Wavelet and short-time Fourier transform analysis of electromyography for detection of back muscle fatigue

EEG driving fatigue detection based on log-Mel spectrogram and convolutional recurrent neural networks

Driver fatigue detection is one of the essential tools to reduce accidents and improve traffic safety. Its main challenge lies in the problem of how to identify the driver's fatigue state accurately. Existing detection methods include yawning and blinking based on facial expressions and physiological signals. Still, lighting and the environment affect the detection results based on facial expressions. In contrast, the electroencephalographic (EEG) signal is a physiological signal that directly responds to the human mental state, thus reducing the impact on the detection results. This paper proposes a log-Mel spectrogram and Convolution Recurrent Neural Network (CRNN) model based on EEG to implement driver fatigue detection. This structure allows the advantages of the different networks to be exploited to overcome the disadvantages of using them individually. The process is as follows: first, the original EEG signal is subjected to a one-dimensional convolution method to achieve a Short Time Fourier Transform (STFT) and passed through a Mel filter bank to obtain a logarithmic Mel spectrogram, and then the resulting logarithmic Mel spectrogram is fed into a fatigue detection model to complete the fatigue detection task for the EEG signals. The fatigue detection model consists of a 6-layer convolutional neural network (CNN), bi-directional recurrent neural networks (Bi-RNNs), and a classifier. In the modeling phase, spectrogram features are transported to the 6-layer CNN to automatically learn high-level features, thereby extracting temporal features in the bi-directional RNN to obtain spectrogram-temporal information. Finally, the alert or fatigue state is obtained by a classifier consisting of a fully connected layer, a ReLU activation function, and a softmax function. Experiments were conducted on publicly available datasets in this study. The results show that the method can accurately distinguish between alert and fatigue states with high stability. In addition, the performance of four existing methods was compared with the results of the proposed method, all of which showed that the proposed method could achieve the best results so far.

Variability of Time- and Frequency-Domain Surface Electromyographic Measures in Non-Fatigued Shoulder Muscles

OCCUPATIONAL APPLICATIONSLocalized Muscle Fatigue (LMF) can be monitored or predicted based on the relative change in the values of surface electromyography (sEMG) measures with respect to the "fresh" or no-fatigue condition. Quantification of LMF based on relative change, though, relies on the assumption that the sEMG measures recorded in a no-fatigue condition can serve as an appropriate reference. Results of this study indicate that sEMG measures in a no-fatigue condition are affected by various work-related factors and provide further guidance on the variability of commonly used time- and frequency-domain sEMG measures to assist the ergonomist in improving the accuracy of LMF assessment.

Targeted Walking in Incomplete Spinal Cord Injury: Role of Corticospinal Control

Locomotor recovery after incomplete spinal cord injury (iSCI) is influenced by spinal and supraspinal networks. Conventional clinical gait analysis fails to differentiate between these components. There is evidence that corticospinal control is enhanced during targeted walking, where each foot must be continuously placed on visual targets in randomized order. This study investigates the potential of targeted walking in the functional assessment of corticospinal integrity. Twenty-one controls and 16 individuals with chronic iSCI performed normal and targeted walking on a treadmill while electromyograms (EMGs) and kinematics were recorded. Precision (% of accurate foot placements) in targeted walking was significantly lower in individuals with iSCI (82.9 ± 14.7%, controls: 94.9 ± 4.0%). Although the overall kinematic pattern was comparable between walking conditions, controls showed significantly higher semitendinosus (ST) activity before heel-strike during targeted walking. This was accompanied by a shift of relative EMG intensity from 90-120 Hz to lower frequencies of 20-60 Hz, previously associated with corticospinal control of muscle activity. Targeted walking in individuals with iSCI evoked smaller EMG changes, suggesting that the switch to more corticospinal control is impaired. Accordingly, mildly impaired iSCI individuals revealed higher adaptations to the targeted walking task than more-impaired individuals. Recording of EMGs during targeted walking holds potential as a research tool to reveal further insights into the neuromuscular control of locomotion. It also complements findings of pre-clinical studies and is a promising novel surrogate marker of integrity of corticospinal control in individuals with iSCI and other neurological impairments. Future studies should investigate its potential for diagnosis or tracking recovery during rehabilitation.

