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.

Residual motion of upper limbs in individuals who experienced cervical spinal cord injury (CSCI) is vital to achieve functional independence. Several interventions were developed to restore shoulder range of motion (ROM) in CSCI patients. However, shoulder ROM assessment in clinical practice is commonly limited to use of a simple goniometer. Conventional goniometric measurements are operator-dependent and require significant time and effort. Therefore, innovative technology for supporting medical personnel in objectively and reliably measuring the efficacy of treatments for shoulder ROM in CSCI patients would be extremely desirable. This study evaluated the validity of a customized wireless wearable sensors (Inertial Measurement Units—IMUs) system for shoulder ROM assessment in CSCI patients in clinical setting. Eight CSCI patients and eight healthy controls performed four shoulder movements (forward flexion, abduction, and internal and external rotation) with dominant arm. Every movement was evaluated with a goniometer by different testers and with the IMU system at the same time. Validity was evaluated by comparing IMUs and goniometer measurements using Intraclass Correlation Coefficient (ICC) and Limits of Agreement (LOA). inter-tester reliability of IMUs and goniometer measurements was also investigated. Preliminary results provide essential information on the accuracy of the proposed wireless wearable sensors system in acquiring objective measurements of the shoulder movements in CSCI patients.

Spinal cord injuries (SCI) often lead to upper limb impairment, necessitating innovative solutions for daily assistance beyond traditional rigid robotics due to their impractical weight and size. Despite still preliminary, soft wearables are arising as a possible solution to fill this gap. Here, we demonstrated an enhanced version of a soft inflatable robot that assists the shoulder against gravity, previously tested with different neurological conditions. Noteworthy improvements include a single-layer actuator, simplifying manufacturing, a built-in bending angle and a nylon hammock, for better armpit conformity. We characterized the actuator (approximately 8Nm at 90∘ at 70kPa) and demonstrated its good transparency, both from a kinematic and a muscular standpoint. Then, on 11 healthy individuals, we showed reductions in shoulder muscle activity (both at the anterior and middle deltoid) while performing a lift and hold task, ranging from 16% to almost 60% of the maximum voluntary contraction. More importantly, we confirmed these effects on two SCI individuals SCI, at two different stages of recovery. While preliminary, considering the limited exploration of soft wearable robots for the shoulder in SCI cases, this is a significant advancement playing an important role in the development of future soft technology for SCI assistance.

Hand neuroprostheses restore voluntary movement in people with paralysis through neuromodulation protocols. There are a variety of strategies to control hand neuroprostheses, which can be based on residual body movements or brain activity. There is no universally superior solution, rather the best approach may vary from patient to patient. Here, we propose a protocol based on an immersive virtual reality (VR) environment that simulates the use of a hand neuroprosthesis to allow patients to experience and familiarize themselves with various control schemes in clinically relevant tasks and choose the preferred one. We used our VR environment to compare two alternative control strategies over 5 days of training in four patients with C6 spinal cord injury: (a) control via the ipsilateral wrist, (b) control via the contralateral shoulder. We did not find a one-fits-all solution but rather a subject-specific preference that could not be predicted based only on a general clinical assessment. The main results were that the VR simulation allowed participants to experience the pros and cons of the proposed strategies and make an educated choice, and that there was a longitudinal improvement. This shows that our VR-based protocol is a useful tool for personalization and training of the control strategy of hand neuroprostheses, which could help to promote user comfort and thus acceptance.

Objective. Loss of hand function following spinal cord injury (SCI) severely impacts independence and quality of life. Restoring volitional hand control in individuals with SCI remains a critical challenge, addressed using different approaches, including physiotherapy, occupational therapy, and assistive technologies such as neuroprostheses and robotic systems. This study aimed to develop and evaluate an easy-to-setup and user-friendly electromyography (EMG) system for decoding attempted hand and finger movements in individuals with SCI. Approach. We implemented an EMG decoding system for hand opening/closing (2 classes) and single-finger flexion (3 classes) attempts, using a fast-donning 32-channel dry EMG sleeve integrated with a seamless deep learning-based decoding pipeline. Eight people with different classifications of SCI tested the system over two sessions while receiving real-time feedback through a virtual hand interface. Main results. The system achieved high online accuracy for hand open–close discrimination across all subjects (mean 92.5%), and an accuracy that was consistently above chance for single-finger flexion classification (mean 74.2%), with a greater error observed for more nuanced movements in participants with higher motor impairment. Performance plateaued quickly over time, indicating that user learning did not play a relevant role within the limited number of sessions performed. Significance. These findings demonstrate that our system allows good online decoding of different hand and finger movements in people with SCI. The approach supports pathways toward intuitive, digit-level control of neuroprostheses and robotic devices, with strong potential for clinical translation due to its quick setup and ease of use.