Force Control Strategy of Five-Digit Precision Grasping With Aligned and Unaligned Configurations

Objective To investigate the digit force control during a five-digit precision grasp in aligned (AG) and unaligned grasping (UG) configurations. Background The effects of various cylindrical handles for tools on power grasp performance have been previously investigated. However, there is little information on force control strategy of precision grasp to fit various grasping configurations. Method Twenty healthy young adults were recruited to perform a lift-hold-lower task. The AG and UG configurations on a cylindrical simulator with force transducers were adjusted for each individual. The applied force and moment, the force variability during holding, and force correlations between thumb and each finger were measured. Result No differences in applied force, force correlation, repeatability, and variability were found between configurations. However, the moments applied in UG were significantly larger than those in AG. Conclusion The force control during precision grasp did not change significantly across AG and UG except for the digit moment. The simulator is controlled efficiently with large moment during UG, which is thus the optimal configuration for precision grasping with a cylindrical handle. Further research should consider the effects of task type and handle design on force control, especially for individuals with hand disorders. Application To design the handle of specific tool, one should consider the appropriate configuration according to the task requirements of precision grasping to reduce the risk of accumulating extra loads on digits with a cylindrical handle.

Grasping Assisting Algorithm in Tele-Operated Robotic Gripper

The involvement of Robots and automated machines in different industries has increased drastically in recent years. Part of this revolution is accomplishing tasks previously performed by humans with advanced robots, which would replace the entire human workforce in the future. In some industries the workers are required to complete different operations in hazardous or difficult environments. Operations like these could be replaced with the use of tele-operated systems that have the capability of grasping objects in their surroundings, thus abandoning the need for the physical presence of the human operator at the area while still allowing control. In this research our goal is to create an assisting system that would improve the grasping of a human operator using a tele-operated robotic gripper and arm, while advising the operator but not forcing a solution. For a given set of objects we computed the optimal grasp to be achieved by the gripper, based on two grasp quality measures of our choosing (namely power grasp and precision grasp). We then tested the performance of different human subjects who tried to grasp the different objects with the tele-operated system, while comparing their success to unassisted and assisted grasping. Our goal is to create an assisting algorithm that would compute optimal grasps and might be integrated into a complete, state-of-the-art tele-operated system.

Motion prediction using electromyography and sonomyography for an individual with transhumeral limb loss

Controlling multi-articulated prosthetic hands with surface electromyography can be challenging for users. Sonomyography, or ultrasound-based sensing of muscle deformation, avoids some of the problems of electromyography and enables classification of multiple motion patterns in individuals with upper limb loss. Because sonomyography has been previously studied only in individuals with transradial limb loss, the purpose of this study was to assess the feasibility of an individual with transhumeral limb loss using this modality for motion classification. A secondary aim was to compare motion classification performance between electromyography and sonomyography. A single individual with transhumeral limb loss created two datasets containing 11 motions each (individual flexion of each finger, thumb abduction, power grasp, key grasp, tripod, point, pinch, wrist pronation). Electromyography or sonomyography signals associated with every motion were acquired and cross-validation accuracy was computed for each dataset. While all motions were usually predicted successfully with both electromyography and sonomyography, the cross-validation accuracies were typically higher for sonomyography. Although this was an exploratory study, the results suggest that controlling an upper limb prosthesis using sonomyography may be feasible for individuals with transhumeral limb loss.

Reach to Grasp Planning for a Synergy-Controlled Robotic Hand based on Pesudo-Distance Formulation

In the past several years, grasp analysis of multi-fingered robotic hands has been actively studied through the use of posture synergies. In these grasping planning algorithms, a formulated optimization is usually performed in the hand’s low-dimensional representation together with the hand’s position and orientation. The optimization terminates at a stable grasp, often after repeated trials with different initial guesses. Furthermore, there is no guarantee that the generated grasp leads to a smooth reach-to-grasp trajectory since the grasping planning process mostly concerns hand poses with the fingers proximal to the object. A unified theoretical framework of a gradient-based iterative algorithm is hence proposed in this paper to plan a reach-to-grasp task, predicting the grasp quality and adjusting the hand’s posture synergies, position and orientation during the approaching phase to achieve a stable grasp. The grasp quality measurement is adopted from a highly efficient pseudo-distance formulation. Stable power grasp and precision pinch can be consistently and intentionally planned with different contact conditions specified in the formulation, which means that an intention for planning a power grasp would not generate a pinch result. Several numerical simulation case studies are presented to demonstrate the effectiveness of the proposed algorithm.

How does simulation of an observed external body state influence categorisation of an easily graspable object?

Several works have provided evidence of a resonant motor effect while observing a hand interacting with painful stimuli. The aim of this work is to show that participants are sensitive to the observation of an injured hand when they have to categorise an easily graspable object with their own hand. In Experiment 1, participants indicated whether or not photographs of objects (graspable or non-graspable, left or right oriented) could be grasped with their dominant hand, by tapping a key on a keyboard. Target objects were preceded by primes consisting of photographs of hands (injured vs healthy) in a grasping posture (power grasp). Experiment 2 consisted of two phases: In the first phase, participants had to categorise square or circle shapes. After their response (Group 1: tapping a key vs Group 2: constricting a hand grip), photograph of two types of hand (injured vs healthy) was displayed on the computer screen. In the second phase, participants had to indicate whether objects could be easily grasped with their dominant hand. Target objects were preceded by primes (square and circle) as shown in the first phase. Results show that response times were slower when the graspable target objects were right oriented and preceded by the photograph or a geometric shape associated with an injured hand. This response delay was accentuated in the handgrip condition. These results highlight that the view of an injured hand activates motor programme and pain mechanisms associated with participants relative to the consequences of the simulated grasping action.

HyPro: A Multi-DoF Hybrid-Powered Transradial Robotic Prosthesis

This paper proposes a multi-DoF hybrid-powered transradial robotic prosthesis, named HyPro. The HyPro consists of two prosthetic units: hand and wrist that can achieve five grasping patterns such as power grasp, tip grasp, lateral grasp, hook grasp, and index point. It is an underactuated device with 15 degrees of freedom. A hybrid powering concept is proposed and implemented on hand unit of HyPro where the key focus is on restoration of grasp functions of biological hand. A novel underactuated mechanism is introduced to achieve the required hand preshaping for a given grasping pattern using electric power in the pregrasp stage and body power is used in grasp stage to execute the final grasping action with the selected fingers. Unlike existing hybrid prostheses where each of the joints is separately controlled by either electric or body power, the proposed prosthesis is capable of delivering grasping power in combination. The wrist unit of HyPro is designed and developed to achieve flexion-extension and supination-pronation using electric power. Experiments were carried out to evaluate the functionality and performance of the proposed hybrid-powered robotic prosthesis. The results verified the potential of HyPro to perform intended grasping patterns effectively and efficiently.