Complexity Analysis of Surface Electromyography for Assessing the Myoelectric Manifestation of Muscle Fatigue: A Review

The surface electromyography (sEMG) records the electrical activity of muscle fibers during contraction: one of its uses is to assess changes taking place within muscles in the course of a fatiguing contraction to provide insights into our understanding of muscle fatigue in training protocols and rehabilitation medicine. Until recently, these myoelectric manifestations of muscle fatigue (MMF) have been assessed essentially by linear sEMG analyses. However, sEMG shows a complex behavior, due to many concurrent factors. Therefore, in the last years, complexity-based methods have been tentatively applied to the sEMG signal to better individuate the MMF onset during sustained contractions. In this review, after describing concisely the traditional linear methods employed to assess MMF we present the complexity methods used for sEMG analysis based on an extensive literature search. We show that some of these indices, like those derived from recurrence plots, from entropy or fractal analysis, can detect MMF efficiently. However, we also show that more work remains to be done to compare the complexity indices in terms of reliability and sensibility; to optimize the choice of embedding dimension, time delay and threshold distance in reconstructing the phase space; and to elucidate the relationship between complexity estimators and the physiologic phenomena underlying the onset of MMF in exercising muscles.

Pedaling Performance Changing of Elite Cyclists Is Mainly Determined by the Fatigue of Hamstring and Vastus Muscles during Repeated Sprint Cycling Exercise

Repeated sprint cycling is an effective training method in promoting athletic performance of cyclists, which may induce severe fatigue of lower limb muscles. However, the relationship between the fatigue of each lower limb muscles and the changing of exercise performance remains unclear. In this study, ten cyclist volunteers performed a series of 6-second sprints with 24-s recovery for five times. Power, cadence, and EMG mean frequency (MNF) of each lower limb muscle group for every 2-second epoch, as well as the grey relational grade between exercise performance and MNF of each lower limb muscle group during the whole process were calculated. It has been found that MNF of Rectus femoris (RF), Vastus (VAS), Gastrocnemius (GAS), and the hamstring muscle group (HAM) showed significant negative correlation with the increase in both sprint number and intrasprint duration time, while the grey relational grade of HAM and VAS was higher than that of other muscles. The results demonstrated that the exercise performance of both power and cadence were most closing related to the fatigue degree of HAM and VAS during repeated sprint cycling exercise.

Effects of wearable power assist device on low back fatigue during repetitive lifting tasks

BACKGROUND

A wearable power assist device was developed to reduce the stress on the lower back by using pneumatic muscles. The purpose of this study was to explore whether the assist device could reduce the activity or fatigue of lower back muscles during a repetitive lifting task.

METHODS

Twelve male subjects participated in the study. Electromyography of the thoracic erector spinae at the T9 level and lumbar erector spinae at the L3 level was recorded during 90 lifts in 15 min. Subjects' heart rate and Borg's Rate of Perceived Exertion Scale score were recorded during lifting sessions.

FINDINGS

The electromyography amplitude of thoracic erector spinae and lumbar erector spinae was only increased by 32.45% and 40.17%, respectively, when the wearable power assist device was used when comparing the pre- and post-lifting task. Whereas it was increased by 125.78% and 85.90%, respectively, when the wearable power assist device was not used. The decrease in electromyography median frequency from the start until the end of the lifting session was significantly lower when wearing the assist device for the thoracic erector spinae (2.72% vs 7.45%) and the lumbar erector spinae (3.91% vs 13.70%). Use of the assist device also significantly reduced the percentage change in heart rate and Borg Scale (p < 0.05).

