The standard deviation of surface EMG (EMGσ) is often related to muscle force; the accuracy of EMGσ estimation is valuable for many application areas such as clinical biomechanics, prostheses control and sports medicine. Numerous researchers have developed methods to optimize EMGσ estimation. Whitening the EMG signal has been proved to improve the statistical efficiency of EMGσ estimation, but conventional linear whitening filters fail at low contraction level. An adaptive whitening filter was proposed by Clancy and Farry[14]. This technique has a better performance than prior whitening methods, however, the adaptive whitening filter needs to be calibrated each time electrodes are applied, which increase the complexity of the implementation. We designed a universal whitening filter which can omit most calibration steps for the adaptive whitening filter in future use. We used the ensemble mean of the power spectrum of 512 EMG recordings to form a general shape of a fixed whitening filter that can applied on any EMG signal. The test error on an EMG-torque task based on universal whitening over 512 subjects had a mean error of 4.80% maximum voluntary contraction (MVC) and standard deviation (std) of 2.03% MVC, compared with an original adaptive whitening filter error of 4.84±1.98% MVC. Additionally, the rest contraction modeling hasn’t received enough attention. Existing RMS estimates of EMGσ subtract noise in either the amplitude or power domain. These procedures have never been modeled analytically. We show that power domain noise subtraction is optimal. But rest contractions which are processed using power domain noise subtraction only estimate a zero-valued EMGσ approximately 50% of the time, which is undesirable in prosthesis-control. The prosthesis has a 50% possibility to slowly drift based on the current RMS estimator. We propose a new estimator to improve the zero-amplitude estimation probability during rest. We used 512 rest contraction recordings to validate the probability distribution of rest EMG signal showing that it only has 1.6% difference compared with Gaussian distribution. We also evaluated the percent of zero-valued EMGσ estimates using power domain noise subtraction and our new estimator, matching experimental findings to the theoretic predictions.

The delayed occurrence of fatigue while maintaining submaximal force output is a function that could be driven by the central nervous system (CNS). It has been found previously that mean EMG amplitude increases with fatigue. Endurance time has also been found to increase over repeated testing. The purpose of this study was to compare the muscle activation patterns and endurance times after training of the AdP muscle. This study analyzed surface EMG of the adductor pollicis (AdP) muscle in young, healthy adults during a sustained submaximal isometric fatiguing contraction before and after 4 weeks of muscular endurance task training. Eight participants (training group: n = 4 and control group: n = 4) carried out maximal voluntary contractions (MVCs) while sustaining isometric force of 20% MVC of thumb adduction before and after the four weeks of endurance training. EMG, recorded through surface electrodes, was measured before and after training in an effort to detect a possible CNS training effect. The endurance training group trained the AdP muscle at 20% MVC every other day for 4 weeks. Average force was calculated over 5 second time bins every 5% of endurance time (20 time bins total). A significant increase in endurance time was seen in the training group of this study. A significant effect of change for pre and post-training mean EMG amplitude across the two groups was found (p < .001). A significant interaction effect between pre and post training and control groups was also found (p = .016). There was also a significant deficit in increases of mean amplitude between the first and last time bins of the endurance task (pre and post) after training. This indicates that there is an effect of training on increasing endurance time which can be exhibited through changes in mean EMG amplitude. / text

