Patients suffering from neurological and musculoskeletal disorders often experience impaired upper limb function, significantly reducing their quality of life. In recent years, wearable robots have emerged as a promising solution to facilitate rehabilitation and assist in daily activities. Among these, tendon-driven actuation has been widely adopted; however, such systems face challenges in achieving precise position control compared to direct motor-driven systems. This is primarily due to the hysteresis and backlash resulting from the high compliance and elasticity of tendons, necessitating effective compensation strategies. In this paper, we implement an embedded compact sensor system for end-effector-level position tracking in a soft wearable robot designed for forearm pronation/supination and grasping motions. By integrating sensors at the end-effector, we enable real-time motion data acquisition and establish a closed-loop feedback mechanism that effectively compensates for the limitations of tendon-driven actuation, thereby enhancing overall control accuracy. Based on the embedded end-effect-level sensing system, we introduce a novel wearable robot kit for motion tracking that comprising two parts: a sensor-only exosuit for real-time capture of user hand and forearm movements, and a motor-equipped exosuit that replicates and assists movements based on the sensor feedback. This Leader-Follower Control Mode allows for accurate capture and rapid response to user motion intent, offering a new solution for applications in tele-control, mirror therapy, and motion synchronization.

Robot feet are crucial for maintaining dynamic stability and propelling the body during walking, especially on uneven terrains. Traditionally, robot feet were mostly designed as flat and stiff pieces of metal, which meets its limitations when the robot is required to step on irregular grounds, e.g., stones. While one could think that adding compliance under such feet would solve the problem, this is not the case. To address this problem, we introduced the SoftFoot, an adaptive foot design that can enhance walking performance over irregular grounds. The proposed design is completely passive and varies its shape and stiffness based on the exerted forces, through a system of pulley, tendons, and springs opportunely placed in the structure. This article outlines the motivation behind the SoftFoot and describes the theoretical model which led to its final design. The proposed system has been experimentally tested and compared with two analogous conventional feet, a rigid one and a compliant one, with similar footprints and soles. The experimental validation focuses on the analysis of the standing performance, measured in terms of the equivalent support surface extension and the compensatory ankle angle, and the rejection of impulsive forces, which is important in events such as stepping on unforeseen obstacles. Results show that the SoftFoot has the largest equivalent support surface when standing on obstacles, and absorbs impulsive loads in a way almost as good as a compliant foot.

Learning the advanced functionalities of modern myoelectric prostheses can be challenging and highly cognitive demanding for naive users. While virtual reality (VR) has recently emerged as a promising tool for neurorehabilitation, it is important to consider also the cognitive load aspect of the training process, for a more realistic assessment of users’ capabilities. This study aims to investigate the correlation between functional performance and cognitive demand when learning a myoelectric control method in an immersive virtual reality environment for training and assessment. The developed virtual training environment simulated activities of daily living, while the assessments included standard tests, as well as a motor-cognitive dual-task methodology that combined both aspects. The study was conducted with 10 able-bodied participants, who controlled a virtual multi-grip prosthesis using a conventional myoelectric control strategy that requires muscle co-activation to switch between power and precision grasp. Performance in terms of functionalities, cognitive load, and user perception was assessed before and after training. Results show that VR training led to an immediate improvement in functionalities, enabling fast object manipulation, and highlighting the importance of including cognitive evaluation in the training progress.

In recent years, a clear trend toward simplification emerged in the development of robotic hands. The use of soft robotic approaches has been a useful tool in this prospective, enabling complexity reduction by embodying part of grasping intelligence in the hand mechanical structure. Several hand prototypes designed according to such principles have accomplished good results in terms of grasping simplicity, robustness, and reliability. Among them, the Pisa/IIT SoftHand demonstrated the feasibility of a large variety of grasping tasks, by means of only one actuator and an opportunely designed tendon-driven differential mechanism. However, the use of a single degree of actuation prevents the execution of more complex tasks, like fine preshaping of fingers and in-hand manipulation. While possible in theory, simply doubling the Pisa/IIT SoftHand actuation system has several disadvantages, e.g., in terms of space and mechanical complexity. To overcome these limitations, we propose a novel design framework for tendon-driven mechanisms, in which the main idea is to turn transmission friction from a disturbance into a design tool. In this way, the degrees of actuation (DoAs) can be doubled with little additional complexity. By leveraging on this idea, we design a novel robotic hand, the Pisa/IIT SoftHand 2. We present here its design, modeling, control, and experimental validation. The hand demonstrates that by opportunely combining only two DoAs with hand softness, a large variety of grasping and manipulation tasks can be performed, only relying on the intelligence embodied in the mechanism. Examples include rotating objects with different shapes, opening a jar, and pouring coffee from a glass.

Sensing contact forces can be a key enabler for higher order dexterous manipulation in robotic hands. To sense the full range of contact pressure distribution would provide the best solution, but it is in practice unfeasible when considering very deformable and adaptable hands. This paper proposes an approach to estimate the contact forces acting on an under-actuated adaptable hand by combining the compliance model of the hand with the geometric configuration of the hand itself. This is done by introducing reasonable assumptions about the net contact force on each phalanx. The proposed method is introduced and experimentally validated on two fingers of the Pisa/IIT SoftHand.

State of the art of hand prosthetics is divided between simple and reliable gripper-like systems and sophisticate hi-tech poly-articular hands which tend to be complex both in their design and for the patient to operate. In this paper, we introduce the idea of decoding different movement intentions of the patient using the dynamic frequency content of the control signals in a natural way. We move a step further showing how this idea can be embedded in the mechanics of an underactuated soft hand by using only passive damping components. In particular we devise a method to design the hand hardware to obtain a given desired motion. This method, that we call of the dynamic synergies, builds on the theory of linear descriptor systems, and is based on the division of the hand movement in a slow and a fast components. We use this method to evolve the design of the Pisa/IIT SoftHand in a prototype prosthesis which, while still having 19 degrees of freedom and just one motor, can move along two different synergistic directions of motion (and combinations of the two), to perform either a pinch or a power grasp. Preliminary experimental results are presented, demonstrating the effectiveness of the proposed design.