Replacing the human hand with artificial devices of equal capability and effectiveness is a long-standing challenge. Even the most advanced hand prostheses, which have several active degrees of freedom controlled by the electrical signals of the stump’s residual muscles, do not achieve the complexity, dexterity, and adaptability of the human hand. Thus, prosthesis abandonment rate remains high due to poor embodiment. Here, we report a prosthetic hand called Hannes that incorporates key biomimetic properties that make this prosthesis uniquely similar to a human hand. By means of an holistic design approach and through extensive codevelopment work involving researchers, patients, orthopaedists, and industrial designers, our proposed device simultaneously achieves accurate anthropomorphism, biomimetic performance, and human-like grasping behavior that outperform what is required in the execution of activities of daily living (ADLs). To evaluate the effectiveness and usability of Hannes, pilot trials on amputees were performed. Tests and questionnaires were used before and after a period of about 2 weeks, in which amputees could autonomously use Hannes domestically to perform ADLs. Last, experiments were conducted to validate Hannes’s high performance and the human likeness of its grasping behavior. Although Hannes’s speed is still lower than that achieved by the human hand, our experiments showed improved performance compared with existing research or commercial devices.

In this paper, we address depth perception in the peripersonal space within three virtual environments: poor environment (dark room), reduced cues environment (wireframe room), and rich cues environment (a lit textured room). Observers binocularly viewed virtual scenes through a head-mounted display and evaluated the egocentric distance to spheres using visually open-loop pointing tasks. We conducted two different experiments within all three virtual environments. The apparent size of the sphere was held constant in the first experiment and covaried with distance in the second one. The results of the first experiment revealed that observers more accurately estimated depth in the rich virtual environment compared to the visually poor and the wireframe environments. Specifically, observers' pointing errors were small in distances up to 55 cm, and increased with distance once the sphere was further than 55 cm. Individual differences were found in the second experiment. Our results suggest that the quality of virtual environments has an impact on distance estimation within reaching space. Also, manipulating the targets' size cue led to individual differences in depth judgments. Finally, our findings confirm the use of vergence as an absolute distance cue in virtual environments within the arm's reaching space.

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Because of the complex anatomy of the human hand, in the absence of external constraints, a large number of postures and force combinations can be used to attain a stable grasp. Motor synergies provide a viable strategy to solve this problem of motor redundancy. In this study, we exploited the technical advantages of an innovative sensorized object to study unconstrained hand grasping within the theoretical framework of motor synergies. Participants were required to grasp, lift, and hold the sensorized object. During the holding phase, we repetitively applied external disturbance forces and torques and recorded the spatiotemporal distribution of grip forces produced by each digit. We found that the time to reach the maximum grip force during each perturbation was roughly equal across fingers, consistent with a synchronous, synergistic stiffening across digits. We further evaluated this hypothesis by comparing the force distribution of human grasping vs. robotic grasping, where the control strategy was set by the experimenter. We controlled the global hand stiffness of the robotic hand and found that this control algorithm produced a force pattern qualitatively similar to human grasping performance. Our results suggest that the nervous system uses a default whole hand synergistic control to maintain a stable grasp regardless of the number of digits involved in the task, their position on the objects, and the type and frequency of external perturbations. We studied hand grasping using a sensorized object allowing unconstrained finger placement. During object perturbation, the time to reach the peak force was roughly equal across fingers, consistently with a synergistic stiffening across fingers. Force distribution of a robotic grasping hand, where the control algorithm is based on global hand stiffness, was qualitatively similar to human grasping. This suggests that the central nervous system uses a default whole hand synergistic control to maintain a stable grasp.