Notwithstanding the advancement of modern bionic hands and the large variety of prosthetic hands in the market, commercial devices still present limited acceptance and percentage of daily use. While commercial prostheses present rigid mechanical structures, emerging trends in the design of robotic hands are moving towards soft technologies. Although this approach is inspired by nature and could be promising for prosthetic applications, there is scant literature concerning its benefits for end-users and in real-life scenarios. In this work, we evaluate and assess the role and the benefits of soft robotic technologies in the field of prosthetics. We propose a thorough comparison between rigid and soft characteristics of two poly-articulated hands in 5 non-expert myo-electric prosthesis users in pre- and post-therapeutic training conditions. The protocol includes two standard functional assessments, three surveys for user-perception, and three customized tests to evaluate the sense of embodiment. Results highlight that rigid hands provide a more precise grasp, while soft properties show higher functionalities thanks to their adaptability to different requirements, intuitive use and more natural execution of activities of daily living. This comprehensive evaluation suggests that softness could also promote a quick integration of the system in non-expert users.

To physically interact with a rich variety of environments and to match situation-dependent requirements, humans adapt both the force and stiffness of their limbs. Reflecting this behavior in prostheses may promote a more natural and intuitive control and, consequently, improve prostheses acceptance in everyday life. This pilot study proposes a method to control a prosthetic robot hand and its impedance, and explores the utility of variable stiffness when performing activities of daily living and physical social interactions. The proposed method is capable of a simultaneous and proportional decoding of position and stiffness intentions from two surface electro-myographic sensors placed over a pair of antagonistic muscles. The feasibility of our approach is validated and compared to existing control modalities in a preliminary study involving one prosthesis user. The algorithm is implemented in a soft under-actuated prosthetic hand (SoftHand Pro). Then, we evaluate the usability of the proposed approach while executing a variety of tasks. Among these tasks, the user interacts with other 12 able-bodied subjects, whose experiences were also assessed. Several statistically significant aspects from the System Usability Scale indicate user's preference of variable stiffness control over low or high constant stiffness due to its reactivity and adaptability. Feedback reported by able-bodied subjects reveal a general tendency to favor soft interaction, i.e., low stiffness, which is perceived more human-like and comfortable. These combined results suggest the use of variable stiffness as a viable compromise between firm control and safe interaction which is worth investigating further.

Despite the advances in bionic reconstruction of missing limbs, the control of robotic limbs is still limited and, in most cases, not felt to be as natural by users. In this study, we introduce a control approach that combines robotic design based on postural synergies and neural decoding of synergistic behavior of spinal motoneurons. We developed a soft prosthetic hand with two degrees of actuation that realizes postures in a two-dimensional linear manifold generated by two postural synergies. Through a manipulation task in nine participants without physical impairment, we investigated how to map neural commands to the postural synergies. We found that neural synergies outperformed classic muscle synergies in terms of dimensionality and robustness. Leveraging these findings, we developed an online method to map the decoded neural synergies into continuous control of the two-synergy prosthetic hand, which was tested on 11 participants without physical impairment and three prosthesis users in real-time scenarios. Results demonstrated that combined neural and postural synergies allowed accurate and natural control of coordinated multidigit actions (>90% of the continuous mechanical manifold could be reached). The target hit rate for specific hand postures was higher with neural synergies compared with muscle synergies, with the difference being particularly pronounced for prosthesis users (prosthesis users, 82.5% versus 35.0%; other participants, 79.5% versus 54.5%). This demonstration of codesign of multisynergistic robotic hands and neural decoding algorithms enabled users to achieve natural modular control to span infinite postures across a two-dimensional space and to execute dexterous tasks, including in-hand manipulation, not feasible with other approaches.

