Grasping Force Research Articles

Compliant Grasp Control Method for the Underactuated Prosthetic Hand Based on the Estimation of Grasping Force and Muscle Stiffness with sEMG

Human muscles can generate force and stiffness during contraction. When in contact with objects, human hands can achieve compliant grasping by adjusting the grasping force and the muscle stiffness based on the object’s characteristics. To realize humanoid-compliant grasping, most prosthetic hands obtain the stiffness parameter of the compliant controller according to the environmental stiffness, which may be inconsistent with the amputee’s intention. To address this issue, this paper proposes a compliant grasp control method for an underactuated prosthetic hand that can directly obtain the control signals for compliant grasping from surface electromyography (sEMG) signals. First, an estimation method of the grasping force is established based on the Huxley muscle model. Then, muscle stiffness is estimated based on the muscle contraction principle. Subsequently, a relationship between the muscle stiffness of the human hand and the stiffness parameters of the prosthetic hand controller is established based on fuzzy logic to realize compliant grasp control for the underactuated prosthetic hand. Experimental results indicate that the prosthetic hand can adjust the desired force and stiffness parameters of the impedance controller based on sEMG, achieving a quick and stable grasp as well as a slow and gentle grasp on different objects.

Let Me Give You a Hand: Enhancing Human Grasp Force With a Soft Robotic Assistive Glove

Let Me Give You a Hand: Enhancing Human Grasp Force With a Soft Robotic Assistive Glove

Design and Research of the Grasping Force Feedback Mechanism of Flexible Surgical Robots.

Robot-assisted microsurgery (RAMS) is gradually becoming the preferred method for some delicate surgical procedures. However, the lack of haptic feedback reduces the safety of the surgery. Surgeons are unable to feel the grasping force between surgical instruments and the patient's tissues, which can easily lead to grasping failure or tissue damage. This paper proposes a tendon-driven grasping force feedback mechanism, consisting of a follower hand and a leader hand, to address the lack of grasping force feedback in flexible surgical robots. Considering the friction in the tendon transmission process, a grasping force estimation model is established for the follower hand. The admittance control model is designed for force/position control of the leader hand. Through experimental validation, it has been confirmed that the grasping force sensing range of the follower hand is 0.5-5N, with a sensing accuracy of 0.3N. The leader hand is capable of providing feedback forces in the range of 0-5N, with a static force accuracy of 0.1N. The designed mechanism and control strategy can provide the grasping force feedback function. Future work will focus on improving force feedback performance. This research has no clinical trials.

Graph-Driven Simultaneous and Proportional Estimation of Wrist Angle and Grasp Force via High-Density EMG.

Myoelectric prostheses are generally unable to accurately control the position and force simultaneously, prohibiting natural and intuitive human-machine interaction. This issue is attributed to the limitations of myoelectric interfaces in effectively decoding multi-degree-of-freedom (multi-DoF) kinematic and kinetic information. We thus propose a novel multi-task, spatial-temporal model driven by graphical high-density electromyography (HD-EMG) for simultaneous and proportional control of wrist angle and grasp force. Twelve subjects were recruited to perform three multi-DoF movements, including wrist pronation/supination, wrist flexion/extension, and wrist abduction/adduction while varying grasp force. Experimental results demonstrated that the proposed model outperformed five baseline models, with the normalized root mean square error of 13.2% and 9.7% and the correlation coefficient of 89.6% and 91.9% for wrist angle and grasp force estimation, respectively. In addition, the proposed model still maintained comparable accuracy even with a significant reduction in the number of HD-EMG electrodes. To the best of our knowledge, this is the first study to achieve simultaneous and proportional wrist angle and grasp force control via HD-EMG and has the potential to empower prostheses users to perform a broader range of tasks with greater precision and control, ultimately enhancing their independence and quality of life.

