Humans need only 200†ms to generate posture-specific muscle activation patterns for successful vertical jumps in reaction to an auditory trigger.

Introduction: It is currently unknown how the central nervous system controls ballistic whole-body movements like vertical jumps. Here we set out to study the time frame of generating muscle activation patterns for maximum-effort jumps from different initial postures. Methods: We had ten healthy male participants make a slow countermovement from an upright position and initiate a maximal vertical jump as soon as possible following an auditory trigger. The trigger was produced when hip height dropped below one of three preselected values, unknown in advance to the participant, so that the participant was uncertain about the posture from which to initiate the jump. Furthermore, we determined the ensuing bottom postures reached during jumps, and from these postures had the participants perform maximum-effort squat jumps in two conditions: whenever they felt ready, or as soon as possible following an auditory trigger. Kinematics and ground reaction forces were measured, and electromyograms were collected from gluteus maximus, biceps femoris, rectus femoris, vastus lateralis, gastrocnemius and soleus. For each muscle, we detected activation onsets, as well as reaction times defined as the delay between trigger onset and activation onset. Results: In the jumps preceded by a slow countermovement, the posture from which to initiate the jump was unknown before trigger onset. Nevertheless, in these jumps, posture-specific muscle activation patterns were already released within 200 ms after trigger onset and reaction times were not longer and jump heights not less than in squat jumps from corresponding bottom postures. Discussion: Our findings suggest that the generation of muscle activation patterns for jumping does not start before trigger onset and requires only about 200 ms.

Biomechanical evaluation of a new passive back support exoskeleton.

The number one cause of disability in the world is low-back pain, with mechanical loading as one of the major risk factors. To reduce mechanical loading, exoskeletons have been introduced in the workplace. Substantial reductions in back muscle activity were found when using the exoskeleton during static bending and manual materials handling. However, most exoskeletons only have one joint at hip level, resulting in loss of range of motion and shifting of the exoskeleton relative to the body. To address these issues, a new exoskeleton design has been developed and tested. The present study investigated the effect of the SPEXOR passive exoskeleton on compression forces, moments, muscle activity and kinematics during static bending at six hand heights and during lifting of a box of 10 kg from around ankle height using three techniques: Free, Squat and Stoop. For static bending, the exoskeleton reduced the compression force by 13-21% depending on bending angle. Another effect of the exoskeleton was that participants substantially reduced lumbar flexion. While lifting, the exoskeleton reduced the peak compression force, on average, by 14%. Lifting technique did not modify the effect of the exoskeleton such that the reduction in compression force was similar. In conclusion, substantial reductions in compression forces were found as a result of the support generated by the exoskeleton and changes in behavior when wearing the exoskeleton. For static bending, lumbar flexion was reduced with the exoskeleton, indicating reduced passive tissue strain. In addition, the reduced peak compression force could reduce the risk of compression induced tissue failure during lifting.

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.

Effects of a passive back exoskeleton on the mechanical loading of the low-back during symmetric lifting.

Low-back pain is the number one cause of disability in the world, with mechanical loading as one of the major risk factors. Exoskeletons have been introduced in the workplace to reduce low back loading. During static forward bending, exoskeletons have been shown to reduce back muscle activity by 10% to 40%. However, effects during dynamic lifting are not well documented. Relative support of the exoskeleton might be smaller in lifting compared to static bending due to higher peak loads. In addition, exoskeletons might also result in changes in lifting behavior, which in turn could affect low back loading. The present study investigated the effect of a passive exoskeleton on peak compression forces, moments, muscle activity and kinematics during symmetric lifting. Two types (LOW and HIGH) of the device, which generate peak support moments at large and moderate flexion angles, respectively, were tested during lifts from knee and ankle height from a near and far horizontal position, with a load of 10 kg. Both types of the trunk exoskeleton tested here reduced the peak L5S1 compression force by around 5-10% for lifts from the FAR position from both KNEE and ANKLE height. Subjects did adjust their lifting style when wearing the device with a 17% reduced peak trunk angular velocity and 5 degrees increased lumbar flexion, especially during ANKLE height lifts. In conclusion, the exoskeleton had a minor and varying effect on the peak L5S1 compression force with only significant differences in the FAR lifts.

The effect of control strategies for an active back-support exoskeleton on spine loading and kinematics during lifting.

With mechanical loading as the main risk factor for LBP, exoskeletons (EXO) are designed to reduce the load on the back by taking over part of the moment normally generated by back muscles. The present study investigated the effect of an active exoskeleton, controlled using three different control modes (INCLINATION, EMG & HYBRID), on spinal compression forces during lifting with various techniques. Ten healthy male subjects lifted a 15 kg box, with three lifting techniques (free, squat & stoop), each of which was performed four times, once without EXO and once each with the three different control modes. Using inverse dynamics, we calculated L5/S1 joint moments. Subsequently, we estimated spine forces using an EMG-assisted trunk model. Peak compression forces substantially decreased by 17.8% when wearing the EXO compared to NO EXO. However, this reduction was partly, by about one third, attributable to a reduction of 25% in peak lifting speed when wearing the EXO. While subtle differences in back load patterns were seen between the three control modes, no differences in peak compression forces were found. In part, this may be related to limitations in the torque generating capacity of the EXO. Therefore, with the current limitations of the motors it was impossible to determine which of the control modes was best. Despite these limitations, the EXO still reduced both peak and cumulative compression forces by about 18%.

Effects of a passive exoskeleton on the mechanical loading of the low back in static holding tasks.