Although recent technological developments in the field of bionic upper limb prostheses, their rejection rate remains excessively high. The reasons are diverse (e.g. lack of functionality, control complexity, and comfortability) and most of these are reported only through self-rated questionnaires. Indeed, there is no quantitative evaluation of the extent to which a novel prosthetic solution can effectively address users’ needs compared to other technologies. This manuscript discusses the challenges and limitations of current upper limb prosthetic devices and evaluates their functionality through a standard functional assessment, the Assessment of Capacity for Myoelectric Control (ACMC). To include a good representation of technologies, the authors collect information from participants in the Cybathlon Powered Arm Prostheses Race 2016 and 2020. The article analyzes 7 hour and 41 min of video footage to evaluate the performance of different prosthetic devices in various tasks inspired by activities of daily living (ADL). The results show that commercially-available rigid hands perform well in dexterous grasping, while body-powered solutions are more reliable and convenient for competitive environments. The article also highlights the importance of wrist design and control modality for successful execution of ADL. Moreover, we discuss the limitations of the evaluation methodology and suggest improvements for future assessments. With regard to future development, this work highlights the need for research in intuitive control of multiple degrees of freedom, adaptive solutions, and the integration of sensory feedback.

Background: Among commercially-available upper-limb prostheses, the two most often used solutions are simple hook-style grippers and poly-articulated hands, which present a higher number of articulations and show a closer resemblance to biological limbs. In their majority, the former type of prostheses is body-powered, while the second type is controlled by myoelectric signals. Body-powered grippers are easy to control and allow a simple form of force feedback, frequently appreciated by users. However, they present limited versatility. Poly-articulated hands afford a wide range of grasp and manipulation types, but require enough residual muscle activation for dexterous control. Several factors, e.g. level of limb loss, personal preferences, cost, current occupation, and hobbies can influence the preference for one option over the other, and is always a result of the trade-off between system performance and users’ needs. Methods: The SoftHand Pro (SHP) is an artificial hand platform that has 19 independent joints (degrees-of-freedom), but is controlled by a single input. The design of this prosthesis is inspired by the concept of postural synergies in motor control and implemented with soft-robotic technologies. Their combination provides increased robustness, safe interaction and the execution of diverse grasps. The potential of the SHP is fully unleashed when users learn how to exploit its features and create an intimate relationship between the technical aspects of the prosthesis design and its control by the user. Results: The great versatility of the SoftHand Pro (a reasearch protpotype) permitted its adaptation to the user requirements. This was experienced by the SoftHand Pro Team during the preparation for different CYBATHLON events (from 2016 to 2020). The mixed power and dexterous hand operations required by each task of the race is inspired by everyday tasks. Our prosthesis was driven by different pilots, with different habits and backgrounds. Consequently, the hand control modality was customized according to the user’s preferences. Furthermore, the CYBATHLON tasks had some variations in this period, promoting the continuous development of our technology with a user-centered approach. In this paper, we describe the participation and preparation of the SoftHand Pro Team from 2016 to 2020 with three pilots and two different activation modalities, hybrid body-controlled and myoelectric control. Conclusions: We introduced our pilots, the implementation of the two control modalities, and describe the successful participation in all CYBATHLON events. This work proves the versatility of the system towards the user’s preferences and the changes in the race requirements. Finally, we discussed how the CYBATHLON experience and the training in the real-world scenario have driven the evolution of our system and gave us remarkable insights for future perspectives.

Robotic hand engineers usually focus on finger capabilities, often disregarding the palm contribution. Inspired by human anatomy, this paper explores the advantages of including a flexible concave palm into the design of a robotic hand actuated by soft synergies. We analyse how the inclusion of an articulated palm improves finger workspace and manipulability. We propose a mechanical design of a modular palm with two elastic rolling-contact palmar joints, that can be integrated on the Pisa/IIT SoftHand, without introducing additional motors. With this prototype, we evaluate experimentally the grasping capabilities of a robotic palm. We compare its performance to that of the same robotic hand with the palm fixed, and to that of a human hand. To assess the effective grasp quality achieved by the three systems, we measure the contact area using paint-transfer patterns in different grasping actions. Preliminary grasping experiments show a closer resemblance of the soft-palm robotic hand to the human hand. Results evidence a higher adaptive capability and a larger involvement of all fingers in grasping.