability assessed by new spectral electromyographic indices

The aim of this study was to differentiate between endurance and power athletes based on electromyographic (EMG) data analysed using new spectral indices. Nine endurance and six strength athletes were recruited to complete sets of knee extension repetitions (15 per set) until exhaustion, with each set followed by a maximal voluntary isometric knee extensor contraction. Peripheral muscle fatigue of the vastus lateralis, vastus medialis, and rectus femoris (bilaterally) was quantified by the changes in median frequency of the EMG power spectrum and a new spectral EMG fatigue index. Cluster analysis of the fatigue indices differentiated athletes into two groups: endurance (fatigue resistant) and strength (faster fatigue), whereas cluster analysis of the median EMG power spectrum frequency produced six indistinct groups. The average fatigue index for the quadriceps group increased across repetitions by 40 +/- 24% in the endurance group and by 184 +/- 12% in the strength group. The decrease in peak force and power across repetitions, and the rate of force decrease during maximal voluntary contraction per set, were significantly smaller for the endurance than for the strength group. The new spectral EMG indices effectively discriminated between strength and endurance athletes, thus providing a useful functional index that could be applied to track training adaptations as well as potentially talent identification.

Muscle fatigue during dynamic contractions assessed by new spectral indices

PURPOSE

The aim of the present study was to test the applicability and sensitivity of new electromyography (EMG) spectral indices in assessing peripheral muscle fatigue during dynamic knee-extension exercise.

METHODS

Seven subjects completed 10 sets of 15 repetitions of right knee-extension exercise lifting 50% of their one-repetition maximum. Torque (T), knee-joint angle, and the interference EMG of rectus femoris muscle were recorded simultaneously. Maximal voluntary isometric contraction (MVC) was tested before and after exercise. Median spectral frequency (Fmed) and new spectral indices of muscle fatigue (FInsmk) were calculated for each repetition.

RESULTS

The rate and range of FInsmk- and Fmed-relative changes against the first repetition of the corresponding set increased gradually across successive repetitions within the set, reflecting accumulation of peripheral muscle fatigue. The maximal change of FInsmk observed in the present experiment was approximately eightfold, whereas that of Fmed was only 32%. Significant between-subject variability in the range of FInsmk changes (P < 0.0001) was found, so a hierarchical cluster analysis of muscle fatigue indices was conducted. Three distinct subgroups of subjects were identified: high (N = 1, FInsmk change > 400%), medium (N = 4, 200% < FInsmk change < 400%), and low (N = 2, FInsmk change < 200%) muscle fatigability. The changes in muscle performance during (last vs first repetition peak T, P = 0.03) and after (post- vs preexercise MVC, P = 0.012) exercise were significantly different between clusters (one-way ANOVA). The rate of fatigue development was also significantly different between clusters (linear regression analysis of Fmed and FInsmk changes).

CONCLUSIONS

The new spectral indices are a valid and reliable tool for assessment of muscle fatigability irrespective of EMG signal variability caused by dynamic muscle contractions, and these indices are more sensitive than those traditionally used.

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.

Simulation analysis of interference EMG during fatiguing voluntary contractions. Part II--changes in amplitude and spectral characteristics

Capabilities of amplitude and spectral methods for information extraction from interference EMG signals were assessed through simulation and preliminary experiment. Muscle was composed of 4 types of motor units (MUs). Different hypotheses on changes in firing frequency of individual MUs, intracellular action potential (IAP) and muscle fibre propagation velocity (MFPV) during fatigue were analyzed. It was found that changes in amplitude characteristics of interference signals (root mean square, RMS, or integrated rectified value, IEMG) detected by intramuscular and surface electrodes differed. RMS and IEMG of surface detected interference signals could increase even under MU firing rate reduction and without MU synchronisation. IAP profile lengthening can affect amplitude characteristics more significantly than MU firing frequency. Thus, an increase of interference EMG amplitude is unreliable to reflect changes in the neural drive. The ratio between EMG amplitude and contraction response can hardly characterise the so-called 'neuromuscular efficiency'. The recently proposed spectral fatigue indices can be used for quantification of interference EMG signals. The indices are practically insensitive to MU firing frequency. IAP profile lengthening and decrease in MFPV enhanced the index value, while recruitment of fast fatigable MUs reduced it. Sensitivity of the indices was higher than that of indices traditionally used.

Estimate of M-wave changes in human biceps brachii during continuous stimulation

The purpose of the present study was to validate the capability of new fatigue indexes (in the time and frequency domain) applied to experimental recordings and thus, to test some assumptions made in previous simulations. The indexes were applied to M-waves detected non-invasively from human m.biceps brachii during repetitive slightly above threshold stimulations. It was found that distance between the motor point and middle of the end-plate region could be relatively large. Under identical conditions (signals detected by monopolar electrodes and high-pass filtered at 1 Hz), the relative changes of the indexes obtained in electrophysiological experiments and simulations were similar. Changes of the intracellular action potential profile during fatigue used in the simulations were consequently supposed to be close to the actual ones for the muscle analyzed. When the high-pass cut-off frequency was higher than 1 Hz, the sensitivity of the index in the time domain was higher, while that in the frequency domain was lower. If the normalizing spectral moment was of higher order, the sensitivity of the spectral index could be even 150-times greater than that of the fatigue indexes traditionally used. Thus, the spectral index promises high capability to assess fatigue during functional electrical stimulation.