INTERPRETATION

The use of the wearable power assist device showed less back muscle contraction compared to the no-use, which can potentially minimize the level of back muscle fatigue across the lifting session.

Weighted-Cumulated S-EMG Muscle Fatigue Estimator

This paper addresses a new approach to objectively evaluate muscle fatigue in isometric and dynamic physical exertions using surface electromyography (S-EMG). The emphasis of this proposal is to preserve the spectral signature of the muscle fatigue phenomenon while reducing the spatial effects of electrode localization, and decreasing the disparity of results obtained by the same experimental protocol at different times. A cumulated and normalized modeling was sought to make evident the nonstationary characteristics of muscle fatigue that is gradually identified with its inertia and intensity. A metric involving the proposal of temporal, frequency, and time-frequency weighted-cumulated indicators is presented. Results based on real signals are shown for isometric and dynamic experimental protocols. Performance comparison of the various proposed weighted-cumulated indexes is shown and discussed. The presented approach for the objective cumulative evaluation of muscle fatigue with S-EMG signals has shown to be promising.

Control of Prosthetic Hands via the Peripheral Nervous System

This paper intends to provide a critical review of the literature on the technological issues on control and sensorization of hand prostheses interfacing with the Peripheral Nervous System (i.e., PNS), and their experimental validation on amputees. The study opens with an in-depth analysis of control solutions and sensorization features of research and commercially available prosthetic hands. Pros and cons of adopted technologies, signal processing techniques and motion control solutions are investigated. Special emphasis is then dedicated to the recent studies on the restoration of tactile perception in amputees through neural interfaces. The paper finally proposes a number of suggestions for designing the prosthetic system able to re-establish a bidirectional communication with the PNS and foster the prosthesis natural control.

Assessment of early onset of driver fatigue using multimodal fatigue measures in a static simulator

Driver fatigue is an important contributor to road accidents. This paper reports a study that evaluated driver fatigue using multimodal fatigue measures, i.e., surface electromyography (sEMG), electroencephalography (EEG), seat interface pressure, blood pressure, heart rate and oxygen saturation level. Twenty male participants volunteered in this study by performing 60 min of driving on a static simulator. Results from sEMG showed significant physical fatigue (ρ < 0.05) in back and shoulder muscle groups. EEG showed significant (ρ < 0.05) increase of alpha and theta activities and a significant decrease of beta activity during monotonous driving. Results also showed significant change in bilateral pressure distribution on thigh and buttocks region during the study. These findings demonstrate the use of multimodal measures to assess early onset of fatigue. This will help us understand the influence of physical and mental fatigue on driver during monotonous driving.

Fluctuating emg signals: investigating long-term effects of pattern matching algorithms

In this paper, we investigate the behavior of state-of-the-art pattern matching algorithms when applied to electromyographic data recorded during 21 days. To this end, we compare the five classification techniques k-nearest-neighbor, linear discriminant analysis, decision trees, artificial neural networks and support vector machines. We provide all classifiers with features extracted from electromyographic signals taken from forearm muscle contractions, and try to recognize ten different hand movements. The major result of our investigation is that the classification accuracy of initially trained pattern matching algorithms might degrade on subsequent data indicating variations in the electromyographic signals over time.

Endurance time is joint-specific: a modelling and meta-analysis investigation

Static task intensity-endurance time (ET) relationships (e.g. Rohmert's curve) were first reported decades ago. However, a comprehensive meta-analysis to compare experimentally-observed ETs across bodily regions has not been reported. We performed a systematic literature review of ETs for static contractions, developed joint-specific power and exponential models of the intensity-ET relationships, and compared these models between each joint (ankle, trunk, hand/grip, elbow, knee, and shoulder) and the pooled data (generalised curve). 194 publications were found, representing a total of 369 data points. The power model provided the best fit to the experimental data. Significant intensity-dependent ET differences were predicted between each pair of joints. Overall, the ankle was most fatigue-resistant, followed by the trunk, hand/grip, elbow, knee and finally the shoulder was most fatigable. We conclude ET varies systematically between joints, in some cases with large effect sizes. Thus, a single generalised ET model does not adequately represent fatigue across joints. STATEMENT OF RELEVANCE: Rohmert curves have been used in ergonomic analyses of fatigue, as there are limited tools available to accurately predict force decrements. This study provides updated endurance time-intensity curves using a large meta-analysis of fatigue data. Specific models derived for five distinct joint regions should further increase prediction accuracy.

