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This study aimed to predict and fit the nonlinear dynamic grip force of the human upper limb using surface electromyographic (sEMG) signals. The research employed a time-series-based neural network, NARX, to establish a mapping relationship between the electromyographic signals of the forearm muscle groups and dynamic grip force. Three-channel electromyographic signal acquisition equipment and a grip force sensor were used to record muscle signals and grip force data of the subjects under specific dynamic force conditions. After preprocessing the data, including outlier removal, wavelet denoising, and baseline drift correction, the NARX model was used for fitting analysis. The model compares two different training strategies: regularized stochastic gradient descent (BRSGD) and conjugate gradient (CG). The results show that the CG greatly shortened the training time, and performance did not decline. NARX demonstrated good accuracy and stability in dynamic grip force prediction, with the model with 10 layers and 20 time delays performing the best. The results demonstrate that the proposed method has potential practical significance for force control applications in smart prosthetics and virtual reality.

Planning motor‐actions involves the neuronal representation of key parameters such as force and timing prior to execution. Functional magnetic resonance imaging (fMRI) studies have shown that activity in premotor and parietal areas covaries with these parameters during motor‐preparation. While previous research has demonstrated that parametric codes reflect graded grip‐force intensities before and after their transformation into motor‐codes, it remains unclear whether these representations are encoded in effector‐specific brain‐regions. To address this, we conducted an fMRI‐study using a delayed grip‐force task in which participants prepared one of four force‐intensities with either their right or left cued‐hand, with the hand to‐be‐used being switched in 50% of the trials midway through the delay. Using time‐resolved multivoxel pattern analysis (MVPA) with a searchlight approach, we identified brain‐regions encoding anticipated grip‐force intensities of the cued‐hand across the two 6‐s delay‐periods. In addition, cross‐decoding analyses tested whether force‐intensities were represented in an effector‐specific or effector‐independent format. We found above‐chance decoding in two lateralized networks: the contralateral intraparietal sulcus (r−/l‐IPS), as well as the lateral occipitotemporal cortex (r−/l‐LOTC) during the first, and the contralateral primary motor cortices (r−/l‐M1) during the second delay. These results indicate effector‐specific coding of anticipated grip‐force intensities, which is revealed by systematic lateralization of decoding‐accuracy depending on the hand to‐be‐used. Cross‐decoding corroborated effector‐specific representation in these regions. Together, our results show that contralateral IPS and LOTCs encode effector‐specific parametric information prior to M1s, likely reflecting a transformation process in which the intended grip‐force intensity is selected, maintained, and then converted into detailed movement‐plans. In an fMRI study, we used a delayed grip‐force task and time‐resolved MVPA to test where anticipated grip‐force intensities are parametrically encoded, whether these are represented in effector‐specific brain regions, and how these representations are transformed across two 6‐s delay periods. During the first delay, contralateral intraparietal sulci (l‐IPS, r‐IPS) and extrastriate body areas (l‐EBA, r‐EBA) encoded the intended grip‐force, which was later transformed into motor codes in contralateral primary motor cortices (l‐M1, r‐M1). Systematic above‐chance decoding in contralateral regions of the to‐be‐used hand indicates effector‐specific coding, corroborated by cross‐decoding.

Previous functional magnetic resonance imaging (fMRI) studies have shown that activity in premotor and parietal brain‐regions covaries with the intensity of upcoming grip‐force. However, it remains unclear how information about the intended grip‐force intensity is initially represented and subsequently transformed into a motor code before motor execution. In this fMRI study, we used multivoxel pattern analysis (MVPA) to decode where and when information about grip‐force intensities is parametrically coded in the brain. Human participants performed a delayed grip‐force task in which one of four cued levels of grip‐force intensity had to be maintained in working memory (WM) during a 9‐s delay‐period preceding motor execution. Using time‐resolved MVPA with a searchlight approach and support vector regression, we tested which brain regions exhibit multivariate WM codes of anticipated grip‐force intensities. During the early delay period, we observed above‐chance decoding in the ventromedial prefrontal cortex (vmPFC). During the late delay period, we found a network of action‐specific brain regions, including the bilateral intraparietal sulcus (IPS), left dorsal premotor cortex (l‐PMd), and supplementary motor areas. Additionally, cross‐regression decoding was employed to test for temporal generalization of activation patterns between early and late delay periods with those during cue presentation and motor execution. Cross‐regression decoding indicated temporal generalization to the cue period in the vmPFC and to motor‐execution in the l‐IPS and l‐PMd. Together, these findings suggest that the WM representation of grip‐force intensities undergoes a transformation where the vmPFC encodes information about the intended grip‐force, which is subsequently converted into a motor code in the l‐IPS and l‐PMd before execution. In a functional magnetic resonance imaging study, we employed a delayed grip‐force task and time‐resolved MVPA to decode where and when information about grip‐force intensities is parametrically coded. With a combination of support vector regression and cross‐regression decoding, we tested the translational processes between early and late delay periods and their similarity to action‐selection and motor execution. During the early delay period, we found the ventromedial prefrontal cortex to code the intended grip‐force intensity. This representation is then translated into motor‐like action‐specific codes during the late delay period, involving the intraparietal sulcus, left dorsal premotor cortex, and supplementary motor areas.

