The journey of a prosthetic user is characterized by the opportunities and the limitations of a device that should enable activities of daily living (ADL). In particular, experiencing a bionic hand as a functional (and, advantageously, embodied) limb constitutes the premise for promoting the practice in using the device, mitigating the risk of its abandonment. In order to achieve such a result, different aspects need to be considered for making the artificial limb an effective solution to accomplish ADL. According to such a perspective, this review aims at presenting the current issues and at envisioning the upcoming breakthroughs in upper limb prosthetic devices. We first define the sources of input and feedback involved in the system control (at user-level and device-level), alongside the related algorithms used in signal analysis. Moreover, the paper focuses on the user-centered design challenges and strategies that guide the implementation of novel solutions in this area in terms of technology acceptance, embodiment, and, in general, human-machine integration based on co-adaptive processes. We here provide the readers (belonging to the target communities of researchers, designers, developers, clinicians, industrial stakeholders, and end-users) with an overview of the state-of-the-art and the potential innovations in bionic hands features, hopefully promoting interdisciplinary efforts for solving current issues of upper limb prostheses. The integration of different perspectives should be the premise to a transdisciplinary intertwining leading to a truly holistic comprehension and improvement of the bionic hands design. Overall, this paper aims to move the boundaries in prosthetic innovation beyond the development of a tool and toward the engineering of human-centered artificial limbs.

Developing a prosthetic system that emulates the complexity of the human upper limb is a formidable challenge. Unfortunately, abandonment rates for such devices remain high, primarily due to the limited intuitiveness of control and poor dexterity. Specifically, inadequate wrist mobility, i.e., the absence of actively controllable flexion-extension and pronation-supination degrees of freedom, often results in subpar dexterity in upper limb prostheses. This work introduces an anthropomorphic wrist prosthesis featuring active flexion-extension and pronation-supination capabilities, integrated with the poly-articulated Hannes hand. The central focus of this study is to compare the functionality of this prosthetic system with the natural wrist movement of healthy participants, demonstrating that the biomechanical range of motion falls within that of the mechatronic system. The overarching goal is to improve the performance of trans-radial prostheses by enhancing their dexterity and overall functionality. Our preliminary findings from healthy subjects demonstrate that the incorporation of a 2 Degrees-of-Freedom active biomimetic wrist into the prosthesis can approximate human-like capabilities in upper limb prostheses. Moreover, the resulting development confirm its enhanced dexterity when operated by amputees. These results provide valuable insights into the potential applications of this technology for amputees, offering a basis for future investigations.

Noninvasive human-machine interfaces such as surface electromyography (sEMG) have long been employed for controlling robotic prostheses. However, classical controllers are limited to few degrees of freedom (DoF). More recently, machine learning methods have been proposed to learn personalized controllers from user data. While promising, they often suffer from distribution shift during long-term usage, requiring costly model re-training. Moreover, most prosthetic sEMG sensors have low spatial density, which limits accuracy and the number of controllable motions. In this work, we address both challenges by introducing a novel myoelectric prosthetic system integrating a high density-sEMG (HD-sEMG) setup and incremental learning methods to accurately control 7 motions of the Hannes prosthesis. First, we present a newly designed, compact HD-sEMG interface equipped with 64 dry electrodes positioned over the forearm. Then, we introduce an efficient incremental learning system enabling model adaptation on a stream of data. We thoroughly analyze multiple learning algorithms across 7 subjects, including one with limb absence, and 6 sessions held in different days covering an extended period of several months. The size and time span of the collected data represent a relevant contribution for studying long-term myocontrol performance. Therefore, we release the DELTA dataset together with our experimental code.