Simultaneous measurement of force and muscle fatigue using frequency-band wavelet analysis

Static and dynamic handgrip experiments are performed in order to evaluate the effectiveness of utilizing frequency-band wavelet analysis in measuring force and muscle fatigue simultaneously. SEMG signals are recorded from flexor muscle and analyzed using continuous wavelet transform (CWT). The wavelet coefficients are grouped into high frequency (65Hz - 350Hz) and low frequency (5Hz - 45Hz) band. A significant correlation is discovered between amplitude of high frequency band and force level. On the other hand, the amplitude of low frequency band is associated with muscle fatigue. These results have an important implication for estimating force and muscle fatigue simultaneously especially during dynamic contraction.

The application of mutual information-based feature selection and fuzzy LS-SVM-based classifier in motion classification

This paper presents an effective mutual information-based feature selection approach for EMG-based motion classification task. The wavelet packet transform (WPT) is exploited to decompose the four-class motion EMG signals to the successive and non-overlapped sub-bands. The energy characteristic of each sub-band is adopted to construct the initial full feature set. For reducing the computation complexity, mutual information (MI) theory is utilized to get the reduction feature set without compromising classification accuracy. Compared with the extensively used feature reduction methods such as principal component analysis (PCA), sequential forward selection (SFS) and backward elimination (BE) etc., the comparison experiments demonstrate its superiority in terms of time-consuming and classification accuracy. The proposed strategy of feature extraction and reduction is a kind of filter-based algorithms which is independent of the classifier design. Considering the classification performance will vary with the different classifiers, we make the comparison between the fuzzy least squares support vector machines (LS-SVMs) and the conventional widely used neural network classifier. In the further study, our experiments prove that the combination of MI-based feature selection and SVM techniques outperforms other commonly used combination, for example, the PCA and NN. The experiment results show that the diverse motions can be identified with high accuracy by the combination of MI-based feature selection and SVM techniques. Compared with the combination of PCA-based feature selection and the classical Neural Network classifier, superior performance of the proposed classification scheme illustrates the potential of the SVM techniques combined with WPT and MI in EMG motion classification.

Fatigue-induced changes in muscle fiber action potentials estimated by wavelet analysis

We aimed to investigate fatigue-induced changes in the spectral parameters of slow (SMF) and fast fatigable muscle fiber (FMF) action potentials using discrete wavelet (DWT) and fast Fourier (FFT) transforms. Intracellular potentials were recorded during repetitive stimulation of isolated muscle fibers immersed in Ca(2+)-enriched medium, while extracellular potentials were obtained from muscle fibers pre-exposed to electromagnetic microwaves (MMW, 2.45 GHz, 20 mW/cm(2)). The changes in the frequency distribution of the action potentials during the period of uninterrupted fiber activity were used as criteria for fatigue assessment. The wavelet coefficients' changes in the calculated frequency scales demonstrated a contribution of the increased [Ca(2+)](0) to an earlier compression of the frequency spectrum towards lower ranges. Root mean square (RMS) analysis of the wavelet coefficients calculated from SMF potentials showed a reduction of the higher frequencies (scale 1) by 90% in elevated [Ca(2+)](0) vs. 55% in controls and an increase of low frequencies (scale 5) by 323% vs. 187%, respectively. For FMF potentials a decrease of 71% vs. 59% for high frequencies (scale 1, elevated [Ca(2+)](0) vs. control) and an increase of 386% vs. 295% in scale 5, respectively, were observed. MMW pre-exposure resulted in increased muscle fiber resistance to fatigue. The fatigue-induced decrease of potential high frequencies (SMF: 59% vs. 96%, MMW vs. control; FMF: 30% vs. 92%, respectively), and the increase of low frequencies (SMF: 200% vs. 207%, MMW vs. control; FMF: 93% vs. 314%, respectively) were significantly smaller and delayed in exposed muscle fibers. Data from RMS analysis indicate that DWT provides a reliable method for estimation of muscle fatigue onset and progression.

