Towards humanlike grasp in robotic hands: mechanical implementation of force synergies.

In the field of robotic hands, finger force coordination is usually achieved by complex mechanical structures and control systems. This study presents the design of a novel transmission system inspired from the physiological concept of force synergies, aiming to simplify the control of multifingered robotic hands. To this end, we collected human finger force data during six isometric grasping tasks, and force synergies (i.e. the synergy weightings and the corresponding activation coefficients) were extracted from the concatenated force data to explore their potential for force modulation. We then implemented two force synergies with a cable-driven transmission mechanism consisting of two spring-loaded sliders and five V-shaped bars. Specifically, we used fixed synergy weightings to determine the stiffness of the compression springs, and the displacements of sliders were determined by time-varying activation coefficients. The derived transmission system was then used to drive a five-finger robotic hand named SYN hand. We also designed a motion encoder to selectively activate desired fingers, making it possible for two motors to empower a variety of hand postures. Experiments on the prototype demonstrate successful grasp of a wide range of objects in everyday life, and the finger force distribution of SYN hand can approximate that of human hand during six typical tasks. To our best knowledge, this study shows the first attempt to mechanically implement force synergies for finger force modulation in a robotic hand. In comparison to state-of-the-art robotic hands with similar functionality, the proposed hand can distribute humanlike force ratios on the fingers by simple position control, rather than resorting to additional force sensors or complex control strategies. The outcome of this study may provide alternatives for the design of novel anthropomorphic robotic hands, and thus show application prospects in the field of hand prostheses and exoskeletons.

Development of a Two-Finger Haptic Robotic Hand with Novel Stiffness Detection and Impedance Control.

Haptic hands and grippers, designed to enable skillful object manipulation, are pivotal for high-precision interaction with environments. These technologies are particularly vital in fields such as minimally invasive surgery, where they enhance surgical accuracy and tactile feedback: in the development of advanced prosthetic limbs, offering users improved functionality and a more natural sense of touch, and within industrial automation and manufacturing, they contribute to more efficient, safe, and flexible production processes. This paper presents the development of a two-finger robotic hand that employs simple yet precise strategies to manipulate objects without damaging or dropping them. Our innovative approach fused force-sensitive resistor (FSR) sensors with the average current of servomotors to enhance both the speed and accuracy of grasping. Therefore, we aim to create a grasping mechanism that is more dexterous than grippers and less complex than robotic hands. To achieve this goal, we designed a two-finger robotic hand with two degrees of freedom on each finger; an FSR was integrated into each fingertip to enable object categorization and the detection of the initial contact. Subsequently, servomotor currents were monitored continuously to implement impedance control and maintain the grasp of objects in a wide range of stiffness. The proposed hand categorized objects' stiffness upon initial contact and exerted accurate force by fusing FSR and the motor currents. An experimental test was conducted using a Yale-CMU-Berkeley (YCB) object set consisted of a foam ball, an empty soda can, an apple, a glass cup, a plastic cup, and a small milk packet. The robotic hand successfully picked up these objects from a table and sat them down without inflicting any damage or dropping them midway. Our results represent a significant step forward in developing haptic robotic hands with advanced object perception and manipulation capabilities.

i-MYO: A multi-grasp prosthetic hand control system based on gaze movements, augmented reality, and myoelectric signals.

BACKGROUND: Controlling a multi-grasp prosthetic hand still remains a challenge. This study explores the influence of merging gaze movements and augmented reality in bionics on improving prosthetic hand control. METHODS: A control system based on gaze movements, augmented reality, and myoelectric signals (i-MYO) was proposed. In the i-MYO, the GazeButton was introduced into the controller to detect the grasp-type intention from the eye-tracking signals, and the proportional velocity scheme based on the i-MYO was used to control hand movement. RESULTS: The able-bodied subjects with no prior training successfully transferred objects in 91.6% of the cases and switched the optimal grasp types in 97.5%. The patient could successfully trigger the EMG to control the hand holding the objects in 98.7% of trials in around 3.2 s and spend around 1.3 s switching the optimal grasp types in 99.2% of trials. CONCLUSIONS: Merging gaze movements and augmented reality in bionics can widen the control bandwidth of prosthetic hand. With the help of i-MYO, the subjects can control a prosthetic hand using six grasp types if they can manipulate two muscle signals and gaze movement.

