Grasping Force Research Articles

Object Texture Recognition Based on Grasping Force Data Using Feedforward Neural Network

Object Texture Recognition Based on Grasping Force Data Using Feedforward Neural Network

Vertical Climbing Experiment of Four-Legged Rock-Climbing Robotwith Grasping Force Compensation in Rugged Wall

Vertical Climbing Experiment of Four-Legged Rock-Climbing Robotwith Grasping Force Compensation in Rugged Wall

Static Model-Based Grasping Force Control of Parallel Grasping Robots With Partial Cartesian Force Measurement

This article presents a static model-based approach for grasping force control of a class of low-inertia kinematically redundant parallel and hybrid parallel robots with remotely operated gripper. Although these robots are originally studied with kinematic redundancy, they can also be considered to be redundantly actuated or nonredundant depending on the interactions with the environment. Three different static models are then developed according to the types of redundancy and velocity Jacobians, which are demonstrated through a planar and a spatial parallel robots. The possibility of implementing these models for grasping force control is analyzed. Moreover, we show that the Cartesian force and moment included in the closed force control loop do not need to be measured directly by multidegree-of-freedom force/torque sensors, but can be calculated based on the grasping force that is acting on each jaw of the gripper, which is easily measured using a load cell. A combined position and grasping force control scheme is proposed and experiments are conducted to verify the effectiveness of the nonredundant static model. The proposed approach is readily applied to other kinematically redundant parallel grasping robots.

High intensity aerobic exercise improves bimanual coordination of grasping forces in Parkinson's disease

IntroductionParkinson's disease (PD) disrupts the control and coordination of grasping forces, likely due to a disruption in basal ganglia circuitry and diminished activity within the supplementary motor area (SMA). High intensity aerobic exercise has been shown to enhance connectivity between basal ganglia nuclei and cortical areas, including the SMA. The aim of this project was to determine the effects of high intensity lower extremity exercise on motor control patterns underlying a manual dexterity task. MethodsPD participants completed eight weeks of high intensity aerobic exercise under forced or voluntary exercise (FE or VE) modalities. Grasping forces for each limb were quantified during a functional bimanual dexterity task. Data were collected while OFF antiparkinsonian medication at baseline, end of treatment (EOT), and eight weeks after exercise cessation (EOT+8). ResultsEight weeks of high intensity exercise improved MDS-UPDRS Motor III clinical ratings by more than 4 points (~15%) for the FE and VE groups. Time to complete the task decreased nearly 30% across both groups as well. The control and coordination of grasping forces, simultaneity of force initiation, and rate of grip and load force exhibited significant improvements following exercise. In general, improvements in biomechanical outcomes were sustained following exercise cessation. ConclusionHigh intensity aerobic exercise, achieved via a forced or voluntary mode, improved PD symptoms and bimanual dexterity. Sustained improvement of upper extremity motor control following exercise cessation indicates high intensity exercise enhances CNS functioning and suggests exercise may be a candidate for altering PD progression.

Optimal strategy of sEMG feature and measurement position for grasp force estimation.

Grasp force estimation based on surface electromyography (sEMG) is essential for the dexterous control of a prosthetic hand. Nowadays, although increasing the number of sEMG measurement positions and extracting more features are common methods to increase the accuracy of grasp force estimation, it will increase the computational burden. In this paper, an approach based on analysis of variance (ANOVA) and generalized regression neural network (GRNN) for optimal measurement positions and features is proposed, with the purpose of using fewer measurement positions or features to achieve higher estimation accuracy. Firstly, we captured six channels of sEMG from subjects’ forearm and grasp force synchronously. Then, four kinds of features in time domain are extracted from each channel of sEMG. By combining different measurement position sets (MPSs) and feature set (FSs), we construct 945 data sets. These data sets are fed to GRNN to realize grasp force estimation. Normalized root mean square error (NRMS), normalized mean of absolute error (NMAE), and correlation coefficient (CC) between estimated grasp force and actual force are introduced to evaluate the performance of grasp force estimation. Finally, ANOVA and Tukey HSD testing are introduced to analyze grasp force estimation results so as to obtain the optimal measurement positions and features. We obtain the optimal MPSs for grasp force estimation when different FSs are employed, and the optimal FSs when different MPSs are utilized.