STRAS (Single access Transluminal Robotic Assistant for Surgeons) is a new robotic system based on the Anubis® platform of Karl Storz for application to intra-luminal surgical procedures. Pre-clinical testing of STRAS has recently permitted to demonstrate major advantages of the system in comparison with classic procedures. Benchmark methods permitting to establish objective criteria for ‘expertise’ need to be worked out now to effectively train surgeons on this new system in the near future. STRAS consists of three cable-driven sub-systems, one endoscope serving as guide, and two flexible instruments. The flexible instruments have three degrees of freedom and can be teleoperated by a single user via two specially designed master interfaces. In this study, small force sensors sewn into a wearable glove to ergonomically fit the master handles of the robotic system were employed for monitoring the forces applied by an expert and a trainee (complete novice) during all the steps of surgical task execution in a simulator task (4-step-pick-and-drop). Analysis of grip-force profiles is performed sensor by sensor to bring to the fore specific differences in handgrip force profiles in specific sensor locations on anatomically relevant parts of the fingers and hand controlling the master/slave system.

Hand exoskeletons are potential solutions for enhancing upper extremity function after stroke, yet achieving intuitive control remains challenging. We recently showed that isometric grip force tracking is preserved after stroke, providing a possible control source for a hand exoskeleton. In this study, we developed a hand exoskeleton with a soft compliant mechanism and novel force control strategy that leverages isometric grip force control of digits 3–5 to control an index–thumb pinch grip. We first present characterization of the compliant mechanisms output impedance (34.77 N/m), and output force (2.3 ± 0.57 N). We then present results of a study that assessed the intuitiveness of the strategy during a grip–lift–move task in ten unimpaired individuals. From four unimpaired individuals we also gathered user preferences on force sensitivity and operating mode, where in one mode flexion force from digits 3–5 caused index finger closing, while in the other mode it caused index finger opening. The strategy proved intuitive, improving movement frequency on the grip–lift–move task by 30%. Users preferred greater force sensitivity and using flexion force from digits 3–5 to drive index finger extension. The force control strategy incorporated into the exoskeleton shows promise warranting further investigation in neurologically impaired participants.

The actual grip force provided by a hand prosthesis is an important parameter to evaluate its efficiency. To this end, a split cylindrical handlebar embedding a single-axis load cell was designed, 3D printed and assembled. Various measurements were made to evaluate the performances of the “Federica” hand, a simple low-cost hand prosthesis. The handlebar was placed at different angular positions with respect to the hand palm, and the experimental data were processed to estimate the overall grip force. In addition, piezoresistive force sensors were applied on selected phalanxes of the prosthesis, in order to map the distribution of the grasping forces between them. The electrical current supplied to the single servomotor that actuates all the five fingers, was monitored to estimate the force exerted on the main actuator tendon, while tendon displacement was evaluated by a rotary potentiometer fixed to the servomotor shaft. The force transfer ratio of the whole system was about 12.85 %, and the mean dissipated energy for a complete cycle of closing-opening was 106.80 Nmm, resulting lower than that of many commercial prostheses. The mean grip force of the “Federica” hand was 8.80 N, that is enough to support the user in many actions of daily life, also considering the adaptive wrapping capability of the prosthesis. On average, the middle phalanges exerted the greatest grip force (2.65 N) on the handlebar, while the distal phalanges a force of 1.66 N.