Novel near E-Field Topography Sensor for Human-Machine Interfacing in Robotic Applications.

This work investigates a new sensing technology for use in robotic human-machine interface (HMI) applications. The proposed method uses near E-field sensing to measure small changes in the limb surface topography due to muscle actuation over time. The sensors introduced in this work provide a non-contact, low-computational-cost, and low-noise method for sensing muscle activity. By evaluating the key sensor characteristics, such as accuracy, hysteresis, and resolution, the performance of this sensor is validated. Then, to understand the potential performance in intention detection, the unmodified digital output of the sensor is analysed against movements of the hand and fingers. This is done to demonstrate the worst-case scenario and to show that the sensor provides highly targeted and relevant data on muscle activation before any further processing. Finally, a convolutional neural network is used to perform joint angle prediction over nine degrees of freedom, achieving high-level regression performance with an RMSE value of less than six degrees for thumb and wrist movements and 11 degrees for finger movements. This work demonstrates the promising performance of this novel approach to sensing for use in human-machine interfaces.

Journey from human hands to robot hands: biological inspiration of anthropomorphic robotic manipulators.

The development of robotic hands that can replicate the complex movements and dexterity of the human hand has been a longstanding challenge for scientists and engineers. A human hand is capable of not only delicate operation but also crushing with power. For performing tasks alongside and in place of humans, an anthropomorphic manipulator design is considered the most advanced implementation, because it is able to follow humans' examples and use tools designed for people. In this article, we explore the journey from human hands to robot hands, tracing the historical advancements and current state-of-the-art in hand manipulator development. We begin by investigating the anatomy and function of the human hand, highlighting the bone-tendon-muscle structure, skin properties, and motion mechanisms. We then delve into the field of robotic hand development, focusing on highly anthropomorphic designs. Finally, we identify the requirements and directions for achieving the next level of robotic hand technology.

Muscle redistribution technique for expressing motion intention in patients with wrist-level amputation.

This study reports the preliminary results of a technique for redistributing muscles at the wrist in the stump of hand amputees by suturing the tendons to the dermis. The technique has the potential to improve control of hand prostheses by detecting movement intentions.

A Novel Low-Pressure Robotic Glove Based on CT-Optimized Finger Joint Kinematic Model for Long-Term Rehabilitation of Stroke Patients.

Wearing robotic gloves has become increasingly crucial for hand rehabilitation in stroke patients. However, traditional robotic gloves can exert additional pressure on the hand, such as prolonged use leading to poor blood circulation and muscle stiffness. To address these concerns, this work analyzes the finger kinematic model based on computerized tomography (CT) images of human hands, and designs a low-pressure robotic glove that conforms to finger kinematic characteristics. Firstly, physiological data on finger joint flexion and extension were collected through CT scans. The equivalent rotation centers of finger joints were obtained using the SURF and RANSAC algorithms. Furthermore, the trajectory of finger joint end and the correlation equation of finger joint motion were fitted, and a comprehensive finger kinematic model was established. Based on this finger kinematic model, a novel under-actuated exoskeleton mechanism was designed using a human-machine integration approach. The novel robotic glove fully aligns with the equivalent rotation centers and natural motion trajectories of the fingers, exerting minimal and evenly distributed dynamic pressure on the fingers, with a theoretical static pressure value of zero. Experiments involving gripping everyday objects demonstrated that the novel robotic glove significantly reduces the overall pressure on the fingers during grasping compared to the pneumatic glove and the traditional exoskeleton robotic glove. It is suitable for long-term use by stroke patients for rehabilitation training.

EMG-Based Control Strategies of a Supernumerary Robotic Hand for the Rehabilitation of Sub-Acute Stroke Patients: Proof of Concept.