Control Modification of Grasp Force Covaries Agency and Performance on Rigid and Compliant Surfaces

This study investigated how modifications in the display of a computer trace under user control of grasp forces can co-modulate agency (perception of control) and performance of grasp on rigid and compliant surfaces. We observed positive correlation (p < 0.01) between implicit agency, measured from time-interval estimation for intentional binding, and grasp performance, measured by force-tracking error, across varying control modes for each surface type. The implications of this work are design directives for cognition-centered device interfaces for rehabilitation of grasp after neurotraumas such as spinal cord and brain injuries while considering if grasp interaction is rigid or compliant. These device interfaces should increase user integration to virtual reality training and powered assistive devices such as exoskeletons and prostheses. The modifications in control modes for this study included changes in force magnitude, addition of mild noise, and a measure of automation. Significant differences (p < 0.001) were observed for each surface type across control modes with metrics for implicit agency, performance, and grasp control efficiency. Explicit agency, measured from user survey responses, did not exhibit significant variations in this study, suggesting implicit measures of agency are needed for identifying co-modulation with grasp performance. Grasp on the compliant surface resulted in greater dependence of performance on agency and increases in agency and performance with the addition of mild noise. Noise in conjunction with perceived freedom at a flexible surface may have amplified visual feedback responses. Introducing automation in control decreased agency and performance for both surfaces, suggesting the value in continuous user control of grasp. In conclusion, agency and performance of grasp can be co-modulated across varying modes of control, especially for compliant grasp actions. Future studies should consider reliable measures of implicit agency, including physiological recordings, to automatically adapt rehabilitation interfaces for better cognitive engagement and to accelerate functional outcomes.

LAPKaans: Tool-Motion Tracking and Gripping Force-Sensing Modular Smart Laparoscopic Training System.

Laparoscopic surgery demands highly skilled surgeons. Traditionally, a surgeon’s knowledge is acquired by operating under a mentor-trainee method. In recent years, laparoscopic simulators have gained ground as tools in skill acquisition. Despite the wide range of laparoscopic simulators available, few provide objective feedback to the trainee. Those systems with quantitative feedback tend to be high-end solutions with limited availability due to cost. A modular smart trainer was developed, combining tool-tracking and force-using employing commercially available sensors. Additionally, a force training system based on polydimethylsiloxane (PDMS) phantoms for sample stiffness differentiation is presented. This prototype was tested with 39 subjects, between novices (13), intermediates (13), and experts (13), evaluating execution differences among groups in training exercises. The estimated cost is USD $200 (components only), not including laparoscopic instruments. The motion system was tested for noise reduction and position validation with a mean error of 0.94 mm. Grasping force approximation showed a correlation of 0.9975. Furthermore, differences in phantoms stiffness effectively reflected user manipulation. Subject groups showed significant differences in execution time, accumulated distance, and mean and maximum applied grasping force. Accurate information was obtained regarding motion and force. The developed force-sensing tool can easily be transferred to a clinical setting. Further work will consist on a validation of the simulator on a wider range of tasks and a larger sample of volunteers.

Ability to adjust occlusal force in implant-supported overdenture wearers.