The electrical activity of skeletal muscle¡ªthe electromyogram (EMG)¡ªis of value to many different application areas, including ergonomics, clinical biomechanics and prosthesis control. For many applications the EMG is related to muscular tension, joint torque and/or applied forces. In these cases, a goal is for an EMG-torque model to emulate the natural relationship between the central nervous system and peripheral joints and muscles. This thesis mainly describes an experimental study which relates the simultaneous biceps/triceps surface EMG of 12 subjects to elbow torque at seven joint angles (ranging from 45¡ÃƒÂ£to 135¡ÃƒÂ£) during constant-posture, quasi-constant-torque contractions. The contractions ranged between 50% maximum voluntary contractions (MVC) extension and 50% MVC flexion. Advanced EMG amplitude (EMG¦Ãƒâ€™) estimation processors were investigated, and three nonlinear EMG¦Ãƒâ€™-torque models were evaluated. Results show that advanced (i.e., whitened, multiple-channel) EMG¦Ãƒâ€™ processors lead to improved joint torque estimation, compared to unwhitened, single-channel EMG¦Ãƒâ€™ processors. Depending on the joint angle, use of the multiple-channel whitened EMG¦Ãƒâ€™ processor with higher polynomial degrees produced a median error that was 50%-66% that found when using the single-channel, unwhitened EMG¦Ãƒâ€™ processor with a polynomial degree of 1. The best angle-specific model achieved a minimum error of 3.39% MVCF90 (i.e., error referenced to MVC at 90¢X flexion), yet it does not allow interpolation across angles. The best model which parameterizes the angle dependence achieved an error of 3.55% MVCF90. This thesis also summarizes other collaborative research contributions performed as part of this thesis. (1) Decomposition of needle EMG data was performed as part of a study to characterize motor unit behavior in patients with amyotrophic lateral sclerosis (ALS) [with Spaulding Rehabilitation Hospital, Boston, MA]. (2) EMG-force modeling of force produced at the finger tips was studied with the purpose of assessing the ability to determine two or more independent, continuous degrees of freedom of control from the muscles of the forearm [with WPI and Sherbrooke University]. (3) Identification of a nonlinear, dynamic EMG-torque relationship about the elbow was studied [WPI]. (4) Signal whitening preprocessing for improved classification accuracies in myoelectric control of a prosthesis was studied [with WPI and the University of New Brunswick].

Context: Knee injury is a common problem for runners. Knee pain is a common symptom in knee injury and is associated with alterations in knee and hip muscle activation and hip joint angles. Relieving pain through intervention may help to restore neuromuscular function. Objective: To examine the effects of ice and transcutaneous electrical nerve stimulation (TENS) combination treatment on perceived knee pain, hip frontal plane angle, and muscle activation during running in individuals with experimental knee pain (EKP). Design: Crossover. Setting: Laboratory. Subjects: 19 participants (11 males and 8 females, 23.2 ± 1.9 y, 176 ± 11.6 cm, 71.5 ± 16.9 kg; right leg dominant). Interventions: Hypertonic saline was infused into the infrapatellar fat pad for 74 minutes (total 11.1 mL). Subjects underwent 2 treatment conditions (sham; ice/TENS combination). Measurements were recorded during running at 4 time points (preinfusion, postinfusion, posttreatment, and postinterval). Main Outcome Measures: Perceived knee pain on a 100-mm visual analog scale (VAS), knee and hip muscle peak electromyography (EMG) amplitude, and hip adduction angles. Results: Hypertonic saline infusion increased perceived anterior knee pain in all participants. The average of peak perceived knee pain was 28 mm on a 100-mm VAS in EKP application. While the increased perceived knee pain level stayed consistent across time in the sham session, ice/TENS combination treatment significantly reduced perceived knee pain by 35% at 6 minutes after the treatment start (p = 0.049), and the reduced knee pain lasted for 22 minutes (p > 0.05). Peak EMG amplitude of the gluteus medius was decreased by 13.5% and 14.3% (p = 0.023; p = 0.013) during running after EKP in sham and treatment sessions, respectively. However, the peak EMG amplitude was not restored to pain-free level during running after the treatment (p = 0.026). No other muscles changed their peak EMG amplitude due to EKP or treatment. Hip adduction angles during running were also not altered by EKP or treatment (p > 0.3) in both sham and treatment sessions. Conclusions: EKP increased perceived knee pain and decreased peak muscle activation of the gluteus medius during running. Ice/TENS combination treatment reduced perceived knee pain quickly, but did not restore neuromechanics during running.