One of the most frequent and severe aftermaths of a stroke is the loss of upper limb functionality. Therapy started in the sub-acute phase proved more effective, mainly when the patient participates actively. Recently, a novel set of rehabilitation and support robotic devices, known as supernumerary robotic limbs, have been introduced. This work investigates how a surface electromyography (sEMG) based control strategy would improve their usability in rehabilitation, limited so far by input interfaces requiring to subjects some level of residual mobility. After briefly introducing the phenomena hindering post-stroke sEMG and its use to control robotic hands, we describe a framework to acquire and interpret muscle signals of the forearm extensors. We applied it to drive a supernumerary robotic limb, the SoftHand-X, to provide Task-Specific Training (TST) in patients with sub-acute stroke. We propose and describe two algorithms to control the opening and closing of the robotic hand, with different levels of user agency and therapist control. We experimentally tested the feasibility of the proposed approach on four patients, followed by a therapist, to check their ability to operate the hand. The promising preliminary results indicate sEMG-based control as a viable solution to extend TST to sub-acute post-stroke patients.

Feasibility of force myography for the direct control of an assistive robotic hand orthosis in non-impaired individuals.

BACKGROUND: Assistive robotic hand orthoses can support people with sensorimotor hand impairment in many activities of daily living and therefore help to regain independence. However, in order for the users to fully benefit from the functionalities of such devices, a safe and reliable way to detect their movement intention for device control is crucial. Gesture recognition based on force myography measuring volumetric changes in the muscles during contraction has been previously shown to be a viable and easy to implement strategy to control hand prostheses. Whether this approach could be efficiently applied to intuitively control an assistive robotic hand orthosis remains to be investigated. METHODS: In this work, we assessed the feasibility of using force myography measured from the forearm to control a robotic hand orthosis worn on the hand ipsilateral to the measurement site. In ten neurologically-intact participants wearing a robotic hand orthosis, we collected data for four gestures trained in nine arm configurations, i.e., seven static positions and two dynamic movements, corresponding to typical activities of daily living conditions. In an offline analysis, we determined classification accuracies for two binary classifiers (one for opening and one for closing) and further assessed the impact of individual training arm configurations on the overall performance. RESULTS: We achieved an overall classification accuracy of 92.9% (averaged over two binary classifiers, individual accuracies 95.5% and 90.3%, respectively) but found a large variation in performance between participants, ranging from 75.4 up to 100%. Averaged inference times per sample were measured below 0.15 ms. Further, we found that the number of training arm configurations could be reduced from nine to six without notably decreasing classification performance. CONCLUSION: The results of this work support the general feasibility of using force myography as an intuitive intention detection strategy for a robotic hand orthosis. Further, the findings also generated valuable insights into challenges and potential ways to overcome them in view of applying such technologies for assisting people with sensorimotor hand impairment during activities of daily living.

Robust neural decoding for dexterous control of robotic hand kinematics.

BACKGROUND: Manual dexterity is a fundamental motor skill that allows us to perform complex daily tasks. Neuromuscular injuries, however, can lead to the loss of hand dexterity. Although numerous advanced assistive robotic hands have been developed, we still lack dexterous and continuous control of multiple degrees of freedom in real-time. In this study, we developed an efficient and robust neural decoding approach that can continuously decode intended finger dynamic movements for real-time control of a prosthetic hand. METHODS: High-density electromyogram (HD-EMG) signals were obtained from the extrinsic finger flexor and extensor muscles, while participants performed either single-finger or multi-finger flexion-extension movements. We implemented a deep learning-based neural network approach to learn the mapping from HD-EMG features to finger-specific population motoneuron firing frequency (i.e., neural-drive signals). The neural-drive signals reflected motor commands specific to individual fingers. The predicted neural-drive signals were then used to continuously control the fingers (index, middle, and ring) of a prosthetic hand in real-time. RESULTS: Our developed neural-drive decoder could consistently and accurately predict joint angles with significantly lower prediction errors across single-finger and multi-finger tasks, compared with a deep learning model directly trained on finger force signals and the conventional EMG-amplitude estimate. The decoder performance was stable over time and was robust to variations of the EMG signals. The decoder also demonstrated a substantially better finger separation with minimal predicted error of joint angle in the unintended fingers. CONCLUSIONS: This neural decoding technique offers a novel and efficient neural-machine interface that can consistently predict robotic finger kinematics with high accuracy, which can enable dexterous control of assistive robotic hands.

