Supernumerary robotic limbs (SRLs) are wearable robots designed to enhance the sensorimotor abilities of humans. SRLs can be used to compensate for lost functions in patients with motor deficits and, more in general, to augment the sensorimotor capabilities of humans to interact with the environment. The design and control of SRLs present several challenges. SRLs must have high levels of ergonomics and wearability and, depending on the application, they might also require enhanced robustness and strength. Supernumerary robotics differs from collaborative robotics, since SRLs are not collaborative external agents but rather act under the direct command of the operator who, thanks to the use of suitable interfaces, gains the control of some actions of the SRLs. From the control point of view, it is fundamental to find the right trade-off between the degrees of freedom that are under the direct control of the user and the level of robot autonomy. The adoption of feedback interfaces can help the human to better command and use the SRL. In this review, we discuss all these aspects, relating them to the current literature on SRLs. We also present the main applications and the potential of these relatively recent devices, as well as the main neuroscientific questions they rise on the implications of their use on the users’ body schema.

The human hand represents a complex fascinating system with highly sensitive sensory capabilities and dexterous grasping and manipulation functionalities. As a consequence, estimating the hand pose and at the same time having the capability to provide haptic feedback in a wearable way may benefit areas such as rehabilitation, human-robot interaction, gaming, and many more. Existing solutions allow us to accurately measure the hand configuration and provide effective force feedback to the user. However, they have limited wearability/portability. In this paper, we present the wearable sensing/actuation system glove for enhanced sensing and touching (GESTO). It is based on inertial and magnetic sensors for hand tracking, coupled with cutaneous devices for the force feedback rendering. Unlike vision-based tracking systems, the sensing glove does not suffer from occlusion problems and lighting conditions. We properly designed the cutaneous devices in order to reduce possible interferences with the magnetic sensors and performed an experimental validation on ten healthy subjects. In order to measure the estimation accuracy of GESTO, we used a high-precision optical tracker. A comparison between using the glove with and without the haptic devices shows that the presence of them does not induce a statistically significant increase in the estimation error. Experimental results revealed the effectiveness of the proposed approach. The accuracy of our system, 3.32° mean estimation error in the worst case, is comparable with the human ability of discriminating finger joint angle.

Robotic devices for rehabilitation and training is a promising and challenging research topic with a potentially huge social impact. The availability of tools for autonomously performing physiotherapy exercises increases their efficiency, provides supplementary information about results and progress, reduces physiotherapists’ efforts and the need of their physical presence during exercise sessions, and encourages autonomy and independence in people with disabilities. Nevertheless, supportive technologies developed without the inputs and feedback of the end-user throughout the design process are less likely to be adopted for their intended purpose and use case. In this article, we propose a modular hand/wrist exoskeleton that actuates the wrist flexion/extension and adduction/abduction motions and hand fingers flexion/extension motions. It is designed to be wearable and easy to control and manage and can be used by the patient in collaboration with the physiotherapist or autonomously. A user-centered design perspective has been employed in all the design and development phases. This article introduces the main features of the device and presents some tests conducted with a user having limited hand and wrist mobility.

Estimating the limbs pose in a wearable way may benefit multiple areas such as rehabilitation, teleoperation, human-robot interaction, gaming, and many more. Several solutions are commercially available, but they are usually expensive or not wearable/portable. We present a wearable pose estimation system (WePosE), based on inertial measurements units (IMUs), for motion analysis and body tracking. Differently from camerabased approaches, the proposed system does not suffer from occlusion problems and lighting conditions, it is cost effective and it can be used in indoor and outdoor environments. Moreover, since only accelerometers and gyroscopes are used to estimate the orientation, the system can be used also in the presence of iron and magnetic disturbances. An experimental validation using a high precision optical tracker has been performed. Results confirmed the effectiveness of the proposed approach.

Human guidance in situations where the users cannot rely on their main sensory modalities, such as assistive or search-and-rescue scenarios, is a challenging task. In this letter, we address the problem of guiding users along collision-free paths in dynamic environments, assuming that they cannot rely on their main sensory modalities. In order to safely guide the subjects, we adapt the optimal reciprocal collision avoidance to our specific problem. The proposed algorithm takes into account the stimuli which can be displayed to the users and the motion uncertainty of the users when reacting to the provided stimuli. The proposed algorithm was evaluated in three different dynamic scenarios. A total of 18 blindfolded human subjects were asked to follow haptic cues in order to reach a target area while avoiding real static obstacles and moving users. Three metrics such as time to reach the goal, length of the trajectories, and minimal distance from the obstacles are considered to compare results obtained using this approach and experiments performed without visual impairments. Experimental results reveal that blindfolded subjects are successfully able to avoid collisions and safely reach the targets in all the performed trials. Although in this letter we display directional cues via haptic stimuli, we believe that the proposed approach can be general and tuned to work with different haptic interfaces and/or feedback modalities.

The alarming morbidity of COVID-19 has drawn the attention to the social role of hygiene rules, with a particular focus on the importance of limiting face-touch occurrences. To deal with this aspect, we present No Face-Touch, a system able to estimate hand proximity to face and notify the user whenever a face-touch movement is detected. In its complete setup, the system consists of an application running on the smartwatch and a wearable accessory. Its ease of implementation allows this solution to be ready-to-use and large-scale deployable. We developed two gesture detection approaches compatible with sensors embedded in recent smartwatches, i.e. inertial and magnetic sensors. After preliminary tests to tune target gesture parameters, we tested the two approaches and compared their accuracy. The final phase of this project consisted in exploiting the most robust approach in a daily living scenario during a 6-days campaign. Experimental results revealed the effectiveness of the proposed system, demonstrating its impact in reducing the number of face-touches and their duration.