Studies on back-support exoskeletons focus mostly on the evaluation of single devices. Also, these studies suggest that passive exoskeletons might be preferred when performing static tasks, whereas active ones, could show their advantage in dynamic tasks. To verify this hypothesis and provide a comparison between different devices, in this study, we analyse the performance of the XoTrunk and the Laevo v2.56, an active back-support exoskeleton and a passive one, respectively. We analyse these devices highlighting their ability to reduce back muscle activation, perceived discomfort and usability. The study was carried out by simulating a dynamic and a static task. The results show that both exoskeletons reduce back-muscle activation levels, although the XoTrunk provides a reduction that was more than twice as large as the Laevo (e.g., 41% vs 16%, respectively). The results also show that, overall, in the dynamic condition, the XoTrunk has better scores than the Laevo. Conversely, in the static task, the participants in the study had a preference for the passive device, Laevo. The results presented in this work can help exoskeleton end-users to properly select the best device to fit their needs.

Occupational back-support exoskeletons see their potential application in many industrial sectors to mitigate low back pain risk for workers performing demanding tasks. This work aims to design and evaluate a control strategy that improves the efficacy of an active exoskeleton by exploiting the raw signal from an accelerometer placed on the user’s torso. The designed strategy adapts the assistance to the user’s dynamics during lifting and lowering tasks, increasing the assistance for the lifting phase and decreasing it for lowering. The strategy efficacy is demonstrated through experimental analysis on twenty subjects. Comparisons are made with the task executed without the exoskeleton and a state-of-the-art inclination-based strategy. Results demonstrate the efficacy of the proposed strategy in improving users’ acceptance and reducing erector spinae muscle activity. Using the accelerometer signal, the exoskeleton can enhance the assistance provided to workers performing manual material handling in real working scenarios, increasing safety, efficiency, and productivity.

During the development and assessment of an exoskeleton, many different analyzes need to be performed. The most frequently used evaluate the changes in muscle activations, metabolic consumption, kinematics, and kinetics. Since human-exoskeleton interactions are based on the exchange of forces and torques, the latter of these, kinetic analyzes, are essential and provide indispensable evaluation indices. Kinetic analyzes, however, require access to, and use of, complex experimental apparatus, involving many instruments and implicating lengthy data analysis processes. The proposed methodology in this paper, which is based on data collected via EMG and motion capture systems, considerably reduces this burden by calculating kinetic parameters, such as torque and power, without needing ground reaction force measurements. This considerably reduces the number of instruments used, allows the calculation of kinetic parameters even when the use of force sensors is problematic, does not need any dedicated software, and will be shown to have high statistical validity. The method, in fact, combines data found in the literature with those collected in the laboratory, allowing the analysis to be carried out over a much greater number of cycles than would normally be collected with force plates, thus enabling easy access to statistical analysis. This new approach evaluates the kinetic effects of the exoskeleton with respect to changes induced in the user's kinematics and muscular activation patterns and provides indices that quantify the assistance in terms of torque (AMI) and power (API). Following the User-Center Design approach, which requires driving the development process as feedback from the assessment process, this aspect is critical. Therefore, by enabling easy access to the assessment process, the development of exoskeletons could be positively affected.

This study on occupational back-support exoskeletons performs a laboratory evaluation of realistic tasks with expert workers from the railway sector. Workers performed both a static task and a dynamic task, each involving manual material handling (MMH) and manipulating loads of 20 kg, in three conditions: without an exoskeleton, with a commercially available passive exoskeleton (Laevo v2.56), and with the StreamEXO, an active back-support exoskeleton developed by our institute. Two control strategies were defined, one for dynamic tasks and one for static tasks, with the latter determining the upper body’s gravity compensation through the Model-based Gravity Compensation (MB-Grav) approach. This work presents a comparative assessment of the performance of active back support exoskeletons versus passive exoskeletons when trialled in relevant and realistic tasks. After a lab characterization of the MB-Grav strategy, the experimental assessment compared two back-support exoskeletons, one active and one passive. The results showed that while both devices were able to reduce back muscle activation, the benefits of the active device were triple those of the passive system regarding back muscle activation ($26\%$ and $33\%$ against $9\%$ and $11\%$, respectively), while the passive exoskeleton hindered trunk mobility more than the active mechanism.

Work-related musculoskeletal disorders are globally one of the leading causes of work-related injuries. They significantly impact worker health and business costs. Work task ergonomic risk indices have been developed that use observational assessments to identify potential injuries, and allow safety managers to promptly intervene to mitigate the risks. However, these assessments are very subjective and difficult to perform in real time. This work provides a technique that can digitalize this process by developing an online algorithm to calculate the NIOSH index and provide additional data for ergonomic risk assessment. The method is based on the use of inertial sensors, which are easily found commercially and can be integrated into the industrial environment without any other sensing technology. This preliminary study demonstrates the effectiveness of the first version of the Online Lifting Index (On-LI) algorithm on a common industrial logistic task. The effectiveness is compared to the standard ergonomic assessment method. The results report an average error of 3.6% compared to the NIOSH parameters used to calculate the ergonomic risk and a relative error of the Lifting Index of 2.8% when compared to observational methods.

The assessment of realistic work tasks is a critical aspect of introducing exoskeletons to work environments. However, as the experimental task’s complexity increases, the analysis of muscle activity becomes increasingly challenging. Thus, it is essential to use metrics that adequately represent the physical human–exoskeleton interaction (pHEI). Muscle activity analysis is usually reduced to a comparison of point values (average or maximum muscle contraction), neglecting the signals’ trend. Metrics based on single values, however, lack information about the dynamism of the task and its duration. Their meaning can be uncertain, especially when analyzing complex movements or temporally extended activities, and it is reduced to an overall assessment of the interaction on the whole task. This work proposes a method based on integrated EMGs (iEMGs) to evaluate the pHEI by considering task dynamism, temporal duration, and the neural energy associated with muscle activity. The resulting signal highlights the task phases in which the exoskeleton reduces or increases the effort required to accomplish the task, allowing the calculation of specific indices that quantify the energy exchange in terms of assistance (AII), resistance (RII), and overall interaction (OII). The method provides an analysis tool that enables developers and controller designers to receive insights into the exoskeleton performances and the quality of the user-robot interaction. The application of this method is provided for passive and two active back support exoskeletons: the Laevo, XoTrunk, and StreamEXO.