We present a novel soft exoskeleton providing active support for hand closing and opening. The main novelty is a different tendon routing, folded laterally on both sides of the hand, and adding clenching forces when the exoskeleton is activated. It improves the stability of the glove, diminishing slippage and detachment of tendons from the hand palm toward the grasping workspace. The clenching effect is released when the hand is relaxed, thus enhancing the user's comfort. The alternative routing allowed embedding a single actuator on the hand dorsum, resulting more compact with no remote cable transmission. Enhanced adaptation to the hand is introduced by the modular design of the soft polymer open rings. FEM simulations were performed to understand the interaction between soft modules and fingers. Different experiments assessed the desired effect of the proposed routing in terms of stability and deformation of the glove, evaluated the inter-finger compliance for non-cylindrical grasping, and characterized the output grasping force. Experiments with subjects explored the grasping performance of the soft exoskeleton with different hand sizes. A preliminary evaluation with Spinal Cord Injury patients was useful to highlight the strengths and limitations of the device when applied to the target scenario.

Robotic avatar technology has the potential to impact the future of human connectivity, transporting the sense of a human’s presence to a remote location anywhere and in real-time. In this regard, the recent ANA Avatar XPRIZE challenge fostered the development of the ultimate generation of non-autonomous robotic avatar designs. This paper is devoted to introducing our system proposal, allowing intuitive motion-based control and multi-modal feedback. The teleoperated robot endorses an anthropomorphic upper body with dual arms and dexterous hands for fine manipulation of even small objects, as well as an omnidirectional platform for improved and safe locomotion. Special focus is pointed at the crucial challenge of providing self-body perception to the operator, in particular regarding control of the arms and rendering of haptic sensations. To this end, we propose an upper-limb exoskeleton and a teleoperation architecture allowing retargeting of the operator’s skills and receiving tactile and kinesthetic force feedback with realistic and informative perception of the own’s arms. Lastly, the platform was validated during laboratory and challenge missions mimicking several social, cooperative, and cultural scenarios from which we report on the lesson learned and future improvements.

Over the past decade, considerable steps have been made in designing wearable assistive devices that reduce the metabolic cost of walking. As the field continues to advance, a growing emphasis is extending to human running, driven by the goal of improving efficiency and reducing metabolic strain. In our study, we developed a portable active exosuit to support hip extension during endurance running. The exosuit, featuring custom linear actuators and a control system synchronous with the wearer’s kinematics, initially underwent bench testing and, finally, a field evaluation with users running at their self-selected pace on an athletics track. Results from seven participants showed a significant reduction in the metabolic cost of transport when the exosuit was active. Specifically, we observed a 9.6% decrease with respect to the unpowered condition, with a 4.3% net saving compared to not wearing the device. Additionally, kinematic assessments revealed no alteration of the participants’ motion after toe-off, indicating transparency to physiological movement pattern during hip flexion. These findings highlight the potential of the exosuit to enhance athletic performance, opening new possibilities for running assistance in real-world scenarios.

Hand exoskeletons are increasingly used to support post-stroke reach-to-grasp, yet most intention-detection strategies trigger assistance from local hand events without considering the synergy between proximal arm transport and distal hand shaping. We evaluated whether proximal arm kinematics, alone or fused with EMG, can predict flexor and extensor digitorum activity for synergy-aligned hand assistance. We trained nine models per participant: linear regression (LINEAR), feedforward neural network (NONLINEAR), and LSTM, each under EMG-only, kinematics-only (KIN), and EMG+KIN inputs. Performance was assessed by RMSE on test trials and by a synergy-retention analysis, comparing synergy weights from original EMG versus a hybrid EMG in which extensor and flexor digitorum measure signals were replaced by model predictions. Results have shown that kinematic information can predict muscle activity even with a simple linear model (average RMSE around 30% of signal amplitude peak during go-to-grasp contractions), and synergy analysis indicated high cosine similarity between original and hybrid synergy weights (on average 0.87 for the LINEAR model). Furthermore, the LINEAR model with kinematics input has been tested in a real-time go-to-grasp motion, developing a high-level control strategy for a hand exoskeleton, to better simulate post-stroke rehabilitation scenarios. These results suggest the intrinsic synergistic motion of go-to-grasp actions, offering a practical path, in hand rehabilitation contexts, for timing hand assistance in synergy with arm transport and with minimal setup burden.