With mechanical loading as the main risk factor for LBP in mind, exoskeletons are designed to reduce the load on the back by taking over a part of the required moment. The present study assessed the effect of a passive exoskeleton on back and abdominal muscle activation, hip and lumbar flexion and on the contribution of both the human and the exoskeleton to the L5/S1 net moment, during static bending at five different hand heights. Two configurations of the exoskeleton (LOW & HIGH) differing in angle-torque characteristics were tested. L5/S1 moments generated by the subjects were significantly reduced (15-20% for the most effective type) at all hand heights. LOW generated 4-11 Nm more support than HIGH at 50%, 25% and 0% upright stance hand height and HIGH generated 4-5 Nm more support than LOW at 100% and 75%. Significant reductions (11-57%) in back muscle activity were found compared to WITHOUT for both exoskeletons for some conditions. However, EMG reductions compared to WITHOUT were highly variable across subjects and not always significant. The device allowed for substantial lumbar bending (up to 70°) so that a number of participants showed the flexion-relaxation phenomenon, which prevented further reduction of back EMG by the device and even an increase from 2% to 6% MVC in abdominal activity at 25% hand height. These results indicate that flexion relaxation and its interindividual variation should be considered in future exoskeleton developments.

Rationale, Implementation and Evaluation of Assistive Strategies for an Active Back-Support Exoskeleton.

Active exoskeletons are potentially more effective and versatile than passive ones, but designing them poses a number of additional challenges. An important open challenge in the field is associated to the assistive strategy, by which the actuation forces are modulated to the user's needs during the physical activity. This paper addresses this challenge on an active exoskeleton prototype aimed at reducing compressive low-back loads, associated to risk of musculoskeletal injury during manual material handling (i.e., repeatedly lifting objects). An analysis of the biomechanics of the physical task reveals two key factors that determine low-back loads. For each factor, a suitable control strategy for the exoskeleton is implemented. The first strategy is based on user posture and modulates the assistance to support the wearer's own upper body. The second one adapts to the mass of the lifted object and is a practical implementation of electromyographic control. A third strategy is devised as a generalized combination of the first two. With these strategies, the proposed exoskeleton can quickly adjust to different task conditions (which makes it versatile compared to using multiple, task-specific, devices) as well as to individual preference (which promotes user acceptance). Additionally, the presented implementation is potentially applicable to more powerful exoskeletons, capable of generating larger forces. The different strategies are implemented on the exoskeleton and tested on 11 participants in an experiment reproducing the lifting task. The resulting data highlights that the strategies modulate the assistance as intended by design, i.e., they effectively adjust the commanded assistive torque during operation based on user posture and external mass. The experiment also provides evidence of significant reduction in muscular activity at the lumbar spine (around 30%) associated to using the exoskeleton. The reduction is well in line with previous literature and may be associated to lower risk of injury.

Passive Back Support Exoskeleton Improves Range of Motion Using Flexible Beams.

In the EU, lower back pain affects more than 40% of the working population. Mechanical loading of the lower back has been shown to be an important risk factor. Peak mechanical load can be reduced by ergonomic interventions, the use of cranes and, more recently, by the use of exoskeletons. Despite recent advances in the development of exoskeletons for industrial applications, they are not widely adopted by industry yet. Some of the challenges, which have to be overcome are a reduced range of motion, misalignment between the human anatomy and kinematics of the exoskeleton as well as discomfort. A body of research exists on how an exoskeleton can be designed to compensate for misalignment and thereby improve comfort. However, how to design an exoskeleton that achieves a similar range of motion as a human lumbar spine of up to 60° in the sagittal plane, has not been extensively investigated. We addressed this need by developing and testing a novel passive back support exoskeleton, including a mechanism comprised of flexible beams, which run in parallel to the spine, providing a large range of motion and lowering the peak torque requirements around the lumbo-sacral (L5/S1) joint. Furthermore, we ran a pilot study to test the biomechanical (N = 2) and functional (N = 3) impact on subjects while wearing the exoskeleton. The biomechanical testing was once performed with flexible beams as a back interface and once with a rigid structure. An increase of more than 25% range of motion of the trunk in the sagittal plane was observed by using the flexible beams. The pilot functional tests, which are compared to results from a previous study with the Laevo device, suggest, that the novel exoskeleton is perceived as less hindering in almost all tested tasks.

Estimating the L5S1 flexion/extension moment in symmetrical lifting using a simplified ambulatory measurement system.

Mechanical loading of the spine has been shown to be an important risk factor for the development of low-back pain. Inertial motion capture (IMC) systems might allow measuring lumbar moments in realistic working conditions, and thus support evaluation of measures to reduce mechanical loading. As the number of sensors limits applicability, the objective of this study was to investigate the effect of the number of sensors on estimates of L5S1 moments. Hand forces, ground reaction forces (GRF) and full-body kinematics were measured using a gold standard (GS) laboratory setup. In the ambulatory setup, hand forces were estimated based on the force plates measured GRF and body kinematics that were measured using (subsets of) an IMC system. Using top-down inverse dynamics, L5S1 flexion/extension moments were calculated. RMSerrors (Nm) were lowest (16.6) with the full set of 17 sensors and increased to 20.5, 22 and 30.6, for 8, 6 and 4 sensors. Absolute errors in peak moments (Nm) ranged from 17.7 to 16.4, 16.9 and 49.3 Nm, for IMC setup's with 17, 8, 6 and 4 sensors, respectively. When horizontal GRF were neglected for 6 sensors, RMSerrors and peak moment errors decreased from 22 to 17.3 and from 16.9 to 13 Nm, respectively. In conclusion, while reasonable moment estimates can be obtained with 6 sensors, omitting the forearm sensors led to unacceptable errors. Furthermore, vertical GRF information is sufficient to estimate L5S1 moments in lifting.