Mixed [Formula: see text]-synthesis tracking control and disturbance rejection in a robotic digit of an impaired human hand for anthropomorphic coordination.

In a partially impaired anthropomorphic hand, maintaining the movement coordination of the robotic digits with the central nervous system (CNS) and natural digits is crucial for robust performance. A challenge in the control perspective of movement coordination of a human hand is finding methods robust to the disturbances in a well-posed control problem of a biomechanical model. We use visco-elastic dynamics in the human palm frame of reference to explore the biomechanics of movement coordination to solve this control problem. Our biomechanical model incorporates the time delay due to actuation force, parametric uncertainty, exogenous disturbances, and sensory noise to constitute a 21-degree of freedom model. A mixed [Formula: see text]-synthesis controller, considering the real parametric uncertainty, represents the CNS in the control paradigm. We consider the robotic finger's flexion movement when perturbed from the initial equilibrium. The controller provides feedback force at the joints to regulate the robotic finger movement. The index finger follows a reference trajectory of the joint angular position profile and stabilizes at a flexion angle of 1 rad/s at a time of 1 s. The main control objective is to keep the angular displacement of the finger joint constant when a disturbance force acts. We simulate the modeling scheme in MATLAB/ Simulink. The results demonstrate that our controller scheme is robust against the worst-case disturbance and achieves the desired performance value. Developing a biologically inspired neurophysiological controller with robust performance has many applications, including assistive rehabilitation devices, hand movement disorder diagnosis, and robotic manipulators.

A hierarchical sensorimotor control framework for human-in-the-loop robotic hands.

Human manual dexterity relies critically on touch. Robotic and prosthetic hands are much less dexterous and make little use of the many tactile sensors available. We propose a framework modeled on the hierarchical sensorimotor controllers of the nervous system to link sensing to action in human-in-the-loop, haptically enabled, artificial hands.

Associations among serum VEGF and CGRP levels with the peripheral vascular blood flow of the skin of the hands in women with Fibromyalgia.

BACKGROUND: Fibromyalgia (FM) is a long-term condition of unknown physiopathology, whose hallmark symptoms are diffuse musculoskeletal chronic pain and fatigue. OBJECTIVES: We aimed to analyze the associations among serum vascular endothelial growth factor (VEGF) and calcitonin gene-related peptide (CGRP) levels with the peripheral temperature of the skin of both hands and the core body temperature in patients with FM and healthy controls. METHODS: We conducted a case-control observational study with fifty-three women diagnosed with FM and twenty-four healthy women. VEGF and CGRP levels were spectrophotometrically analyzed in serum by enzyme-linked immunosorbent assay. We used an infrared thermography camera to assess the peripheral temperature of the skin of the dorsal thumb, index, middle, ring, and pinkie fingertips and dorsal centre as well as the palm thumb, index, middle, ring, and pinkie fingertips, palm centre and thenar and hypothenar eminences of both hands and an infrared thermographic scanner to record the tympanic membrane and axillary temperature. RESULTS: Linear regression analysis adjusting for age, menopause status, and body mass index showed that serum VEGF levels were positively associated with the maximum (ß = 65.942, 95% CI [4.100,127.784], p = 0.037), minimum (ß = 59.216, 95% CI [1.455,116.976], p = 0.045), and mean (ß = 66.923, 95% CI [3.142,130.705], p = 0.040) temperature of the thenar eminence of the non-dominant hand, as well as with the maximum temperature of the hypothenar eminence of the non-dominant hand (ß = 63.607, 95% CI [3.468,123.747], p = 0.039) in women diagnosed with FM. CONCLUSIONS: Mild associations were observed between serum VEGF levels and the peripheral temperature of the skin in hand areas in patients with FM; therefore, it is not possible to establish a clear relationship between this vasoactive molecule and vasodilation of the hands in these patients.