Activities of daily living with grasp types and force measurements during object manipulation

Background: Limited scientific evidence guides hand rehabilitation towards improved hand function, and safe early return to work. Grasp types, the subunits of object manipulation and the forces which may improve functional outcomes, have been identified as missing links that may inform rehabilitation after second to fifth metacarpal fractures, through progression of basic and instrumental activities of daily living (ADL). The aim of the study was to collect ADL forces through grasp types to inform hand rehabilitation for second to fifth metacarpal fractures. Methods: This cross-sectional, quantitative study included 32 conveniently sampled healthy adults aged 20 and 59 years. Thirty-one (31) ADLs, both basic and instrumental, each associated with a predominant grasp type, were tested. The participants donned two testing gloves, with force sensing resistors (FSRs) glued to the fingertips, the ADLs were performed, and forces measured. The researcher imported the force data into an Excel spreadsheet for both descriptive and inferential analyses with STATA. Results: Fourteen males and 18 females, with a mean age of 37 years, participated. Statistically significant differences between genders were found for seven grasp types. Three thumb-adducted power palm grasps, three thumb-abduction precision pad grasps, and one thumb-abduction power palm grasp constituted the testing. Light and heavy ADLs and their associated grasp types were identified. Conclusion: To ensure optimal hand function, early safe return to ADLs should be the goal of second to fifth metacarpal fracture rehabilitation and may be achieved by incorporating active grasp types as exercises with graded resisted grasp types and ADLs. Implications for practice • Occupational therapists can incorporate grasp types in their observation during assessments of individuals who sustained second to fifth metacarpal fractures to determine the subcomponents of functional deficits. • Hand rehabilitation may be augmented by incorporating grasp types in isolation during unresisted active joint range of motion. • Occupational therapists, guided by the analysis of hand functioning, can incorporate ADLs with the predominant grasp types which may improve hand function. • Grasp force data may guide safe and early return to function.

A Novel Incipient Slip Degree Evaluation Method and Its Application in Adaptive Control of Grasping Force

Grasping is the most basic and important function of robots. However, the grasping performance of existing manipulators is far inferior to that of human hands. Human hands are able to evaluate the degree of incipient slip on contact surface based on tactile information during grasping process and thus regulate grasping force. As a result, humans can use the smallest possible grasping force while ensuring successful grasping, which maximizes the performance of their hands and avoids damaging the grasped object as much as possible. The existing incipient slip degree evaluation methods have certain shortcomings. So this paper proposes a novel method which can be applied to complex contact conditions where the incipient slip degree is not unidirectional and torque exists. Also, there are no restrictions on the material parameters and surface topography of the grasped object, and no need to obtain any information about it in advance. We construct a grasping force control strategy for parallel grippers based on this evaluation method, with the goal of enabling the gripper to achieve the similar grasping performance of human hands. The grasping strategy is verified in simulations and actual experiments, and the difference between the controlled force and minimum grasping force is demonstrated. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In unstructured grasping tasks, the information of the grasped object cannot be obtained in advance, which makes it difficult for robotic grasping. Conventional grasping strategies often use the maximum force of grippers, but this can cause damage to fragile objects. In contrast, human hands can flexibly control the size of the grasping force, which will not far exceed the minimum force required to ensure successful grasping. This capability greatly expands the grasping range of human hands. Neuroscience studies have shown that such excellent grasping performance owes to the perception of incipient slip by human’s tactile system. However, existing incipient slip detection methods for manipulators have certain drawbacks. To fill this gap, we propose a novel method that can be applied to contact conditions where tangential forces are reversed and torques exist. Meanwhile the method has no restrictions on the surface morphology and material parameters of the contact object, which has a good range of applications. This paper applies this method to a parallel gripper commonly used in industrial scenarios, and proposes a grasping force control strategy that can automatically adjust the magnitude of the grasping force to achieve a similar grasping performance to human hands -keeping the grasping force within a certain range beyond the minimum value. In the future, we also intend to apply the proposed method to more dexterous manipulators and perform more complex manipulation tasks. Possible application scenarios include: smart prosthesis, virtual reality, remote control, etc. Currently, we assume that the Coulomb’s law of friction is satisfied on the contact surface, and more complex friction models will be introduced in the future to further improve the applicability of this method.

Closed-Loop Force Control by Biorealistic Hand Prosthesis with Visual and Tactile Sensory Feedback.

