Objective: Over the course of the twenty-first century, work-related musculoskeletal disorders are still persisting among blue collar workers. At present, no epidemiological overview exists. Therefore, a systematic review and meta-analysis was performed on the epidemiology of work-related musculoskeletal disorders (WMSD) within Europe’s secondary industries. Methods: Five databases were screened, yielding 34 studies for the qualitative analysis and 17 for the quantitative analysis. Twelve subgroups of WMSDs were obtained for the meta-analysis by means of predefined inclusion criteria: back (overall), upper back, lower back, neck, shoulder, neck/shoulder, elbow, wrist/hand, leg (overall), hip, knee, and ankle/feet. Results: The most prevalent WMSDs were located at the back (overall), shoulder/neck, neck, shoulder, lower back and wrist WMSDs with mean 12-month prevalence values of 60, 54, 51, 50, 47, and 42%, respectively. The food industry was in the majority of subgroups the most prominent researched sector and was frequently associated with high prevalence values of WMSDs. Incidence ratios of upper limb WMSDs ranged between 0.04 and 0.26. Incidence ratios could not be calculated for other anatomical regions due to the lack of sufficient articles. Conclusion: WMSDs are still highly present among blue collar workers. Relatively high prevalence values and low incidence ratios indicate a limited onset of WMSDs with however long-term complaints.

Shoulder exoskeletons potentially reduce overuse injuries in industrial settings including overhead work or lifting tasks. Previous studies evaluated these devices primarily in laboratory setting, but evidence of their effectiveness outside the lab is lacking. The present study aimed to evaluate the effectiveness of two passive shoulder exoskeletons and explore the transfer of laboratory-based results to the field. Four industrial workers performed controlled and in-field evaluations without and with two exoskeletons, ShoulderX and Skelex in a randomized order. The exoskeletons decreased upper trapezius activity (up to 46%) and heart rate in isolated tasks. In the field, the effects of both exoskeletons were less prominent (up to 26% upper trapezius activity reduction) while lifting windscreens weighing 13.1 and 17.0 kg. ShoulderX received high discomfort scores in the shoulder region and usability of both exoskeletons was moderate. Overall, both exoskeletons positively affected the isolated tasks, but in the field the support of both exoskeletons was limited. Skelex, which performed worse in the isolated tasks compared to ShoulderX, seemed to provide the most support during the in-field situations. Exoskeleton interface improvements are required to improve comfort and usability. Laboratory-based evaluations of exoskeletons should be interpreted with caution, since the effect of an exoskeleton is task specific and not all in-field situations with high-level lifting will equally benefit from the use of an exoskeleton. Before considering passive exoskeleton implementation, we recommend analyzing joint angles in the field, because the support is inherently dependent on these angles, and to perform in-field pilot tests. This paper is the first thorough evaluation of two shoulder exoskeletons in a controlled and in-field situation.

Recent developments in Statistical Parametric Mapping (SPM) for continuum data (e.g. kinematic time series) have been adopted by the biomechanics research community with great interest. The Python/MATLAB package spm1d developed by T. Pataky has introduced SPM into the biomechanical literature, adapted originally from neuroimaging. The package already allows many of the statistical analyses common in biomechanics from a frequentist perspective. In this paper, we propose an application of Bayesian analogs of SPM based on Bayes factors and posterior probability with default priors using the BayesFactor package in R. Results are provided for two typical designs (two-sample and paired sample t-tests) and compared to classical SPM results, but more complex standard designs are possible in both classical and Bayesian frameworks. The advantages of Bayesian analyses in general and specifically for SPM are discussed. Scripts of the analyses are available as supplementary materials.

Physical human-robot interfaces are a challenging aspect of exoskeleton design, mainly due to the fact that interfaces tend to migrate relatively to the body leading to discomfort and power losses. Therefore, the key is to develop interfaces that optimize attachment stiffness, i.e., reduce relative motion, without compromising comfort. To that end, we propose a method to obtain the optimal attachment pressure in terms of connection stiffness and comfort. The method is based on a soft robotic interface capable of actively controlling strapping pressure which is coupled to a cobot. Hereby the effects of strapping pressure on energetic losses, connection stiffness, and perceived comfort are analyzed. Results indicate a trade-off between connection stiffness and perceived comfort for this type of interface. An optimal strapping pressure was found in the 50 to 80 mmHg range. Connection stiffness was found to increase linearly over a pressure range from 0 mmHg (stiffness of 1139 N/m) to 100 mmHg (stiffness of 2232 N/m). And energetic losses were reduced by 42% by increasing connection stiffness. This research highlights the importance of strapping pressure when attaching an exoskeleton to a human and introduces a new adaptive interface to improve the coupling from an exoskeleton to an individual.

The purpose of this paper was to examine differences in ball release speed and throwing kinematics between male and female team-handball players in a standing throw with run-up. Other research has shown that this throwing type produces the highest ball release speeds and comparing groups with differences in ball release speed can suggest where this difference might come from. If throwing technique differs, perhaps gender-specific coordination- and strength-training guidelines are in order. Measurements of three-dimensional kinematics were performed with a seven-camera VICON motion capture system and subsequent joint angles and angular velocities calculations were executed in Mathcad. Data-analysis with Statistical Parametric Mapping allowed us to examine the entire time-series of every variable without having to reduce the data to certain scalar values such as minima/maxima extracted from the time-series. Statistical Parametric Mapping enabled us to detect several differences in the throwing kinematics (12 out of 20 variables had one or more differences somewhere during the motion). The results indicated two distinct strategies in generating and transferring momentum through the kinematic chain. Male team-handball players showed more activity in the transverse plane (pelvis and trunk rotation and shoulder horizontal abduction) whereas female team-handball players showed more activity in the sagital plane (trunk flexion). Also the arm cocking maneuver was quite different. The observed differences between male and female team handball players in the motions of pelvis, trunk and throwing arm can be important information for coaches to give feedback to athletes. Whether these differences contribute to the observed difference in ball release speed is at the present unclear and more research on the relation with anthropometric profile needs to be done. Kinematic differences might suggest gender-specific training guidelines in team-handball.