Trunk extensor muscle endurance and its relationship to action potential conduction velocity and spectral parameters estimated using high-density electromyography.

Trunk extensor muscle fatigue typically manifests as a decline in spectral content of surface electromyography. However, previous research on the relationship of this decline with trunk extensor muscle endurance have shown inconsistent results. The decline of spectral content mainly reflects the decrease in average motor unit action potential conduction velocity (CV). We evaluated whether the rate of change in CV, as well as two approaches employing the change in spectral content, are related to trunk extensor muscle endurance. Fourteen healthy male participants without a low-back pain history performed a non-strictly controlled static forward trunk bending trial until exhaustion while standing. For 13 participants, physiologically plausible CV estimates were obtained from high-density surface electromyography bilaterally from T6 to L5. Laterally between L1 and L2, the linear rate of CV change was strongly correlated to endurance time (R2 = 0.79), whereas analyses involving the linear rate of change in spectral measures showed a lower (R2 = 0.38) or no correlation. For medial electrode locations, estimating CV and its relationship with endurance time was less successful, while the linear rate of change in spectral measures correlated moderately to endurance time (R2 = 0.44; R2 = 0.56). This study provides guidance on monitoring trunk extensor muscle fatigue development using electromyography.

Development of a real time estimation method of L5S1 moments in occupational lifting.

Mechanical loading of the low-back is an important risk factor for the development of low-back pain. Real-time estimation of the L5S1 joint moment (ML5S1) can give an insight to reduce mechanical loading. Model accuracy depends on sensor information, limiting the number of input variables to estimate ML5S1 increases practical feasibility, but may decrease accuracy. This study aimed to find a model with a limited set of input variables without a large reduction in accuracy. We compared two approaches. The first was based on a simplified inverse dynamics model (SM) that requires a limited number of input variables (EMG/ground reaction forces, and orientations derived from an optoelectronic system (OMC)). Two variations were examined, to determine to what extent arm orientations were needed. The second approach was based on a regression model (RM) that uses the SMs as ground-truth. Two variations in terms of sensor use and calibration were examined. Test trials consisted of re-stacking a stack of 3 boxes. A high-end lab-based OMC-system was used as the gold standard (GS). Fifteen healthy participants, 9 males and 6 females (age 21-30) participated in this study. R2, RMSE, and peak-difference with the GS ML5S1 estimate were compared between models with a repeated-measures ANOVA. The SM including arm sensors performed similar or better than the regression models (r > 0.9 and RMSE < 15 % of average peak moment). However, from the perspective of practical feasibility and minimizing the required number of sensors during work, the best approach would be using one of the two regression model approaches.

Low back muscle action potential conduction velocity estimated using high-density electromyography.

While a decreasing spectral content of surface electromyography reflects low back muscle fatigue development, reliability of these decreases may be insufficient. Decreasing frequency content is largely determined by decreasing average motor unit action potential conduction velocities (CV), which is considered a more direct measure of muscle fatigue development. However, for the low back muscles it has been proven difficult to identify propagating potentials and consequently estimate the CV. The aim of this study was to estimate the low back muscle CV from high-density multi-channel electromyography by using peak-delay and cross-correlation methods. Fourteen healthy male participants without a history of low-back pain performed a 30 degrees lumbar flexion trial until exhaustion while standing. For 10 out of the 14 participants (118 out of 560 sites) realistic CV estimates were obtained using both methods, the majority likely over the iliocostalis lumborum muscle. Between-method CV differences appeared to be small. Close to the spine a considerable number of sites (79) yielded systematically overestimated low back muscle CV values. Estimating low back muscle CV may allow additional insight into low back muscle fatigue development and potentially improve its monitoring using (high-density) surface electromyography.

A novel passive neck orthosis for patients with degenerative muscle diseases: Development & evaluation.

The current study evaluated the effect of a passive neck orthosis, developed for patients suffering from progressive muscular diseases, on neck muscle activity in 10 adult healthy participants. The participants performed discrete head movements involving pure neck flexion (-10 to 30°), pure neck rotation (up to 30° left and right) and combined neck flexion-rotation (-10 to 30°) in steps of 10° by moving a cursor on a screen to reach predefined targets and staying on target for 10 s. Surface electromyography (EMG) was recorded from upper trapezius and sternocleidomastoid muscles and amplitudes were averaged over the static phases in trials with and without the orthosis. Moreover, the variability in head position and time required to perform the tasks were compared between conditions. Wearing the orthosis caused significant reductions (p = 0.027) in upper trapezius activity (a change of 0.2-1.5% EMGmax) while working against gravity. The activity level of the sternocleidomastoid muscle increased (p ≤ 0.025) by 0.3-1.0% EMGmax during pure and combined rotations without any pain reported. The orthosis showed potential to reduce the activity level of the upper trapezius muscle, the main load bearing muscle of the neck. Further study will be carried out to evaluate the effect in different patient groups.

Optimizing Calibration Procedure to Train a Regression-Based Prediction Model of Actively Generated Lumbar Muscle Moments for Exoskeleton Control.

The risk of low-back pain in manual material handling could potentially be reduced by back-support exoskeletons. Preferably, the level of exoskeleton support relates to the required muscular effort, and therefore should be proportional to the moment generated by trunk muscle activities. To this end, a regression-based prediction model of this moment could be implemented in exoskeleton control. Such a model must be calibrated to each user according to subject-specific musculoskeletal properties and lifting technique variability through several calibration tasks. Given that an extensive calibration limits the practical feasibility of implementing this approach in the workspace, we aimed to optimize the calibration for obtaining appropriate predictive accuracy during work-related tasks, i.e., symmetric lifting from the ground, box stacking, lifting from a shelf, and pulling/pushing. The root-mean-square error (RMSE) of prediction for the extensive calibration was 21.9 nm (9% of peak moment) and increased up to 35.0 nm for limited calibrations. The results suggest that a set of three optimally selected calibration trials suffice to approach the extensive calibration accuracy. An optimal calibration set should cover each extreme of the relevant lifting characteristics, i.e., mass lifted, lifting technique, and lifting velocity. The RMSEs for the optimal calibration sets were below 24.8 nm (10% of peak moment), and not substantially different than that of the extensive calibration.

Selecting the appropriate input variables in a regression approach to estimate actively generated muscle moments around L5/S1 for exoskeleton control.

Back support exoskeletons are designed to prevent work-related low-back pain by reducing mechanical loading. For actuated exoskeletons, support based on moments actively produced by the trunk muscles appears a viable approach. The moment can be estimated by a biomechanical model. However, one of the main challenges here is the feasibility of recording the required input variables (kinematics, EMG data, ground reaction forces) to run the model. The aim of this study was to evaluate how accurate different selections of input variables can estimate actively generated moments around L5/S1. Different multivariate regression analyses were performed using a dataset consisting of spinal load, body kinematics and trunk muscle activation levels during different lifting conditions with and without an exoskeleton. The accuracy of the resulting models depended on the number and type of input variables and the regression model order. The current study suggests that third-order polynomial regression of EMG signals of one or two bilateral back muscle pairs together with exoskeleton trunk and hip angle suffices to accurately estimate the actively generated muscle moment around L5/S1, and thereby design a proper control system for back support exoskeletons.