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.

This study examined the trainability of the proprioceptive sense and explored the relationship between proprioception and motor learning. With vision blocked, human learners had to perform goal-directed wrist movements relying solely on proprioceptive/haptic cues to reach several haptically specified targets. One group received additional somatosensory movement error feedback in form of vibro-tactile cues applied to the skin of the forearm. We used a haptic robotic device for the wrist and implemented a 3-day training regimen that required learners to make spatially precise goal-directed wrist reaching movements without vision. We assessed whether training improved the acuity of the wrist joint position sense. In addition, we checked if sensory learning generalized to the motor domain and improved spatial precision of wrist tracking movements that were not trained. The main findings of the study are: First, proprioceptive acuity of the wrist joint position sense improved after training for the group that received the combined proprioceptive/haptic and vibro-tactile feedback (VTF). Second, training had no impact on the spatial accuracy of the untrained tracking task. However, learners who had received VTF significantly reduced their reliance on haptic guidance feedback when performing the untrained motor task. That is, concurrent VTF was highly salient movement feedback and obviated the need for haptic feedback. Third, VTF can be also provided by the limb not involved in the task. Learners who received VTF to the contralateral limb equally benefitted. In conclusion, somatosensory training can significantly enhance proprioceptive acuity within days when learning is coupled with vibro-tactile sensory cues that provide feedback about movement errors. The observable sensory improvements in proprioception facilitates motor learning and such learning may generalize to the sensorimotor control of the untrained motor tasks. The implications of these findings for neurorehabilitation are discussed.

Neurological diseases causing motor/cognitive impairments are among the most common causes of adult-onset disability. More than one billion of people are affected worldwide, and this number is expected to increase in upcoming years, because of the rapidly aging population. The frequent lack of complete recovery makes it desirable to develop novel neurorehabilitative treatments, suited to the patients, and better targeting the specific disability. To date, rehabilitation therapy can be aided by the technological support of robotic-based therapy, non-invasive brain stimulation, and neural interfaces. In this perspective, we will review the above methods by referring to the most recent advances in each field. Then, we propose and discuss current and future approaches based on the combination of the above. As pointed out in the recent literature, by combining traditional rehabilitation techniques with neuromodulation, biofeedback recordings and/or novel robotic and wearable assistive devices, several studies have proven it is possible to sensibly improve the amount of recovery with respect to traditional treatments. We will then discuss the possible applied research directions to maximize the outcome of a neurorehabilitation therapy, which should include the personalization of the therapy based on patient and clinician needs and preferences.

Progress in decoding neural signals has enabled the development of interfaces that translate cortical brain activities into commands for operating robotic arms and other devices. The electrical stimulation of sensory areas provides a means to create artificial sensory information about the state of a device. Taken together, neural activity recording and microstimulation techniques allow us to embed a portion of the central nervous system within a closed-loop system, whose behavior emerges from the combined dynamical properties of its neural and artificial components. In this study we asked if it is possible to concurrently regulate this bidirectional brain-machine interaction so as to shape a desired dynamical behavior of the combined system. To this end, we followed a well-known biological pathway. In vertebrates, the communications between brain and limb mechanics are mediated by the spinal cord, which combines brain instructions with sensory information and organizes coordinated patterns of muscle forces driving the limbs along dynamically stable trajectories. We report the creation and testing of the first neural interface that emulates this sensory-motor interaction. The interface organizes a bidirectional communication between sensory and motor areas of the brain of anaesthetized rats and an external dynamical object with programmable properties. The system includes (a) a motor interface decoding signals from a motor cortical area, and (b) a sensory interface encoding the state of the external object into electrical stimuli to a somatosensory area. The interactions between brain activities and the state of the external object generate a family of trajectories converging upon a selected equilibrium point from arbitrary starting locations. Thus, the bidirectional interface establishes the possibility to specify not only a particular movement trajectory but an entire family of motions, which includes the prescribed reactions to unexpected perturbations.