The ability of a novel biorealistic hand prosthesis for grasp force control reveals improved neural compatibility between the human-prosthetic interaction. The primary purpose here was to validate a virtual training platform for amputee subjects and evaluate the respective roles of visual and tactile information in fundamental force control tasks. We developed a digital twin of tendon-driven prosthetic hand in the MuJoCo environment. Biorealistic controllers emulated a pair of antagonistic muscles controlling the index finger of the virtual hand by surface electromyographic (sEMG) signals from amputees' residual forearm muscles. Grasp force information was transmitted to amputees through evoked tactile sensation (ETS) feedback. Six forearm amputees participated in force tracking and holding tasks under different feedback conditions or using their intact hands. Test results showed that visual feedback played a predominant role than ETS feedback in force tracking and holding tasks. However, in the absence of visual feedback during the force holding task, ETS feedback significantly enhanced motor performance compared to feedforward control alone. Thus, ETS feedback still supplied reliable sensory information to facilitate amputee's ability of stable grasp force control. The effects of tactile and visual feedback on force control were subject-specific when both types of feedback were provided simultaneously. Amputees were able to integrate visual and tactile information to the biorealistic controllers and achieve a good sensorimotor performance in grasp force regulation. The virtual platform may provide a training paradigm for amputees to adapt the biorealistic hand controller and ETS feedback optimally.

Improving the Grasping Force Behavior of a Robotic Gripper: Model, Simulations, and Experiments

Robotic grippers allow industrial robots to interact with the surrounding environment. However, control architectures of the grasping force are still rare in common industrial grippers. In this context, one or more sensors (e.g., force or torque sensors) are necessary. However, the incorporation of such sensors might heavily affect the cost of the gripper, regardless of its type (e.g., pneumatic or electric). An alternative approach could be open-loop force control strategies. Hence, this work proposes an approach for optimizing the open-loop grasping force behavior of a robotic gripper. For this purpose, a specialized robotic gripper was built, as well as its mathematical model. The model was employed to predict the gripper performance during both static and dynamic force characterization, simulating grasping tasks under different experimental conditions. Both simulated and experimental results showed that by managing the mechanical properties of the finger–object contact interface (e.g., stiffness), the steady-state force variability could be greatly reduced, as well as undesired effects such as finger bouncing. Further, the object’s size is not required unlike most of the grasping approaches for industrial rigid grippers, which often involve high finger velocities. These results may pave the way toward conceiving cheaper and more reliable open-loop force control techniques for use in robotic grippers.

“Terrible Letters”: Bad Handwriting and its Implications, 1020–1220

This article focuses on the role of one of the most fundamental of medieval figures—the scribe, as well as on the products of scribes in the eighth to thirteenth centuries. It chiefly addresses how writing seems to have functioned and been conceived of and how present-day scholarly palaeographical methods do not always adjudicate medieval writing practices sensitively or convincingly. This matters, and especially so in our age of the digital aspect, since the inherited categories of description simply no longer hold force. What constitutes ‘bad handwriting’ then and is it, in fact, a description that can be validated by close analysis of scholarship and medieval writing practices?

Design and Grasping Force Modeling for a Soft Robotic Gripper with Multi-stem Twining

Design and Grasping Force Modeling for a Soft Robotic Gripper with Multi-stem Twining

Precise Measurement of Grasping Force for Noncollaborative Infants

AbstractAmong the medical parameters used for infants, the grasping force is particularly important because it indicates their musculoskeletal and neurological development. Although several grasping force measuring devices have been developed for infants, their accuracy and reliability are limited owing to their direction‐dependent sensing mechanisms. It is challenging to calculate the direction and area of the ambiguous forces applied by infants, and pediatricians cannot control the grasping method used by them. In this study, a direction‐independent grasping force measuring device is proposed that features a high resolution (0.1 kPa), cyclic stability (20 000 cycles), and linear sensitivity (21.73 µV kPa−1), and high accuracy and reliability. The grasping forces (average, minimum, and maximum) of the left (normal state) and right (injection needle inserted: uncomfortable state) hands of a 1‐day old infant can be successfully analyzed using the proposed device. It can be used to obtain the standard grasping force data of infants, which can contribute toward understanding the correlation between the grasping force and neurological diseases. The proposed device can be used to quantitatively measure the grasping force of not only infants but also the elderly; therefore, additional studies may report that the grasping force can be a discriminable parameter for identifying neurological diseases.

