Background: Shedding light on the neuroscientific mechanisms of human upper limb motor control, in both healthy and disease conditions (e.g., after a stroke), can help to devise effective tools for a quantitative evaluation of the impaired conditions, and to properly inform the rehabilitative process. Furthermore, the design and control of mechatronic devices can also benefit from such neuroscientific outcomes, with important implications for assistive and rehabilitation robotics and advanced human-machine interaction. To reach these goals, we believe that an exhaustive data collection on human behavior is a mandatory step. For this reason, we release U-Limb, a large, multi-modal, multi-center data collection on human upper limb movements, with the aim of fostering trans-disciplinary cross-fertilization. Contribution: This collection of signals consists of data from 91 able-bodied and 65 post-stroke participants and is organized at 3 levels: (i) upper limb daily living activities, during which kinematic and physiological signals (electromyography, electro-encephalography, and electrocardiography) were recorded; (ii) force-kinematic behavior during precise manipulation tasks with a haptic device; and (iii) brain activity during hand control using functional magnetic resonance imaging.

Modern active prostheses can be used to recover part of the motor function associated with the loss of a hand. Nevertheless, most sensory abilities are lost, and the person has to manage interaction by relying mostly on visual feedback. Despite intensive research devoted to convey touch related cues, very few solutions have been integrated in a real prosthesis worn by a user. This letter describes a soft pneumatic feedback system designed with integrability and wearability among its main concerns. At the system core, two soft pneumatic actuators are placed in contact with the subject's skin and inflated to provide pressure stimuli, which can be used to represent force exerted by the hand grasping. We report on the design and the characterization of the system, including behavioural experiments with able-bodied participants and one prosthesis user. Results from psychophysical, dexterity and usability tests show that the system has the potential to restore sensory feedback in hand amputees, and can be a useful tool for enabling a correct modulation of the force during grasping and manipulation tasks.

Despite technological advancements, upper limb prostheses still face high abandonment/rejection rates due to limitations in control interfaces and the absence of force/tactile feedback. Improving these aspects is crucial for enhancing user acceptance and optimizing functional performance. This pilot study, therefore, aims to understand which sensory feedback in combination with a soft robotic prosthetic hand could provide advantages for amputees, including performing everyday tasks. Tactile cues provided are contact information, grasping force, degree of hand opening, and combinations of this information. To transfer such feedback, different wearable systems are used, based on either vibrotactile or force stimulation in a non-invasive modality matching approach. Five volunteers with a trans-radial amputation controlling the new prosthetic hand SoftHand Pro performed a study protocol including everyday tasks. The results indicate the preference of amputees for a single, i.e. non-combined, feedback modality. The choice of appropriate haptic feedback seems to be subject and task-specific. Furthermore, in alignment with the participants' feedback, force feedback, with adequate granularity and clarity, could potentially be the most valuable feedback among those presented. Finally, the study suggests that prosthetic solutions should be preferred where amputees are able to choose their feedback system.

Artificial intelligence (AI) is rapidly reshaping gastrointestinal (GI) surgery by enhancing decision-making, intraoperative performance, and postoperative management. The integration of AI-driven systems is enabling more precise, data-informed, and personalized surgical interventions. This review provides a state-of-the-art overview of AI applications in GI surgery, organized into four key domains: surgical simulation, surgical computer vision, surgical data science, and surgical robot autonomy. A comprehensive narrative review of the literature was conducted, identifying relevant studies of technological developments in this field. In the domain of surgical simulation, AI enables virtual surgical planning and patient-specific digital twins for training and preoperative strategy. Surgical computer vision leverages AI to improve intraoperative scene understanding, anatomical segmentation, and workflow recognition. Surgical data science translates multimodal surgical data into predictive analytics and real-time decision support, enhancing safety and efficiency. Finally, surgical robot autonomy explores the progressive integration of AI for intelligent assistance and autonomous functions to augment human performance in minimally invasive and robotic procedures. Surgical AI has demonstrated significant potential across different domains, fostering precision, reproducibility, and personalization in GI surgery. Nevertheless, challenges remain in data quality, model generalizability, ethical governance, and clinical validation. Continued interdisciplinary collaboration will be crucial to translating AI from promising prototypes to routine, safe, and equitable surgical practice.

Soft hands allow to simplify the grasp planning to achieve a successful grasp, thanks to their intrinsic adaptability. At the same time, their usage poses new challenges, related to the adoption of classical sensing techniques originally developed for rigid end defectors, which provide fundamental information, such as to detect object slippage. Under this regard, model-based approaches for the processing of the gathered information are hard to use, due to the difficulties in modeling hand–object interaction when softness is involved. To overcome these limitations, in this article, we proposed to combine distributed tactile sensing and machine learning (recurrent neural network) to detect sliding conditions for a soft robotic hand mounted on a robotic manipulator, targeting the prediction of the grasp failure event and the direction of sliding. The outcomes of these predictions allow for an online triggering of a compensatory action performed with a second robotic arm–hand system, to prevent the failure. Despite the fact that the network is trained only with spherical and cylindrical objects, we demonstrate high generalization capabilities of our framework, achieving a correct prediction of the failure direction in $75\%$ of cases, and a $85\%$ of successful regrasps, for a selection of 12 objects of common use.

Grasping an object is one of the most common and complex actions performed by humans. The human brain can adapt and update the grasp dynamics through information received from sensory feedback. Prosthetic hands can assist with the mechanical performance of grasping, however currently commercially available prostheses do not address the disruption of the sensory feedback loop. Providing feedback about a prosthetic hand’s grasp force magnitude is a top priority for those with limb loss. This study tested a wearable haptic system, i.e., the Clenching Upper-Limb Force Feedback device (CUFF), which was integrated with a novel robotic hand (The SoftHand Pro). The SoftHand Pro was controlled with myoelectrics of the forearm muscles. Five participants with limb loss and nineteen able-bodied participants completed a constrained grasping task (with and without feedback) which required modulation of the grasp to reach a target force. This task was performed while depriving participants of incidental sensory sources (vision and hearing were significantly limited with glasses and headphones). The data were analyzed with Functional Principal Component Analysis (fPCA). CUFF feedback improved grasp precision for participants with limb loss who typically use body-powered prostheses as well as a sub-set of able-bodied participants. Further testing, that is more functional and allows participants to use all sensory sources, is needed to determine if CUFF feedback can accelerate mastery of myoelectric control or would benefit specific patient sub-groups.