The purpose of this study was to evaluate the occlusal force adjusting ability of implant-supported overdenture (IOD) wearers, as compared with natural teeth and complete dentures. Subjects were those with natural dentition (ND group; 19 subjects), those with implant-supported overdentures (IOD group; 7 subjects), and those with complete dentures (CD group; 14 subjects). Subjects were asked to hold test foods (peanuts and biscuits mounted on a custom-made apparatus with a force transducer) between their anterior incisors (hold phase) and split test foods (split phase). The mean value of the occlusal force during the hold phase (hold force), the peak force rate during the split phase (peak force rate), the time required to split test foods (duration), and the maximum occlusal force in the split phase (split force) were selected as outcomes. Data were analyzed with Wilcoxon's signed rank test, the Kruskal-Wallis test, and multiple regression analysis (Statistical significance levels: 5%). For peanuts, the peak force rate for the ND group was significantly higher than the IOD and CD groups. The duration of the CD group was significantly longer than the ND and IOD groups. Multiple regression analysis indicated that even with adjustment for age and sex, there were significant differences in the peak force rate between the ND and the IOD, CD groups, and in the duration between the ND and CD group. Subjects with IODs showed superior ability to adjust occlusal force, as compared with complete dentures, although it didn't match the natural dentition.

Soft Humanoid Hands with Large Grasping Force Enabled by Flexible Hybrid Pneumatic Actuators

Soft grippers and actuators have attracted increasing attention due to safer and more adaptable human-machine and environment-machine interactions than their rigid counterparts. In this study we present a novel soft humanoid hand that is capable of robustly grasping a variety of objects with different weights, sizes, shapes, textures, and stiffnesses. The soft hand fingers are made of flexible hybrid pneumatic actuators (FHPAs) designed based on a modular approach. A theoretical model is proposed to evaluate the bending deformation, grasping force, and loading capacity of the FHPAs, and the effects of various design parameters on the performance of the FHPA are investigated for optimizing the soft hands. This new FHPA achieves a balance of required flexibility and necessary stiffness, and the resulting soft humanoid hand has the merits of fast response, large grasping force, low cost, light weight, and ease of fabrication and repair, which shows promise for a variety of applications such as fruit picking, product packaging, and manipulation of fragile objects.

FV10 Neural Correlates of Age-related Changes in Grasping Force Regulation: A Combined EEG-fNIRS Study

FV10 Neural Correlates of Age-related Changes in Grasping Force Regulation: A Combined EEG-fNIRS Study

Grasping Force Control of Multi-Fingered Robotic Hands through Tactile Sensing for Object Stabilization.

Grasping force control is important for multi-fingered robotic hands to stabilize the grasped object. Humans are able to adjust their grasping force and react quickly to instabilities through tactile sensing. However, grasping force control through tactile sensing with robotic hands is still relatively unexplored. In this paper, we make use of tactile sensing for multi-fingered robot hands to adjust the grasping force to stabilize unknown objects without prior knowledge of their shape or physical properties. In particular, an online detection module based on Deep Neural Network (DNN) is designed to detect contact events and object material simultaneously from tactile data. In addition, a force estimation method based on Gaussian Mixture Model (GMM) is proposed to compute the contact information (i.e., contact force and contact location) from tactile data. According to the results of tactile sensing, an object stabilization controller is then employed for a robotic hand to adjust the contact configuration for object stabilization. The spatio-temporal property of tactile data is exploited during tactile sensing. Finally, the effectiveness of the proposed framework is evaluated in a real-world experiment with a five-fingered Shadow Dexterous Hand equipped with BioTac sensors.

Grasp force estimation from the transient EMG using high-density surface recordings

Objective. Understanding the neurophysiological signals underlying voluntary motor control and decoding them for prosthesis control are among the major challenges in applied neuroscience and bioengineering. Usually, information from the electrical activity of residual forearm muscles (i.e. the electromyogram, EMG) is used to control different functions of a prosthesis. Noteworthy, forearm EMG patterns at the onset of a contraction (transient phase) have shown to contain predictive information about upcoming grasps. However, decoding this information for the estimation of grasp force (GF) was so far overlooked. Approach. High density-EMG signals (192 channels) were recorded from twelve participants performing a pick-and-lift task. The final GF was estimated offline using linear regressors, with four subsets of channels and ten features obtained using three channels-features selection methods. Two different evaluation metrics (absolute error and R2), complemented with statistical analysis, were used to select the optimal configuration of the parameters. Different windows of data starting at the GF onset were compared to determine the time at which the GF can be ascertained from the EMG signals. Main results. The prediction accuracy improved by increasing the window length from the moment of the onset and kept improving until the steady state at which a plateau of performances was reached. With our methodology, estimations of the GF through 16 EMG channels reached an absolute error of 2.52% the maximum voluntary force using only transient information and 1.99% with the first 500 ms of data following the onset. Significance. The final GF estimation from transient EMG was comparable to the one obtained using steady state data, confirming our hypothesis that the transient phase contains information about the final GF. This result paves the way to fast online myoelectric controllers capable of decoding grasp strength from the very early portion of the EMG signal.