Ergonomic Design and Assessment of an Improved Handle for a Laparoscopic Dissector Based on 3D Anthropometry.

Laparoscopic surgery (LS) has been shown to provide great benefits to patients compared with open surgery. However, surgeons experience discomfort, low-efficiency, and even musculoskeletal disorders (MSDs) because of the poor ergonomic design of laparoscopic instruments. A methodology for the ergonomic design of laparoscopic dissector handles considering three-dimensional (3D) hand anthropometry and dynamic hand positions was addressed in this research. Two types of hand positions for grasping and stretching were scanned from 21 volunteers using a high-resolution 3D scanner. The 3D anthropometric data were extracted from these 3D hand pose models and used to design an improved handle (IH) that provides additional support for the thumb, a better fit to the purlicue, and a more flexible grasp for the index finger. Thirty subjects were invited to evaluate the IH in terms of muscular effort, goniometric study of motion, and efficiency and effectiveness during four trials of a laparoscopic training task. Questionnaires provided subjective parameters for ergonomic assessment. Positive results included less muscle load in the trapezius as well as significant but small angular differences in the upper limb. No significant reduction in the trial time and no increased percentage of the achievement were observed between the IH and the commercial handle (CH). Improved intuitiveness, comfort, precision, stability, and overall satisfaction were reported. IH provides significant ergonomic advantages in laparoscopic training tasks, demonstrating that the proposed methodology based on 3D anthropometry is a powerful tool for the handle design of laparoscopic dissectors and other surgical instruments.

Upper limb prostheses: bridging the sensory gap.

Replacing human hand function with prostheses goes far beyond only recreating muscle movement with feedforward motor control. Natural sensory feedback is pivotal for fine dexterous control and finding both engineering and surgical solutions to replace this complex biological function is imperative to achieve prosthetic hand function that matches the human hand. This review outlines the nature of the problems underlying sensory restitution, the engineering methods that attempt to address this deficit and the surgical techniques that have been developed to integrate advanced neural interfaces with biological systems. Currently, there is no single solution to restore sensory feedback. Rather, encouraging animal models and early human studies have demonstrated that some elements of sensation can be restored to improve prosthetic control. However, these techniques are limited to highly specialized institutions and much further work is required to reproduce the results achieved, with the goal of increasing availability of advanced closed loop prostheses that allow sensory feedback to inform more precise feedforward control movements and increase functionality.

Alteration of Ligamento-Muscular Reflex Patterns After Cutaneous and Periarticular Desensitization of the Basal Thumb Joint: An Electromyographic Study.

PURPOSE: Stimulation of the dorsoradial ligament (DRL) of the first carpometacarpal joint (CMC-1) has shown a ligamento-muscular reflex pathway between the DRL and CMC-1 stabilizing muscles in healthy volunteers. However, it remains unclear how this ligamento-muscular reflex pattern is altered after anesthetizing sensory skin receptors and administering a further periarticular block around the CMC-1 joint, which may influence the dynamic aspects of joint stability. METHODS: Ligamento-muscular reflexes were obtained from the extensor pollicis longus, abductor pollicis longus, abductor pollicis brevis, and the first dorsal interosseous muscles in 10 healthy participants after establishing superficial anesthesia of the skin around the CMC-1. The DRL was stimulated with a fine wire electrode while EMG activities were recorded during isometric tip, key, and palmar pinch. The measurements were repeated after an additional periarticular CMC-1 block using 5 ml of 1% lidocaine. Average EMG values were analyzed to compare the prestimulus and poststimulus activity. RESULTS: Statistically significant changes in poststimulus EMG activity were observed in all 4 muscles and all 3 tested thumb positions. A markedly reduced activity in all 4 muscles was observed in the palmar position, followed by the tip and key pinch positions. Almost no reactions were observed in the first 20 ms poststimulus for all muscles in all positions. CONCLUSIONS: Superficial skin anesthesia and an additional periarticular CMC-1 block anesthesia resulted in a reduced ligamento-muscular reflex pattern in all 4 muscles. CLINICAL RELEVANCE: Ligamento-muscular reflexes play an important role in dynamic CMC-1 joint stability. The elimination of early reactions, those considered joint-protective reflexes, is a potential risk factor for developing osteoarthritis or injury because it results in an inability to adequately protect and stabilize the joint in sudden movements.