A number of studies have investigated the relationship between surface electromyogram (EMG) and torque exerted about a joint. The standard deviation of the recorded EMG signal is defined as the EMG amplitude. The EMG amplitude estimation technique varies with the study from conventional type of processing (i.e. rectification followed by low pass filtering) to further addition of different noise rejection and signal-to-noise ratio improvement stages. Advanced EMG amplitude processors developed recently that incorporate signal whitening and multiple-channel combination have been shown to significantly improve amplitude estimation. The main contribution of this research is a comparison of the performance of EMG-torque estimators with and without these advanced EMG amplitude processors. The experimental data are taken from fifteen subjects that produced constant-posture, non-fatiguing, force-varying contractions about the elbow while torque and biceps/triceps EMG were recorded. Utilizing system identification techniques, EMG amplitude was related to torque through a zeros-only (finite impulse response, FIR) model. The incorporation of whitening and multiple-channel combination separately reduced EMG-torque errors and their combination provided a cumulative improvement. A 15th-order linear FIR model provided an average estimation error of 6% of maximum voluntary contraction (or 90% of variance accounted for) when EMG amplitudes were obtained using a four-channel, whitened processor. The equivalent single-channel, unwhitened (conventional) processor produced an average error of 8% of maximum voluntary contraction (variance accounted for of 68%). This study also describes the occurrence of spurious peaks in estimated torque when the torque model is created from data with a sampling rate well above the bandwidth of the torque. This problem is anticipated when the torque data are sampled at the same rate as the EMG data. The problem is resolved by decimating the EMG amplitude prior to relating it to joint torque, in this case to an effective sampling rate of 40.96 Hz.

<p>Surface electromyography (sEMG) has been used to monitor muscle activity and predict fatigue in the workplaces. However, objectively measuring fatigue is challenging in complex work with unpredictable work cycles, where sEMG may be influenced by the dynamically changing posture demands. The sEMG is affected by various variables and substantial change in mean power frequencies (MPF), and a decline over 8-9% is primarily considered musculoskeletal fatigue. These MPF thresholds have been frequently used, and there were limited efforts to test their appropriateness in determining musculoskeletal fatigue in live workplaces (which predominantly consist of complex tasks). In addition, the techniques that consider both muscular and postural measurements that incorporate dynamic posture changes observed in complex work have not yet been explored. The overall objective of this work is to leverage both postural and muscular cues to identify musculoskeletal fatigue in complex tasks/jobs (i.e., tasks involving different levels of exertions, durations, and postures). The work was completed in two studies.</p> The first study aimed to (1) predict subjective fatigue using objective measurements in non-repetitive tasks, (2) determine whether the musculoskeletal fatigue thresholds in non-repetitive tasks differed from the previously reported threshold, and (3) utilize the empirically calculated thresholds to test their appropriateness in determining musculoskeletal fatigue in live surgical workplaces. The findings showed that the multi-modal measurements indicate better sensitivity than single-modality (sEMG) measurements in detecting decreases in MPF, a predictor of fatigue. In addition, the results showed that the thresholds in dynamic non-repetitive tasks, like surgery, are different than the previously reported 8% threshold. Additionally, implementing muscle-specific thresholds increased the likelihood of more accurately reporting subjective fatigue. The second study aimed to develop a multi-modal fatigue index to detect musculoskeletal fatigue. A controlled laboratory study was performed to simulate the non-repetitive physical demands at different postures. A series of experiments were conducted to test the effectiveness of various metrics/models to identify subjective fatigue in complex tasks. Next, the composite fatigue index (CFI) function was developed using the time-synced integration of both muscular signals (measured with sEMG sensors) and postural signals (measured with Inertial Measurement Unit (IMU) sensors). The variables from sEMG (amplitude, frequency, and the number of muscles showing signs of fatigue) and IMU (the prevalence of static and demanding postures and the number of shoulders in static/demanding posture) sensors were integrated to generate the CFI function. The prevalence of static/demanding postures was developed using the cumulative exposures to static/demanding postures based on the material fatigue failure theory. The single value fatigue index was obtained using the resultant CFI function, which incorporates both muscular and postural variables, to quantify the muscular fatigue in dynamic non-repetitive tasks. The findings suggested that the propagation of musculoskeletal fatigue can be detected using the multi-modal composite fatigue index in complex tasks. The resultant CFI function was then applied to surgery tasks to differentiate the fatigued and non-fatigued groups. The findings showed that the multi-modal fatigue assessment techniques could be utilized to incorporate the muscular and postural measurements to identify fatigue in complex tasks beyond single-modality assessment approaches.