Objective: The bidirectional communication between the user and the prosthesis is an important requirement when developing prosthetic hands. Proprioceptive feedback is fundamental to perceiving prosthesis movement without the need for constant visual attention. We propose a novel solution to encode wrist rotation using a vibromotor array and Gaussian interpolation of vibration intensity. The approach generates tactile sensation that smoothly rotates around the forearm congruently with prosthetic wrist rotation. The performance of this scheme was systematically assessed for a range of parameter values (number of motors and Gaussian standard deviation). Methods : Fifteen non-disabled subjects and one individual with congenital limb deficiency used vibrational feedback to control a virtual hand in the target-achievement control test. Performance was assessed by end-point error and efficiency as well as subjective impressions. Results : The results showed a preference for smooth feedback and a higher number of motors (8 and 6 versus 4). With 8 and 6 motors, the standard deviation, determining the sensation spread and continuity, could be modulated through a broad range of values (0.1 - 2) without a significant performance loss. The overall average error and efficiency across these feedback configurations were ∼ 10% and ∼ 30%, respectively. For low values of standard deviation (0.1-0.5), the number of motors could be reduced to 4 without a significant performance decrease. Conclusion: The study demonstrated that the developed strategy provided meaningful rotation feedback. Moreover, the results indicate that the Gaussian standard deviation could be used as an independent parameter to encode an additional feedback variable. Significance: The proposed method is a flexible and effective approach to provide proprioceptive feedback while adjusting the trade-off between sensation quality and the number of vibromotors.

Closing the control loop between users and their prostheses by providing artificial sensory feedback is a fundamental step toward the full restoration of lost sensory-motor functions. We propose a novel approach to provide artificial proprioceptive feedback about two degrees of freedom using a single array of 8 vibration motors (compact solution). The performance afforded by the novel method during an online closed-loop control task was compared to that achieved using the conventional approach, in which the same information was conveyed using two arrays of 8 and 4 vibromotors (one array per degree of freedom), respectively. The new method employed Gaussian interpolation to modulate the intensity profile across a single array of vibration motors (compact feedback) to convey wrist rotation and hand aperture by adjusting the mean and standard deviation of the Gaussian, respectively. Ten able-bodied participants and four transradial amputees performed a target achievement control test by utilizing pattern recognition with compact and conventional vibrotactile feedback to control the Hannes prosthetic hand (test conditions). A second group of ten able-bodied participants performed the same experiment in control conditions with visual and auditory feedback as well as no-feedback. Conventional and compact approaches resulted in similar positioning accuracy, time and path efficiency, and total trial time. The comparison with control condition revealed that vibrational feedback was intuitive and useful, but also underlined the power of incidental feedback sources. Notably, amputee participants achieved similar performance to that of able-bodied participants. The study therefore shows that the novel feedback strategy conveys useful information about prosthesis movements while reducing the number of motors without compromising performance. This is an important step toward the full integration of such an interface into a prosthesis socket for clinical use.

Introduction: In recent years, hand prostheses achieved relevant improvements in term of both motor and functional recovery. However, the rate of devices abandonment, also due to their poor embodiment, is still high. The embodiment defines the integration of an external object – in this case a prosthetic device – into the body scheme of an individual. One of the limiting factors causing lack of embodiment is the absence of a direct interaction between user and environment. Many studies focused on the extraction of tactile information via custom electronic skin technologies coupled with dedicated haptic feedback, though increasing the complexity of the prosthetic system. Contrary wise, this paper stems from the authors' preliminary works on multi-body prosthetic hand modeling and the identification of possible intrinsic information to assess object stiffness during interaction.Methods: Based on these initial findings, this work presents the design, implementation and clinical validation of a novel real-time stiffness detection strategy, without ad-hoc sensing, based on a Non-linear Logistic Regression (NLR) classifier. This exploits the minimum grasp information available from an under-sensorized and under-actuated myoelectric prosthetic hand, Hannes. The NLR algorithm takes as input motor-side current, encoder position, and reference position of the hand and provides as output a classification of the grasped object (no-object, rigid object, and soft object). This information is then transmitted to the user via vibratory feedback to close the loop between user control and prosthesis interaction. This implementation was validated through a user study conducted both on able bodied subjects and amputees.Results: The classifier achieved excellent performance in terms of F1Score (94.93%). Further, the able-bodied subjects and amputees were able to successfully detect the objects' stiffness with a F1Score of 94.08% and 86.41%, respectively, by using our proposed feedback strategy. This strategy allowed amputees to quickly recognize the objects' stiffness (response time of 2.82 s), indicating high intuitiveness, and it was overall appreciated as demonstrated by the questionnaire. Furthermore, an embodiment improvement was also obtained as highlighted by the proprioceptive drift toward the prosthesis (0.7 cm).