How virtual and mechanical coupling impact bimanual tracking.

Bilateral training systems look to promote the paretic hand's use in individuals with hemiplegia. Although this is normally achieved using mechanical coupling (i.e., a physical connection between the hands), a virtual reality system relying on virtual coupling (i.e., through a shared virtual object) would be simpler to use and prevent slacking. However, it is not clear whether different coupling modes differently impact task performance and effort distribution between the hands. We explored how 18 healthy right-handed participants changed their motor behaviors in response to the uninstructed addition of mechanical coupling, and virtual coupling using a shared cursor mapped to the average hands' position. In a second experiment, we then studied the impact of connection stiffness on performance, perception, and effort imbalance. The results indicated that both coupling types can induce the hands to actively contribute to the task. However, the task asymmetry introduced by using a cursor mapped to either the left or right hand only modulated the hands' contribution when not mechanically coupled. The tracking performance was similar for all coupling types, independent of the connection stiffness, although the mechanical coupling was preferred and induced the hands to move with greater correlation. These findings suggest that virtual coupling can induce the hands to actively contribute to a task in healthy participants without hindering their performance. Further investigation on the coupling types' impact on the performance and hands' effort distribution in patients with hemiplegia could allow for the design of simpler training systems that promote the affected hand's use.NEW & NOTEWORTHY We showed that the uninstructed addition of a virtual and/or a mechanical coupling can induce both hands to actively contribute in a continuous redundant bimanual tracking task without impacting performance. In addition, we showed that the task asymmetry can only alter the effort distribution when the hands are not connected, independent of the connection stiffness. Our findings suggest that virtual coupling could be used in the development of simpler VR-based training devices.

Assessing differential representation of hand movements in multiple domains using stereo-electroencephalographic recordings.

Invasive brain-computer interfaces (BCI) have made great progress in the reconstruction of fine hand movement parameters for paralyzed patients, where superficial measurement modalities including electrocorticography (ECoG) and micro-array recordings are mostly used. However, these recording techniques typically focus on the signals from the sensorimotor cortex, leaving subcortical regions and other cortical regions related to the movements largely unexplored. As an intracranial recording technique for the presurgical assessments of brain surgery, stereo-encephalography (SEEG) inserts depth electrodes containing multiple contacts into the brain and thus provides the unique opportunity for investigating movement-related neural representation throughout the brain. Although SEEG samples neural signals with high spatial-temporal resolutions, its potential of being used to build BCIs has just been realized recently, and the decoding of SEEG activity related to hand movements has not been comprehensively investigated yet. Here, we systematically evaluated the factors influencing the performance of movement decoding using SEEG signals recorded from 32 human subjects performing a visually-cued hand movement task. Our results suggest that multiple regions in both lateral and depth directions present significant neural selectivity to the task, whereas the sensorimotor area, including both precentral and postcentral cortex, carries the richest discriminative neural information for the decoding. The posterior parietal and prefrontal cortex contribute gradually less, but still rich sources for extracting movement parameters. The insula, temporal and occipital cortex also contains useful task-related information for decoding. Under the cortex layer, white matter presents decodable neural patterns but yields a lower accuracy (42.0 ± 0.8%) than the cortex on average (44.2 ± 0.8%, p<0.01). Notably, collectively using neural signals from multiple task-related areas can significantly enhance the movement decoding performance by 6.9% (p<0.01) on average compared to using a single region. Among the different spectral components of SEEG activity, the high gamma and delta bands offer the most informative features for hand movements reconstruction. Additionally, the phase-amplitude coupling strength between these two frequency ranges correlates positively with the performance of movement decoding. In the temporal domain, maximum decoding accuracy is first reached around 2 s after the onset of movement commands. In sum, this study provides valuable insights for the future motor BCIs design employing both SEEG recordings and other recording modalities.