Joint application of rough set-based feature reduction and Fuzzy LS-SVM classifier in motion classification

This paper presents an effective classification scheme consisting of the rough set theory (RST)-based feature selection and the fuzzy least squares support vector machine (LS-SVM) classifier for the surface electromyographic (sEMG)-based motion classification. The wavelet packet transform (WPT) is exploited to decompose the four-class motion EMG signals to the non-overlapped sub-bands and the energy characteristic of each sub-band is adopted to form the original feature set. In order to reduce the computation complexity, the RST is utilized to get the reduction feature set without compromising classification accuracy. In the feature reduction phase, cluster separation index (CSI) is introduced to evaluate the performance of the proposed algorithm. In the sequel, the Fuzzy LS-SVM is constructed for the multi-class classification task. The RST-based feature selection is independent of the classifier design. Consequently the classification performance will vary with different classifiers. We make the comparison between the proposed classification scheme and the commonly used classification scheme, such as the combination of the principal component analysis (PCA)-based feature selection and the neural network (NN) classifier. The results of comparative experiments show that the diverse motions can be identified with high accuracy by the proposed scheme. Compared with other feature extraction and selection algorithms and classifiers, superior performance of the proposed classification scheme illustrates the potential of the SVM techniques combined with WPT and RST in EMG motion classification.

Introducing a new multi-wavelet function suitable for sEMG signal to identify hand motion commands

In recent years, electromyogram signal (EMG) feature selection, based on wavelet transform, has received considerable attention. This study introduces a new multi-wavelet function for surface EMG (sEMG) signal intended for tasks that involve hand movement recognition. To create the new wavelet function, several types of well known mother wavelet were exploited and through their integration the proposed mother wavelet was generated. The proposed wavelet function closely reproduced the characteristics of the EMG signal, while increasing the recognition accuracy of hand movements. We used eight unique classes of hand motions and considered the ability of various mother wavelets and the proposed multi-wavelet to recognize these movements. Furthermore, we used local extrema and zero crossing (ZC) as DWT features. The results demonstrate that the proposed multi-wavelet function provides 87% recognition mark compared to 78%, the best performance that any other mother wavelet was able to achieve.

Frequency domain analysis to identify neurological disorders from evoked EMG responses

Evoked EMG M-responses obtained from the thenar muscle in the palm by electrical stimulation of the median nerve demonstrate a well-established smooth bipolar shape for normal healthy subjects. Kinks in this curve are observed in certain neurological disorders and preliminary work suggests their relationship to cervical spondylosis. The present work was taken up to develop an objective method for the identification of such neurological disorders for automated diagnosis by analysing the M-responses. A Fourier transform was performed using MATLAB, and features in the frequency domain were studied to distinguish healthy and smooth M-responses from ones with kinks. The features included some basic parameters like peak amplitude, peak frequency, frequency bandwidths, and areas in specified frequency segments. Ratio and deviation parameters from the above basic parameters were also studied to make 39 parameters in all. Out of these 10 came out as 'highly significant', 17 as 'significant' and the rest as insignificant, in statistical t-tests. A weighted combination of the significant parameters may allow identification of kinks with confidence.

Characterization of surface EMG signals using improved approximate entropy

An improved approximate entropy (ApEn) is presented and applied to characterize surface electromyography (sEMG) signals. In most previous experiments using nonlinear dynamic analysis, this certain processing was often confronted with the problem of insufficient data points and noisy circumstances, which led to unsatisfactory results. Compared with fractal dimension as well as the standard ApEn, the improved ApEn can extract information underlying sEMG signals more efficiently and accurately. The method introduced here can also be applied to other medium-sized and noisy physiological signals.