Simulation analysis of the performance of a novel high sensitive spectral index for quantifying M-wave changes during fatigue

A high sensitive fatigue index is desired to improve stimulation strategy and to prevent muscle damage in functional electrical simulations. The great number of indexes used shows that there is no index that satisfies all investigators. A way to develop a high sensitive index for quantifying M-wave changes during fatigue and to estimate its performance was analyzed. The changes in M-wave and its frequency distribution due to variations of intracellular action potential (IAP) and muscle fibre propagation velocity (MFPV) with fatigue were simulated. It was found that the ratio between the spectral moments of order -1 and 2 was considerably more sensitive to peripheral muscle fatigue than the mean (the ratio between the spectral moments of order 1 and 0) and median frequency traditionally used. The sensitivity of the new index depended on the electrode arrangement and position in respect to the active fibres. The belly-tendon detection promised the highest index sensitivity. The length of the active fibres also affected the index sensitivity. The shorter the fibres the lower was the index sensitivity. The sensitivity of the new index could be relatively high even in the case of traditionally used high-pass cut-off frequencies that could distort the M-wave shape.

Mathematical models for fatigue minimization during functional electrical stimulation

We previously reported the development of a force- and fatigue-model system that predicted accurately forces during repetitive fatiguing activation of human skeletal muscles using brief duration (six-pulse) stimulation trains. The model system was tested in the present study using force responses produced by longer duration stimulation trains, containing up to 50 pulses. Our results showed that our model successfully predicted the peak forces produced when the muscle was repetitively activated with stimulation trains of frequencies ranging from 20 to 40 Hz, train durations ranging from 0.5 to 1 s, and varied pulse patterns. The predicted peak forces throughout each protocol matched the experimental peak forces with r2 values above 0.9 and predicted successfully the forces at the end of each protocol with <15% error for all protocols tested. The success of our model system further supports its potential use for the design of optimal stimulation patterns for individual users during functional electrical stimulation.

A predictive fatigue model--I: Predicting the effect of stimulation frequency and pattern on fatigue

Previously we developed a mathematical force- and fatigue-model system that could predict fatigue produced by a wide range of frequencies and pulse patterns. However, the models tended to overestimate the forces produced by higher frequency trains. This paper presents modifications to our previously developed force- and fatigue-model system to improve the accuracy in predicting forces during repetitive activation of human skeletal muscle. By comparing the predictions produced by the modified force and fatigue models to those by our previous models, the modification appears to be successful. The current force- and fatigue-model system accounts for about 93% variance in experimental data produced by fatigue protocols consisting of trains with a wide range of frequencies and pulse patterns. In addition, the present models successfully predict the effect of stimulation frequency and pulse pattern on muscle fatigue. The success of our current force- and fatigue-model system suggests its potential use in helping to identify the optimal activation pattern to use during the clinical application of functional electrical stimulation.

A predictive fatigue model--II: Predicting the effect of resting times on fatigue

We have recently developed a force- and fatigue-model system that accurately predicted the effect of stimulation frequency on muscle fatigue. The data used to test the model were produced by stimulation trains with resting times of 500 ms. Because the resting times between stimulation trains affect muscle fatigue, this study tested the model's ability to predict the effect of resting times on fatigue. In addition, because this study included different subjects than those used to develop the model, the validity of the model could be tested. Data were collected from human quadriceps femoris muscles using fatigue protocols that included resting times of 500, 750, or 1000 ms. Our results showed that the model predicted fatigue as being a decreasing function of resting time, which was consistent with experimental data. Reliability tests between the experimental data and predictions showed interclass correlation coefficients of 0.97, 0.95, and 0.81 for the initial, final, and percentage decline in peak forces, respectively, suggesting strong agreement between the experimental data and the predictions by the model. The success of our current force- and fatigue-model system helps to validate the model and suggests its potential use in identifying the optimal activation pattern during clinical application of functional electrical stimulation.

A predictive model of fatigue in human skeletal muscles

Fatigue is a major limitation to the clinical application of functional electrical stimulation. The activation pattern used during electrical stimulation affects force and fatigue. Identifying the activation pattern that produces the greatest force and least fatigue for each patient is, therefore, of great importance. Mathematical models that predict muscle forces and fatigue produced by a wide range of stimulation patterns would facilitate the search for optimal patterns. Previously, we developed a mathematical isometric force model that successfully identified the stimulation patterns that produced the greatest forces from healthy subjects under nonfatigue and fatigue conditions. The present study introduces a four-parameter fatigue model, coupled with the force model that predicts the fatigue induced by different stimulation patterns on different days during isometric contractions. This fatigue model accounted for 90% of the variability in forces produced by different fatigue tests. The predicted forces at the end of fatigue testing differed from those observed by only 9%. This model demonstrates the potential for predicting muscle fatigue in response to a wide range of stimulation patterns.

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.

Dynamic force responses in electrically stimulated triceps surae muscles: effects of fatigue and temperature

To elicit dynamic force responses (unfused tetani) in isometric triceps surae muscles, low frequency electrical stimulation ranging from 12.5 to 30.0 Hz was applied. The fusing frequency (FF) and the relative dynamic force amplitude (DF) at the 20% and 40% maximum voluntary contraction (MVC) levels were calculated as parameters to determine effects of muscle fatigue (n = 6) and local muscle cooling. In the fatigued muscle (15 min plantar flexion at a 20% MVC level), the FF and DF increased when the fatigue was induced by voluntary contraction (FF increased from 19.6 to 22.5 Hz at 20% MVC) and also when induced by electrical stimulation (FF increased from 19.2 to 23.3 Hz). Cooling of the muscles showed an inverse effect on both parameters, indicating contractile slowing. The responsible physiological mechanisms as well as practical applications, using low frequency stimulation to monitor degenerative changes in muscles, are discussed.