Increasing motor cortex activation during grasping via novel robotic mirror hand therapy: a pilot fNIRS study.

BACKGROUND: Mirror therapy (MT) has been used for functional recovery of the affected hand by providing the mirrored image of the unaffected hand movement, which induces neural activation of the cortical hemisphere contralateral to the affected hand. Recently, many wearable robots assisting the movement of the hand have been developed, and several studies have proposed robotic mirror therapy (RMT) that uses a robot to provide mirrored movements of the unaffected hand to the affected hand with the robot controlled by measuring electromyography or posture of the unaffected hand. In some cases of RMT a mirror is placed to allow the person to observe only the unaffected hand but in others users simply observe the robotically assisted hand performing the mirrored movements, as was the case in this study. There have been limited evaluations of the cortical activity during RMT compared to MT and robotic therapy (RT) providing passive movements despite the difference in the modality of sensory feedback and the involvement of motor intention, respectively. METHODS: This paper analyzes bilateral motor cortex activation in nine healthy subjects and five chronic stroke survivors during a pinching task performed in MT, RT, and RMT conditions using functional near infrared spectroscopy (fNIRS). In the MT condition, the person moved the unaffected hand and observed it in a mirror while the affected hand remained still. In RT condition passive movements were provided to the affected hand with a cable-driven soft robotic glove, while, in RMT condition, the posture of the unaffected hand was measured by a sensing glove and the soft robotic glove mirrored its movement on the affected hand. RESULTS: For both groups, the RMT condition showed the greatest mean cortical activation on the motor cortex contralateral to the affected (non-dominant for the healthy group) hand compared to other conditions. Individual results indicate that RMT induces similar or greater neural activation on the motor cortex compared to MT and RT conditions. The interhemispheric activations of both groups were balanced in RMT condition. In MT condition, significantly greater activation was shown on the hemisphere ipsilateral to the affected (dominant for the healthy group) hand for both subject groups, while the contralateral side showed significantly greater activation for the healthy group in RT condition. CONCLUSION: The experimental results indicate that combining visual feedback, somatosensory feedback, and motor intention are important for greater stimulation on the contralateral motor cortex of the affected hand. RMT that includes these factors is hypothesized to achieve a more effective functional rehabilitation due to greater and more balanced cortical activation.

Motor impairment evoked by direct electrical stimulation of human parietal cortex during object manipulation.

In primates, the parietal cortex plays a crucial role in hand-object manipulation. However, its involvement in object manipulation and related hand-muscle control has never been investigated in humans with a direct and focal electrophysiological approach. To this aim, during awake surgery for brain tumors, we studied the impact of direct electrical stimulation (DES) of parietal lobe on hand-muscles during a hand-manipulation task (HMt). Results showed that DES applied to fingers-representation of postcentral gyrus (PCG) and anterior intraparietal cortex (aIPC) impaired HMt execution. Different types of EMG-interference patterns were observed ranging from a partial (task-clumsy) or complete (task-arrest) impairment of muscles activity. Within PCG both patterns coexisted along a medio (arrest)-lateral (clumsy) distribution, while aIPC hosted preferentially the task-arrest. The interference patterns were mainly associated to muscles suppression, more pronounced in aIPC with respect to PCG. Moreover, within PCG were observed patterns with different level of muscle recruitment, not reported in the aIPC. Overall, EMG-interference patterns and their probabilistic distribution suggested the presence of different functional parietal sectors, possibly playing different roles in hand-muscle control during manipulation. We hypothesized that task-arrest, compared to clumsy patterns, might suggest the existence of parietal sectors more closely implicated in shaping the motor output.