Hybrid wearable robotics, combining soft and rigid elements, offer a promising solution for upper limb assistance by balancing comfort and functionality. This study evaluates a hybrid soft-rigid elbow exosuit designed for elbow movement support in dynamic and endurance tasks, focusing on biomechanical impact, user perception, and ergonomics. A theoretical model guided the design, optimizing device dynamics and assistive force delivery. A generic interaction controller, combined with fixed system parameters, provided effective assistance across participants without the need for subject-specific tuning, simplifying deployment in practical settings. During dynamic tasks, the exosuit significantly reduced biceps muscle activity compared to the unassisted condition, with an average reduction of 28.40%. In endurance tasks, it reduced infraspinatus activity by an average of 44.55% and mitigated increased muscle activation during load-carrying. Subjective assessments indicated lower perceived physical demand, while the System Usability Scale confirmed high usability and acceptance. These findings highlight the exosuit's ability to reduce physical effort and improve muscle coordination while maintaining comfort. The robust mechatronic design enabled effective assistance through a generic controller, emphasizing the importance of hardware reliability.

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.

This work presents a description of the EXOSMOOTH project, oriented to the benchmarking of lower limb exoskeletons performance. In the field of assisted walking by powered lower limb exoskeletons, the EXOSMOOTH project proposes an experiment that targets two scientific questions. The first question is related to the effectiveness of a novel control strategy for smooth assistance. Current assist strategies are based on controllers that switch the assistance level based on the gait segmentation provided by a finite state machine. The proposed strategy aims at managing phase transitions to provide a smoother assistance to the user, thus increasing the device transparency and comfort for the user. The second question is the role of the actuation at the ankle joint in assisted walking. Many novel exoskeletons devised for industrial applications do not feature an actuated ankle joint. In the EXOSMOOTH project, the ankle joint actuation will be one experimental factor to have a direct assessment of the role of an actuated joint in assisted walking. Preliminary results of 15 healthy subjects walking at different speeds while wearing a lower limb exoskeleton supported the rationale behind this question: having an actuated ankle joint could potentially reduce the torques applied by the user by a maximum value of 85 Nm. The two aforementioned questions will be investigated in a protocol that includes walking on a treadmill and on flat ground, with or without slope, and with a load applied on the back. In addition, the interaction forces measured at the exoskeleton harnesses will be used to assess the comfort of the user and the effectiveness of the control strategy to improve transparency.

Lower limbs exoskeletons provide assistance during standing, squatting, and walking. Gait dynamics, in particular, implies a change in the configuration of the device in terms of contact points, actuation, and system dynamics in general. In order to provide a comfortable experience and maximize performance, the exoskeleton should be controlled smoothly and in a transparent way, which means respectively, minimizing the interaction forces with the user and jerky behavior due to transitions between different configurations. A previous study showed that a smooth control of the exoskeleton can be achieved using a gait phase segmentation based on joint kinematics. Such a segmentation system can be implemented as linear regression and should be personalized for the user after a calibration procedure. In this work, a nonlinear segmentation function based on neural networks is implemented and compared with linear regression. An on-line implementation is then proposed and tested with a subject.

The growing interest in wearable robots for assistance and rehabilitation purposes opens the challenge for developing intuitive and natural control strategies. Among several human-machine interaction approaches, myoelectric control consists in decoding the motor intention from muscular activity (or EMG signals) with the aim at moving the assistive robotic device accordingly, thus establishing an intimate human-machine connection. In this scenario, bio-inspired approaches, e.g. synergy-based controllers, are reveling to be the most robust.In this work, the authors presented an undercomplete autoencoder (AE) to extract muscles synergies for motion intention detection. The proposed AE topology has been validate with EMG signals acquired from the main upper limb muscles during planar isometric reaching tasks performed in a virtual environment while wearing an exoskeleton. The presented AE have shown promising results in muscle synergy extraction comparing its performance with the Non-Negative Matrix Factorization algorithm, i.e. the most used approach in literature. The synergy activations extracted with the AE have been then used for estimating the moment applied at the shoulder and elbow joints. Comparing such estimation with the results of other synergy-based techniques already proposed in literature, it emerged that the proposed method achieves comparable performance.