Background When exposed to a novel dynamic perturbation, participants adapt by changing their movements’ dynamics. This adaptation is achieved by constructing an internal representation of the perturbation, which allows for applying forces that compensate for the novel external conditions. To form an internal representation, the sensorimotor system gathers and integrates sensory inputs, including kinesthetic and tactile information about the external load. The relative contribution of the kinesthetic and tactile information in force-field adaptation is poorly understood. Methods In this study, we set out to establish the effect of augmented tactile information on adaptation to force-field. Two groups of participants received a velocity-dependent tangential skin deformation from a custom-built skin-stretch device together with a velocity-dependent force-field from a kinesthetic haptic device. One group experienced a skin deformation in the same direction of the force, and the other in the opposite direction. A third group received only the velocity-dependent force-field. Results We found that adding a skin deformation did not affect the kinematics of the movement during adaptation. However, participants who received skin deformation in the opposite direction adapted their manipulation forces faster and to a greater extent than those who received skin deformation in the same direction of the force. In addition, we found that skin deformation in the same direction to the force-field caused an increase in the applied grip-force per amount of load force, both in response and in anticipation of the stretch, compared to the other two groups. Conclusions Augmented tactile information affects the internal representations for the control of manipulation and grip forces, and these internal representations are likely updated via distinct mechanisms. We discuss the implications of these results for assistive and rehabilitation devices.

The ability to finely control hand grip forces can be compromised by neuromuscular or musculoskeletal disorders. Therefore, it is recommended to include the training and assessment of grip force control in rehabilitation therapy. The benefits of robot-mediated therapy have been widely reported in the literature, and its combination with virtual reality and biofeedback can improve rehabilitation outcomes. However, the existing systems for hand rehabilitation do not allow both monitoring/training forces exerted by single fingers and providing biofeedback. This paper describes the development of a system for the assessment and recovery of grip force control. An exoskeleton for hand rehabilitation was instrumented to sense grip forces at the fingertips, and two operation modalities are proposed: (i) an active-assisted training to assist the user in reaching target force values and (ii) virtual reality games, in the form of tracking tasks, to train and assess the user’s grip force control. For the active-assisted modality, the control of the exoskeleton motors allowed generating additional grip force at the fingertips, confirming the feasibility of this modality. The developed virtual reality games were positively accepted by the volunteers and allowed evaluating the performance of healthy and pathological users.

Recovery of motor function following stroke requires interventions to enhance ipsilesional cortical activity. To improve finger motor function following stroke, we developed a movement task with visuomotor feedback and measured changes in motor cortex activity by electroencephalography. Stroke patients performed two types of movement task on separate days using the paretic fingers: a visuomotor tracking task requiring the patient to match a target muscle force pattern with ongoing feedback and a simple finger flexion/extension task without feedback. Movement-related cortical potentials (MRCPs) were recorded before and after the two motor interventions. The amplitudes of MRCPs measured from the ipsilesional hemisphere were significantly enhanced after the visuomotor tracking task but were unchanged by the simple manual movement task. Increased MRCP amplitude preceding movement onset revealed that the control of manual movement using visual feedback acted on the preparatory stage from motor planning to execution. A visuomotor tracking task can enhance motor cortex activity following a brief motor intervention, suggesting efficient induction of use-dependent cortical plasticity in stroke.

New technologies for monitoring grip forces during hand and finger movements in non-standard task contexts have provided unprecedented functional insights into somatosensory cognition. Somatosensory cognition is the basis of our ability to manipulate and transform objects of the physical world and to grasp them with the right amount of force. In previous work, the wireless tracking of grip-force signals recorded from biosensors in the palm of the human hand has permitted us to unravel some of the functional synergies that underlie perceptual and motor learning under conditions of non-standard and essentially unreliable sensory input. This paper builds on this previous work and discusses further, functionally motivated, analyses of individual grip-force data in manual robot control. Grip forces were recorded from various loci in the dominant and non-dominant hands of individuals with wearable wireless sensor technology. Statistical analyses bring to the fore skill-specific temporal variations in thousands of grip forces of a complete novice and a highly proficient expert in manual robot control. A brain-inspired neural network model that uses the output metric of a self-organizing pap with unsupervised winner-take-all learning was run on the sensor output from both hands of each user. The neural network metric expresses the difference between an input representation and its model representation at any given moment in time and reliably captures the differences between novice and expert performance in terms of grip-force variability.Functionally motivated spatiotemporal analysis of individual average grip forces, computed for time windows of constant size in the output of a restricted amount of task-relevant sensors in the dominant (preferred) hand, reveal finger-specific synergies reflecting robotic task skill. The analyses lead the way towards grip-force monitoring in real time. This will permit tracking task skill evolution in trainees, or identify individual proficiency levels in human robot-interaction, which represents unprecedented challenges for perceptual and motor adaptation in environmental contexts of high sensory uncertainty. Cross-disciplinary insights from systems neuroscience and cognitive behavioral science, and the predictive modeling of operator skills using parsimonious Artificial Intelligence (AI), will contribute towards improving the outcome of new types of surgery, in particular the single-port approaches such as NOTES (Natural Orifice Transluminal Endoscopic Surgery) and SILS (Single-Incision Laparoscopic Surgery).