Work-related musculoskeletal disorders (WMDs) affect a high percentage of operators performing repeated weight lifting and load carrying in industrial scenarios. Since upper limb muscles are affected in the process, the assistance provided by upper body exoskeletons is increasingly needed to prevent WMDs and their consequent cost to the health system. This paper presents the evaluation of Flexos, a portable, bilateral, shoulder exoskeleton prototype designed to assist logistic and industrial operators in performing occupational tasks. An in-lab assessment was conducted on twelve healthy subjects - 9 males, 3 females - to evaluate Flexos capability in assisting the user during the execution of isometric, dynamic, and carrying-load tasks. Different metrics were extracted from time-series signals to assess the effort related to five targeted muscles surrounding the shoulder complex. Despite the limited experimental size and the prototypal level of the device, Flexos managed to cover almost all the shoulders range of motion - 89.2% flexion/extension, and 88.4% internal/external rotation - and to globally decrease muscular activity in occupational activities, particularly when isometric contractions are required for a prolonged time, with average reductions of -27.2% for the static task, -18.6% for the dynamic task and -23.4% for the carrying-load task.

Human beings adapt their motor patterns in response to their surroundings, utilizing sensory modalities such as visual inputs. This context-informed adaptive motor behavior has increased interest in integrating computer vision algorithms into robotic assistive technologies, marking a shift towardscontext aware control. However, such integration has rarely been achieved so far, with current methods mostly relying on data-driven approaches. In this study, we introduce a novel control framework for a soft hip exosuit, employing instead a physics-informed computer vision method grounded on geometric modeling of the captured scene for assistance tuning during stairs and level walking. This approach promises to provide a viable solution that is more computationally efficient and does not depend on training examples. Evaluating the controller with six subjects on a path comprising level walking and stairs, we achieved an overall detection accuracy of$93.0\pm 1.1\%$. Computer vision-based assistance provided significantly greater metabolic benefits compared to non-vision-based assistance, with larger energy reductions relative to being unassisted during stair ascent ($-18.9 \pm 4.1\%$vs.$-5.2 \pm 4.1\%$) and descent ($-10.1 \pm 3.6\%$vs.$-4.7 \pm 4.8\%$). Such a result is a consequence of the adaptive nature of the device, enabled by the context aware controller, that allowed for more effective walking support: i.e. the assistive torque showed a significant increase while ascending stairs ($+33.9\pm 8.8\%$) and decrease while descending stairs ($-17.4\pm 6.0\%$) compared to a condition without assistance modulation enabled by vision. These results highlight the potential of the approach, promoting effective real-time embedded applications in assistive robotics.

The intention-detection strategy used to drive an exosuit is fundamental to evaluate the effectiveness and acceptability of the device. Yet, current literature on wearable soft robotics lacks evidence on the comparative performance of different control approaches for online intention-detection. In the present work, we compare two different and complementary controllers on a wearable robotic suit, previously formulated and tested by our group; a model-based myoelectric control (myoprocessor), which estimates the joint torque from the activation of target muscles, and a force control that estimates human torques using an inverse dynamics model (dynamic arm). We test them on a cohort of healthy participants performing tasks replicating functional activities of daily living involving a wide range of dynamic movements. Our results suggest that both controllers are robust and effective in detecting human–motor interaction, and show comparable performance for augmenting muscular activity. In particular, the biceps brachii activity was reduced by up to 74% under the assistance of thedynamic armand up to 47% under themyoprocessor, compared to a no-suit condition. However, themyoprocessoroutperformed thedynamic armin promptness and assistance during movements that involve high dynamics. The exosuit work normalized with respect to the overall work was$68.84 \pm 3.81\%$when it was ran by themyoprocessor, compared to$45.29 \pm 7.71\%$during thedynamic armcondition. The reliability and accuracy of motor intention detection strategies in wearable device is paramount for both the efficacy and acceptability of this technology. In this article, we offer a detailed analysis of the two most widely used control approaches, trying to highlight their intrinsic structural differences and to discuss their different and complementary performance.

Wearable robots are undergoing a disruptive transition, from the rigid machines that populated the science-fiction world in the early 1980s to lightweight robotic apparel, hardly distinguishable from our daily clothes. In less than a decade of development, soft robotic suits have achieved important results in human motor assistance and augmentation. In this article, we start by giving a definition of soft robotic suits and proposing a taxonomy to classify existing systems. We then critically review the modes of actuation, the physical human–robot interface and the intention-detection strategies of state-of-the-art soft robotic suits, highlighting the advantages and limitations of different approaches. Finally, we discuss the impact of this new technology on human movements, for both augmenting human function and supporting motor impairments, and identify areas that are in need of further development.

In recent years, bowden-cable transmissions have been developed and utilized widely in many robotic applications due to advantages in durability, lightweight, safety, and flexibility. Especially, over the last decade, a substantial number of soft wearable exoskeletons using bowden cables for motion transmission have been designed for human assistance, empowerment and rehabilitation. The major advantage of soft assistive devices driven by bowden-cable transmissions is to allow decentralizing the actuation stages proximally such that their mass has the least effect on the end-effector. Besides the advantage, the main drawback of the bowden cable-driven system comes from the presence of nonlinearities such as friction and backlash hysteresis that affects their control accuracy. Hence, in this paper, we introduce a mathematical model for backlash hysteresis and propose a solution based on the nonlinear adaptive control to compensate for the backlash effect. The backlash hysteresis model and control scheme are validated first on a custom-designed test bench and then applied to control a soft exoskeleton in a preliminary human trial.