A critical review of interfaces with the peripheral nervous system for the control of neuroprostheses and hybrid bionic systems

Considerable scientific and technological efforts have been devoted to develop neuroprostheses and hybrid bionic systems that link the human nervous system with electronic or robotic prostheses, with the main aim of restoring motor and sensory functions in disabled patients. A number of neuroprostheses use interfaces with peripheral nerves or muscles for neuromuscular stimulation and signal recording. Herein, we provide a critical overview of the peripheral interfaces available and trace their use from research to clinical application in controlling artificial and robotic prostheses. The first section reviews the different types of non-invasive and invasive electrodes, which include surface and muscular electrodes that can record EMG signals from and stimulate the underlying or implanted muscles. Extraneural electrodes, such as cuff and epineurial electrodes, provide simultaneous interface with many axons in the nerve, whereas intrafascicular, penetrating, and regenerative electrodes may contact small groups of axons within a nerve fascicle. Biological, technological, and material science issues are also reviewed relative to the problems of electrode design and tissue injury. The last section reviews different strategies for the use of information recorded from peripheral interfaces and the current state of control neuroprostheses and hybrid bionic systems.

Analysis of week-to-week variability in skin blood flow measurements using wavelet transforms

The study of skin blood flow responses is confounded by temporal variability in blood flow measurements. Spectral analysis has been shown useful in isolating the effects of distinct control mechanisms on various stimuli in the microcirculatory system. However, the sensitivity of spectral analysis to temporal blood blow variability has not been reported. This study was designed to assess week-to-week variability in blood flow measurements using wavelet-based spectrum analysis. Ten healthy, young subjects (mean age+/-SD, 30.0+/-3.1 years) were recruited into the study. Incremental heating (35-45 degrees C, 1 degrees step min-1) was applied on the skin over the sacrum once per week for three consecutive weeks. Wavelet analysis was used to decompose the laser Doppler blood flow signal into frequency bands determined to be associated with endothelial nitric oxide (0.008-0.02 Hz), neurogenic (0.02-0.05 Hz), myogenic (0.05-0.15 Hz), respiratory (0.15-0.4 Hz), and cardiac (0.4-2.0 Hz) control mechanisms. The results showed that coefficients of variation for the power in each frequency band at baseline are smaller than the coefficients of variation of blood flow at baseline or at maximal blood flow ratio (P<0.05). Myogenic and respiratory frequency bands showed the highest coefficients of variation among the five frequency bands. An increase in power in the endothelial nitric oxide frequency band and a decrease in power in the myogenic frequency band of the maximal blood flow response were reproduced in three consecutive weeks. Our study suggests that wavelet analysis is an effective method to overcome temporal variability in skin blood flow measurements.

Using EMG to Anticipate Head Motion for Virtual-Environment Applications

In virtual environment (VE) applications, where virtual objects are presented in a see-through head-mounted display, virtual images must be continuously stabilized in space in response to user's head motion. Time delays in head-motion compensation cause virtual objects to "swim" around instead of being stable in space which results in misalignment errors when overlaying virtual and real objects. Visual update delays are a critical technical obstacle for implementing head-mounted displays in applications such as battlefield simulation/training, telerobotics, and telemedicine. Head motion is currently measurable by a head-mounted 6-degrees-of-freedom inertial measurement unit. However, even given this information, overall VE-system latencies cannot be reduced under about 25 ms. We present a novel approach to eliminating latencies, which is premised on the fact that myoelectric signals from a muscle precede its exertion of force, thereby limb or head acceleration. We thus suggest utilizing neck-muscles' myoelectric signals to anticipate head motion. We trained a neural network to map such signals onto equivalent time-advanced inertial outputs. The resulting network can achieve time advances of up to 70 ms.