The development of robotic devices for rehabilitation is a fast-growing field. Nowadays, thanks to novel technologies that have improved robots' capabilities and offered more cost-effective solutions, robotic devices are increasingly being employed during clinical practice, with the goal of boosting patients' recovery. Robotic rehabilitation is also widely used in the context of neurological disorders, where it is often provided in a variety of different fashions, depending on the specific function to be restored. Indeed, the effect of robot-aided neurorehabilitation can be maximized when used in combination with a proper training regimen (based on motor control paradigms) or with non-invasive brain machine interfaces. Therapy-induced changes in neural activity and behavioral performance, which may suggest underlying changes in neural plasticity, can be quantified by multimodal assessments of both sensorimotor performance and brain/muscular activity pre/post or during intervention. Here, we provide an overview of the most common robotic devices for upper and lower limb rehabilitation and we describe the aforementioned neurorehabilitation scenarios. We also review assessment techniques for the evaluation of robotic therapy. Additional exploitation of these research areas will highlight the crucial contribution of rehabilitation robotics for promoting recovery and answering questions about reorganization of brain functions in response to disease.In the last decades, innovative robotic technologies have been developed in order to effectively help clinicians during the neurorehabilitation process. The term "robotic technology" in this application domain refers to any mechatronic device with a certain degree of intelligence that can physically intervene on the behavior of the patient, optimizing and speeding up his/her sensorimotor recovery. The two key capabilities of these robots are: (1) Assessing the human sensorimotor function; and (2) re-training the human brain in order to improve the patient's quality of life. However, most of the studies in this field have been focused more on the development of the devices, whereas less effort was made on maximizing their efficacy for promoting recovery. The main challenge consists of designing effective training modalities, supported by appropriate control strategies. Thus, each robotic device supports a pre-defined training modality depending on the low-level control strategy implemented and also on the residual abilities of each patient. Usually, most of the rehabilitation devices implement a passive training modality (robot-driven, position control strategy), where the robot imposes the trajectories, and an active training modality (patient-driven), where the robot modulates its trajectory in response to the subject's intention to move [7,8]. However, among all the different training modalities, the most relevant is the assistive one. Assistive controllers help participants to move their impaired limbs according to the desired postures during grasping, reaching, or walki...

The correct human brain function is dependent on the activity of non‐neuronal cells called astrocytes. The bioelectrical properties of astrocytes in vitro do not closely resemble those displayed in vivo and the former are incapable of generating action potential; thus, reliable approaches in vitro for noninvasive electrophysiological recording of astrocytes remain challenging for biomedical engineering. Here it is found that primary astrocytes grown on a device formed by a forest of randomly oriented gold coated‐silicon nanowires, resembling the complex structural and functional phenotype expressed by astrocytes in vivo. The device enables noninvasive extracellular recording of the slow‐frequency oscillations generated by differentiated astrocytes, while flat electrodes failed on recording signals from undifferentiated cells. Pathophysiological concentrations of extracellular potassium, occurring during epilepsy and spreading depression, modulate the power of slow oscillations generated by astrocytes. A reliable approach to study the role of astrocytes function in brain physiology and pathologies is presented.

Brain-machine interfaces (BMIs) are mostly investigated as a means to provide paralyzed people with new communication channels with the external world. However, the communication between brain and artificial devices also offers a unique opportunity to study the dynamical properties of neural systems. This review focuses on bidirectional interfaces, which operate in two ways by translating neural signals into input commands for the device and the output of the device into neural stimuli. We discuss how bidirectional BMIs help investigating neural information processing and how neural dynamics may participate in the control of external devices. In this respect, a bidirectional BMI can be regarded as a fancy combination of neural recording and stimulation apparatus, connected via an artificial body. The artificial body can be designed in virtually infinite ways in order to observe different aspects of neural dynamics and to approximate desired control policies.

Sensorimotor learning is a bidirectional process associated with concurrent neuroplastic changes in the motor and somatosensory system. While motor memory consolidation and retention have been extensively studied during skill acquisition, little is known about the formation and consolidation of somatosensory memory associated with motor learning. Using a robotic exoskeleton, we tracked markers of somatosensory and motor learning while healthy participants trained to make goal-directed wrist reaching movements over five days and evaluated retention for up to 10 days after practice. Markers of somatosensory learning were changes in wrist position sense bias (systematic error) and precision (random error). The main results are as follows: First, somatosensory (proprioceptive) memory consolidation shows signs of cost savings with repeated sensorimotor training - the same feature is known for motor memory formation. Moreover, somatosensory learning generalized to untrained workspace. Second, somatosensory learning over days can be characterized as an early improvement in sensory precision and a later improvement in sensory bias. Third, the time course of learning gains in position sense acuity coincided with improvements in spatial movement accuracy. Finally, the gains of somatosensory learning were retained for several days. Improvements in position sense bias were still visible up to 3 days after the end of practice for the trained workspace positions, but decayed faster in the untrained workspace. Improvements in position sense precision were retained for up to 10 days and were workspace independent. The findings are consistent with the view that an internal model of somatosensory joint space is formed during motor learning.