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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.

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.