An Improved Approach for Grasp Force Sensing and Control of Upper Limb Soft Robotic Prosthetics.

The following research proposes a closed loop force control system, which is implemented on a soft robotic prosthetic hand. The proposed system uses a force sensing approach that does not require any sensing elements to be embedded in the prosthetic's fingers, therefore maintaining their monolithic structural integrity, and subsequently decreasing the cost and manufacturing complexity. This is achieved by embedding an aluminum test specimen with a full bridge strain gauge circuit directly inside the actuator's housing rather than in the finger. The location of the test specimen is precisely at the location of the critical section of the bending moment on the actuator housing due to the tension in the driving tendon. Therefore, the resulting loadcell can acquire a signal proportional to the prosthetic's grasping force. A PI controller is implemented and tested using this force sensing approach. The experiment design includes a flexible test object, which serves to visually demonstrate the force controller's performance through the deformation that the test object experiences. Setpoints corresponding to "light", "medium", and "hard" grasps were tested with pinch, tripod, and full grasps and the results of these tests are documented in this manuscript. The developed controller was found to have an accuracy of ±2%. Additionally, the deformation of the test object increased proportionally with the given grasp force setpoint, with almost no deformation during the light grasp test, slight deformation during the medium grasp test, and relatively large deformation of the test object during the hard grasp test.

Stiffness-Estimation-Based Grasping Force Fuzzy Control for Underactuated Prosthetic Hands

Human hands can rapidly perceive the stiffness of an object and quickly adapt to different soft and hard objects by adjusting the grasp force of each finger. However, patients with upper-limb loss have difficulty in outputting accurate grasp force based on objects stiffness information, as the efferent and afferent feedback of nerve channels was losing. To realize stiffness-based force control on a prosthetic hand, it is necessary to know the stiffness of the object to be grasped in advance. This article proposed a novel method to estimate the stiffness of the grasped object based on a kinematics model of prosthetic fingers. Using underactuated mechanisms, the stiffness of the grasped object can be derived based on the contact stiffness information that is obtained upon contact between the prosthetic hand and the target object. Based on the estimation of the stiffness of the grasped object, we developed a control strategy to adjust the inappropriate surface electromyography decoding force of the prosthetic hand, which simulated the motion of a human hand. The selected objects in experiments were representative and common objects used in daily life, with stiffness ranging from soft to hard. The proposed method was proven to be effective and feasible during experimental tests, where such a method can be used to quickly differentiate objects with different stiffness values and thus replicate the controllable stiffness-based force of a human hand with a prosthetic hand.

Continuous Grasping Force Estimation With Surface EMG Based on Huxley-Type Musculoskeletal Model.

Continuous grasping force estimation based on electromyography (EMG) signals, is very useful in practical applications including prosthetic control and human force observation. However, implementing the practical grasping force estimation usually considers a trade-off between the computational precision and resources. Specifically, the estimation based on the Huxley-type muscle model reaches detailed approximation of physiological process at a cost of larger computational resources for solving nonlinear partial differential equations while the counterpart with a traditional Hill-type muscle model. In this article, we achieve the grasping force estimation based on a reduced Huxley-type musculoskeletal model with high accuracy yet low time delay. Leveraging on a balanced truncation method, we further reduce the dimensionality of the spectral method solution in the Huxley-type musculoskeletal model for the model simplification. In addition, we introduce the Kalman filter method to process the EMG signals obtained by an armband, yielding better real-time performances and accuracy compared to the signal treatment using the traditional EMG filter method. Moreover, we also implement an effective identification of the model parameters using a particle swarm method. Finally, we trained the model on the first day and made grasping force estimation experiments involved with three participants over the course of a month. We envision that this effective and practical method would further improve the practical applications in the field of grasping force estimation.

