Can intermittent changes in trunk extensor muscle length delay muscle fatigue development?

Muscle length changes may evoke alternating activity and consequently reduce local fatigue and pain during prolonged static bending. The aim of this study was to assess whether a postural intervention involving intermittent trunk extensor muscle length changes (INTERMITTENT) can delay muscle fatigue during prolonged static bending when compared to a near-isometric condition (ISOMETRIC) or when participants were allowed to voluntarily vary muscle length (VOLUNTARY). These three conditions were completed by 11 healthy fit male participants, in three separate sessions of standing with 30 ± 3 degrees trunk inclination until exhaustion. Conventional and high-density electromyography (convEMG and HDsEMG, respectively) were measured on the left and right side of the spine, respectively. The endurance time for INTERMITTENT was 33.6% greater than ISOMETRIC (95% CI: [3.8, 63.5]; p = 0.027) and 29.4% greater than VOLUNTARY (95% CI: [7.0, 51.7]; p = 0.010), but not different between ISOMETRIC and VOLUNTARY. The convEMG and HDsEMG amplitude coefficient of variation was significantly greater for INTERMITTENT versus ISOMETRIC. The rate of change in convEMG and HDsEMG spectral content did not reveal significant differences between conditions as found in endurance time. Additional regression analyses between endurance time and rate of change in convEMG (p > 0.05) and HDsEMG (R2 = 0.39-0.65, p = 0.005-0.039) spectral content indicated that HDsEMG better reflects fatigue development in low-level contractions. In conclusion, imposed intermittent trunk extensor muscle length changes delayed muscle fatigue development when compared to a near-isometric condition or when participants were allowed to voluntarily vary muscle length, possibly due to evoking alternating activity between/within trunk extensor muscles.

The effect of human autonomy and robot work pace on perceived workload in human-robot collaborative assembly work.

Collaborative robots (in short: cobots) have the potential to assist workers with physically or cognitive demanding tasks. However, it is crucial to recognize that such assistance can have both positive and negative effects on job quality. A key aspect of human-robot collaboration is the interdependence between human and robotic tasks. This interdependence influences the autonomy of the operator and can impact the work pace, potentially leading to a situation where the human's work pace becomes reliant on that of the robot. Given that autonomy and work pace are essential determinants of job quality, design decisions concerning these factors can greatly influence the overall success of a robot implementation. The impact of autonomy and work pace was systematically examined through an experimental study conducted in an industrial assembly task. 20 participants engaged in collaborative work with a robot under three conditions: human lead (HL), fast-paced robot lead (FRL), and slow-paced robot lead (SRL). Perceived workload was used as a proxy for job quality. To assess the perceived workload associated with each condition was assessed with the NASA Task Load Index (TLX). Specifically, the study aimed to evaluate the role of human autonomy by comparing the perceived workload between HL and FRL conditions, as well as the influence of robot pace by comparing SRL and FRL conditions. The findings revealed a significant correlation between a higher level of human autonomy and a lower perceived workload. Furthermore, a decrease in robot pace was observed to result in a reduction of two specific factors measuring perceived workload, namely cognitive and temporal demand. These results suggest that interventions aimed at increasing human autonomy and appropriately adjusting the robot's work pace can serve as effective measures for optimizing the perceived workload in collaborative scenarios.

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.

A Resilient and Effective Task Scheduling Approach for Industrial Human-Robot Collaboration.

Effective task scheduling in human-robot collaboration (HRC) scenarios is one of the great challenges of collaborative robotics. The shared workspace inside an industrial setting brings a lot of uncertainties that cannot be foreseen. A prior offline task scheduling strategy is ineffective in dealing with these uncertainties. In this paper, a novel online framework to achieve a resilient and reliable task schedule is presented. The framework can deal with deviations that occur during operation, different operator skills, error by the human or robot, and substitution of actors, while maintaining an efficient schedule by promoting parallel human-robot work. First, the collaborative job and the possible deviations are represented by AND/OR graphs. Subsequently, the proposed architecture chooses the most suitable path to improve the collaboration. If some failures occur, the AND/OR graph is adapted locally, allowing the collaboration to be completed. The framework is validated in an industrial assembly scenario with a Franka Emika Panda collaborative robot.

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.

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.

Evaluation of the Achilles Ankle Exoskeleton.

This paper evaluates the Achilles exoskeleton. The exoskeleton is intended to provide push-off assistance for healthy subjects during walking. The assistance is provided by a series elastic actuator that has been optimized to provide maximal push-off power. The paper presents the control method of the exoskeleton and the evaluation of the exoskeleton.

Optimization of human walking for exoskeletal support.

It is hypothesized that healthy humans can reduce their energy expenditure during walking by wearing an exoskeleton. Exoskeletons are often designed for mechanical efficiency at joint level. This approach disregards the energy savings mechanisms in the human leg like bi-articular muscles and tendons. We use the muscle-reflex model to simulate the experiments by Cain et al. with an ankle exoskeleton actuated by a pneumatic muscle that supports plantarflexion. The muscle-reflex model predicts muscle activations and metabolic rate. The reflex-control parameters of the model were optimized for walking with and without support from an exoskeleton. The simulated exoskeleton uses either the EMG signal from the soleus muscle (proportional myoelectric control), or a footswitch to trigger the actuation of the pneumatic muscle. Cain et al. did find an experimental reduction in soleus muscle activation of 41.4 percent for the proportional myoelectric control and 13.0 percent for the footswitch control, where the optimization outcomes of simulated walking predicted a reduction of 42.8 percent and 25.9 percent respectively.

A passive exoskeleton with artificial tendons: design and experimental evaluation.

We developed a passive exoskeleton that was designed to minimize joint work during walking. The exoskeleton makes use of passive structures, called artificial tendons, acting in parallel with the leg. Artificial tendons are elastic elements that are able to store and redistribute energy over the human leg joints. The elastic characteristics of the tendons have been optimized to minimize the mechanical work of the human leg joints. In simulation the maximal reduction was 40 percent. The performance of the exoskeleton was evaluated in an experiment in which nine subjects participated. Energy expenditure and muscle activation were measured during three conditions: Normal walking, walking with the exoskeleton without artificial tendons, and walking with the exoskeleton with the artificial tendons. Normal walking was the most energy efficient. While walking with the exoskeleton, the artificial tendons only resulted in a negligibly small decrease in energy expenditure.

Spring uses in exoskeleton actuation design.

An exoskeleton has to be lightweight, compliant, yet powerful to fulfill the demanding task of walking. This imposes a great challenge for the actuator design. Electric motors, by far the most common actuator in robotic, orthotic, and prosthetic devices, cannot provide sufficiently high peak and average power and force/torque output, and they normally require high-ratio, heavy reducer to produce the speeds and high torques needed for human locomotion. Studies on the human muscle-tendon system have shown that muscles (including tendons and ligaments) function as a spring, and by storing energy and releasing it at a proper moment, locomotion becomes more energy efficient. Inspired by the muscle behavior, we propose a novel actuation strategy for exoskeleton design. In this paper, the collected gait data are analyzed to identify the spring property of the human muscle-tendon system. Theoretical optimization results show that adding parallel springs can reduce the peak torque by 66%, 53%, and 48% for hip flexion/extension (F/E), hip abduction/adduction (A/A), and ankle dorsi/plantar flexion (D/PF), respectively, and the rms power by 50%, 45%, and 61%, respectively. Adding a series spring (forming a Series Elastic Actuator, SEA) reduces the peak power by 79% for ankle D/PF, and by 60% for hip A/A. A SEA does not reduce the peak power demand at other joints. The optimization approach can be used for designing other wearable robots as well.