Neural Representation of Observed, Imagined, and Attempted Grasping Force in Motor Cortex of Individuals with Chronic Tetraplegia

Hybrid kinetic and kinematic intracortical brain-computer interfaces (iBCIs) have the potential to restore functional grasping and object interaction capabilities in individuals with tetraplegia. This requires an understanding of how kinetic information is represented in neural activity, and how this representation is affected by non-motor parameters such as volitional state (VoS), namely, whether one observes, imagines, or attempts an action. To this end, this work investigates how motor cortical neural activity changes when three human participants with tetraplegia observe, imagine, and attempt to produce three discrete hand grasping forces with the dominant hand. We show that force representation follows the same VoS-related trends as previously shown for directional arm movements; namely, that attempted force production recruits more neural activity compared to observed or imagined force production. Additionally, VoS-modulated neural activity to a greater extent than grasping force. Neural representation of forces was lower than expected, possibly due to compromised somatosensory pathways in individuals with tetraplegia, which have been shown to influence motor cortical activity. Nevertheless, attempted forces (but not always observed or imagined forces) could be decoded significantly above chance, thereby potentially providing relevant information towards the development of a hybrid kinetic and kinematic iBCI.

Design optimisation for a novel underactuated robotic finger by genetic algorithms

ABSTRACT This paper presents a novel design of the robotic finger as a development of the Otto bock hand design, especially in the index finger of the hand. Namely, make it composed from three phalanges instead of a single one. In addition, in order to improve the grasping dexterity, a four-bar mechanism for each phalanx was used. This results in finding the optimum dimensions of the linkages to drive the robotic finger. To achieve such aim, a Genetic Algorithm (G.A) was employed. Two criteria were chosen for finding the optimal solution; Grasping force criterion and Squeezing force criterion. The optimisation criteria are based on the force analysis equations that derived to calculate the contact forces during power grasping action. The prosthetic finger was built from PLA (Polylactic Acid) plastic using a 3D printer and tested experimentally through a special measuring mechanism provided with a sensory system. The experimental and the theoretical results of the normal force of the finger were in good agreement.

Grasping force prediction based on sEMG signals

In order to realize the force control, when the prosthetic hand grasps the object, the forearm electromyography signal is collected by the multi-channel surface electromyography (sEMG) acquisition system. The grasping force information of the human hand is recorded by the six-dimensional force sensor. The root mean square (RMS) of the electromyography signal steady state is selected, which is an effective feature. The gene expression programming algorithm (GEP) and BP neural network are used to construct the prediction model and predict the grasping force. The force prediction accuracy of GEP algorithm and BP neural network algorithm are discussed under different grasping power levels and different grasping modes. The performance of the two algorithm models are evaluated by two measures of root mean square error (RMSE) and correlation coefficient (CC). The results show that the RMS eigenvalue extracted from the sEMG signal can better characterize the grasping force. The prediction model with GEP algorithm has smaller relative error and higher prediction effect.

Basic Study on Motor Learning while Adjusting Grasping Force with iWakka

Basic Study on Motor Learning while Adjusting Grasping Force with iWakka

Effects of Chronic and Experimental Acute Masseter Pain on Precision Biting Behavior in Humans.