Determination of the effectiveness of materials in attenuating high frequency shock during gait using filterbank analysis

OBJECTIVE

To develop an accurate method for quantifying the frequency content of the ground reaction force transient.

DESIGN

Repeated measures design comparing the impact severity during walking with different insole materials.

BACKGROUND

The body experiences a brief but sizeable impact upon heel strike during walking. This impact transient is believed to result in musculoskeletal injuries. It is important to accurately quantify this impact as a step towards decreasing the risk of injury.

METHODS

Seven subjects walked barefoot at their normal cadence across a force platform, while insole materials (Spenco, Microcel-puff, and Plastazote) were placed on the surface of the force platform. A filterbank program was developed to determine the percent root mean square in 10 Hz frequency bands from zero to 400 Hz. Analysis focused on the impact transient contained in a 20 ms window after heel contact.

RESULTS

The high frequency (>60 Hz) power was significantly larger in the barefoot condition compared to the insole conditions. The barefoot condition also resulted in significantly higher initial peak forces and force loading rates.

CONCLUSIONS

The frequency content of the ground reaction force can be effectively quantified using a filterbank approach. Shoe insole materials can reduce the initial peak force, force loading rate, and frequency content of the impact transient in walking. The frequency content of the initial ground reaction force extends up to 400 Hz in some footwear conditions.

RELEVANCE

The new filterbank procedure illustrates that the vertical ground reaction force in walking has a higher frequency content than previously thought. This signal requires high sampling rates to avoid aliasing, and appropriate signal processing algorithms, such as filter banks, for analysis.

Changes in the surface EMG signal and the biomechanics of motion during a repetitive lifting task

The analysis of surface electromyographic (EMG) data recorded from the muscles of the back during isometric constant-force contractions has been a useful tool for assessing muscle deficits in patients with lower back pain (LBP). Until recently, extending the technique to dynamic tasks, such as lifting, has not been possible due to the nonstationarity of the EMG signals. Recent developments in time-frequency analysis procedures to compute the instantaneous median frequency (IMDF) were utilized in this study to overcome these limitations. Healthy control subjects with no history of LBP (n = 9; mean age 26.3 +/- 6.7) were instrumented for acquisition of surface EMG data from six electrodes on the thoraco-lumbar region and whole-body kinematic data from a stereo-photogrammetric system. Data were recorded during a standardized repetitive lifting task (load = 15% body mass; 12 lifts/min; 5-min duration). The task resulted in significant decreases in IMDF for six of the nine subjects, with a symmetrical pattern of fatigue among contralateral muscles and greater decrements in the lower lumbar region. For those subjects with a significant decrease in IMDF, a lower limb and/or upper limb biomechanical adaptation to fatigue was observed during the task. Increases in the peak box acceleration were documented. In two subjects, the acceleration doubled its value from the beginning to the end of the exercise, which lead to a significant increase in the torque at L4/L5. This observation suggests an association between muscle fatigue at the lumbar region and the way the subject manipulates the box during the exercise. Fatigue-related biomechanical adaptations are discussed as a possible supplement to functional capacity assessments among patients with LBP.

Generalizability of trunk muscle EMG and spinal forces

The generalizability of trunk muscle EMG and spinal loading estimates obtained from an EMG-assisted biomechanical model was assessed over three occasions and three repetitions. The greatest sources of variability consisted of the intersubject differences and the interaction between subject and occasion. The ID (reliability coefficient) was less for trunk muscle activity compared with estimates of anteroposterior shear force, compression force, and gain computed from the biomechanical model. In order to obtain an ID of 0.8, we recommend five testing occasions for submaximal EMG measurements and three testing occasions for biomechanical estimates. Reproducible estimates of maximal trunk extensor EMG could not be obtained within five testing occasions and five repetitions. Although many recruitment patterns could cause the same extension torque output, their net effect on internal loading seems to be less variable than the underlying measurements.