Development of All-in-One Wakka to Make the Testing and Training Device of Adjustability for Grasping Force "iWakka" Compact

Development of All-in-One Wakka to Make the Testing and Training Device of Adjustability for Grasping Force "iWakka" Compact

Prediction Method for Large Grasping Force using Single-Channel Surface Electromyography: A Pilot Study

Frequent grasping with a large force in the workplace leads to work-related musculoskeletal disorders (WMSDs). Thus, the frequency and force of grasping in each worker should be monitored for preventing these WMSDs. The frequency of grasping can be counted by observation and existing methods using vision-based devices or wearable sensors. Grasping force measurement generally requires force sensors between the hand and objects. Almost force sensors are limited to the surface and shape of the object in measurement; thus, measurement method without force sensors is necessary to monitor grasping force in various workplaces. Previous studies developed measurement methods for grasping force using electromyography (EMG). However, almost existing methods require multiple-channels EMG for grasping force measurement. In addition, many methods were not applied to large grasping forces. Therefore, the objective of this study is to develop the prediction method for large grasping force using single-channel EMG. The proposed method predicts grasping force by machine learning and features calculated from single channel surface EMG (sEMG) signal on flexor digitorum superficialis (FDS) muscle. As a pilot study, the proposed method was tested by sEMG data of different large grasping forces (5 to 60 kgf). The results showed that the correlation between the actual and predicted grasping force was greater than 0.8. These results indicate that the proposed method could measure large grasping force by only single-channel sEMG.

Transmission Backlash Compensation and Grasping Force Estimation of Surgical Instruments for the Laparoscopic Minimally Invasive Surgery Robot

It is difficult to install the sensor on the end effector of a minimally invasive surgical robot with a narrow space within which to obtain the position and grasping force; thus, the elongation effect of cable-driven surgical instruments results in low precision control, which may damage the tissues and organs of patients. A novel transmission backlash compensation and grasping force saturation limitation algorithm based on the tension and displacement transmission model of a cable-pulley system is proposed to improve the operation’s accuracy and safety. The algorithm considers the force and position transmission characteristics of each stage of the cable-pulley system including the transition stage. Experimental verifications show that the proposed algorithm can significantly improve the position tracking accuracy of surgical instruments and the safety of grasping operations.

Principal Component Analysis of Grasp Force and Pose During In-Hand Manipulation

Principal Component Analysis of Grasp Force and Pose During In-Hand Manipulation

Grasping force optimization for dual-arm space robot after capturing target based on task compatibility

Grasping force optimization (GFO) is a crucial step in multi-arm manipulation tasks, aiming to suitably distribute the external force applied on the target to manipulators. The problem has not been applied to the field of space robot, which is more complex than ground mechanisms due to the dynamic coupling effect and multiple constraints. This paper presents the GFO method for the dual-arm space robot after capturing target. The problem is formulated as a convex optimization problem, which considers the friction constraints and generalized input constraints synthetically. The input constraints are derived for the space robotic system which contain both kinematic and dynamic characteristics. In order to reduce the dimension of the problem, a convenient method based on the concept of task compatibility is proposed to obtain the initial value and decouple the input constraints from the optimization problem. Meanwhile, the line search method based on eigenvalue analysis is adopted to determine the step size. Finally, simulation results are presented to show the effectiveness of the proposed method for different target motion.

Grasping Force Control of Robotic Gripper With High Stiffness

In this article, a robotic gripper is developed to study the grasping force control of rigid gripper with high stiffness. As observed in experiments, the dynamics of rigid gripper involves heavy uncertainties and nonlinearities, including backlash and force-dependent friction. The backlash is considered as a major discontinuous disturbance that can be compensated by a Preisach-operator-based inversion control. When increasing the tactile force, the backlash is strongly coupled with the force-dependent friction, which is considered as a continuous disturbance. To reject such continuous disturbance, an active disturbance rejection control (ADRC) is used to estimate the disturbance using an extended state observer and, then, actively reject it by adding a feed-forward control. The ADRC and backlash compensation work in parallel to reject both continuous and discontinuous disturbances, as observed in the rigid gripper. The robustness and stability analysis of the hybrid control proves that the hybrid control is stable, effective, and robust to the unknown disturbances involved in the rigid gripper. Both simulation and experimental results have shown that the ADRC can eliminate the steady-state error, and the Preisach-operator-based compensation can reduce the settling time and overshoot.