Chronic pain in the orofacial region is common worldwide. Pain seems to affect the jaw motor control. Hence, temporomandibular disorders (TMD) are often accompanied by pain upon chewing, restricted mouth opening and impaired maximal bite forces. However, little is known on the effects of pain, in particular the effects of chronic jaw muscle pain on precision biting. The aim of the study was to investigate the effect of chronic and acute jaw muscle pain on oral motor control during precision biting in humans. Eighteen patients with chronic masseter muscle pain and 18 healthy participants completed the experiment. All participants were examined according to the Diagnostic Criteria for TMD. Experimental acute pain was induced by bilateral, simultaneous sterile hypertonic saline infusions into the healthy masseter muscles. A standardized hold and split biting task was used to assess the precision biting. The data was analyzed with non-parametric statistical tests. The results showed no significant differences in the hold forces, split forces, durations of split or peak split rates within or between the pain and pain-free conditions. The mean split rate increased significantly compared to baseline values both in the chronic patients and the pain-free condition. However, this increase was not evident in the experimental acute pain condition. Further, there were no significant differences in the mean split rates between the conditions. The data suggest that jaw muscle pain does not seem to alter precision biting in humans, however, the possibility that a nociceptive modulation of spindle afferent activity might have occurred but compensated for cannot be ruled out.

The development of a new variable stiffness soft gripper

A new variable stiffness four-finger soft gripper with a combination of rigid and soft structures is designed. The combination of rigid and soft structures is one of the methods to improve the performance of soft grippers. Grasping motion is achieved by the rigid structure of the screw and the connecting rod. Soft gripper uses human finger-like structure made of silicone, and the air pressure and the rigidity of the soft fingers can be adjusted by the air pump. The soft gripper overcomes the inability of a rigid gripper to easily and safely grasp soft and brittle objects and the inability of a completely soft gripper to exert sufficiently high forces to achieve effective grasping. Grasping force can be improved by increasing the stiffness of the finger and the driving stroke of screw. The variable grasping force allows the soft gripper to grasp different shape objects, specially soft and brittle objects.

Increase in Grasp Force Reflects a Desire to Improve Movement Precision.

Grasping is an action engraved in the human genome, enabling newborn infants to hang from a monkey-bar immediately after birth. The grasp force provides rich information about the brain’s control of arm movements. In this study, we tested the hypothesis that the grasp force increases to improve the hand’s movement precision during reaching. In two reaching experiments, subjects increased grasp force to suppress movement imprecision that arose from both self-generated motor noise and from an unpredictable environment. Furthermore, the grasp force did not increase constantly, but increased specifically along the movement where the hand’s deviation was greatest. The increased grasp was premeditated and was not a reaction to environmental forces, suggesting that the central nervous system has a predictive, state-dependent model of movement precision during reaching. The grasp force provides a high temporal resolution and calibration-less estimate of movement precision adaptation.

Electrotactile Feedback with Spatial and Mixed Coding for Object Identification and Closed-loop Control of Grasping Force in Myoelectric Prostheses.

Providing high-quality somatosensory feedback from myoelectric prostheses to an upper-limb amputee user is a long-standing challenge. Various approaches have been investigated for tactile feedback, ranging from direct neural stimulation to noninvasive sensory substitution methods. However, only a few of studies evaluated the closed-loop performance, and real-time movement information of active prostheses still could not be transferred in the form of proprioceptive feedback so far. In current study, an integrated closed-loop prosthesis system consisted of two types of sensors, programmable electrical stimulator and multichannel array electrodes was presented. The grasping angle and corresponding grasping force of the single-freedom myoelectric prosthesis were simultaneously coded with spatial and mixed (spatial and intensity of sensation) coding scheme and tested in 15 able-bodied subjects. The experimental results demonstrated that the subjects were able to discriminate 4 types of object sizes, 3 kinds of different softness and 4 levels of grasping forces in relatively high correct identification rates (CIRs) (size: 87.5%, Softness: 94%, grasping force: 73.8%). The study outcomes and specific conclusions provide valuable guidance for the design of closed-loop myoelectric prostheses equipped with electrotactile feedback.