Adaptive Human Control Gains During Precision Grip

Eight human test subjects attempted to track a desired position trajectory with an instrumented manipulandum (MN). The test subjects used the MN with three different levels of stiffness. A transfer function was developed to represent the human application of a precision grip from the data when the test subjects initially displaced the MN so as to learn the position mapping from the MN onto the display. Another transfer function was formed from the data of the remainder of the experiments, after significant displacement of the MN occurred. Both of these transfer functions accurately modelled the system dynamics for a portion of the experiments, but neither was accurate for the duration of the experiments because the human grip dynamics changed while learning the position mapping. Thus, an adaptive system model was developed to describe the learning process of the human test subjects as they displaced the MN in order to gain knowledge of the position mapping. The adaptive system model was subsequently validated following comparison with the human test subject data. An examination of the average absolute error between the position predicted by the adaptive model and the actual experimental data yielded an overall average error of 0.34mm for all three levels of stiffness

Electromyogram Synergy Control of a Dexterous Artificial Hand to Unscrew and Screw Objects

Due to their limited dexterity, it is currently not possible to use a commercially available prosthetic hand to unscrew or screw objects without using elbow and shoulder movements. For these tasks, prosthetic hands function like a wrench, which is unnatural and limits their use in tight working environments. Results from timed rotational tasks with human subjects demonstrate the clinical need for increased dexterity of prosthetic hands, and a clinically viable solution to this problem is presented for an anthropomorphic artificial hand

Hierarchical tactile sensation integration from prosthetic fingertips enables multi-texture surface recognition\u3csup\u3e†\u3c/sup\u3e

Multifunctional flexible tactile sensors could be useful to improve the control of prosthetic hands. To that end, highly stretchable liquid metal tactile sensors (LMS) were designed, manufactured via photolithography, and incorporated into the fingertips of a prosthetic hand. Three novel contributions were made with the LMS. First, individual fingertips were used to distinguish between different speeds of sliding contact with different surfaces. Second, differences in surface textures were reliably detected during sliding contact. Third, the capacity for hierarchical tactile sensor integration was demonstrated by using four LMS signals simultaneously to distinguish between ten complex multi-textured surfaces. Four different machine learning algorithms were compared for their successful classification capabilities: K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and neural network (NN). The time-frequency features of the LMSs were extracted to train and test the machine learning algorithms. The NN generally performed the best at the speed and texture detection with a single finger and had a 99.2 ± 0.8% accuracy to distinguish between ten different multi-textured surfaces using four LMSs from four fingers simultaneously. The capability for hierarchical multi-finger tactile sensation integration could be useful to provide a higher level of intelligence for artificial hands

Human Model Reference Adaptive Control of a Prosthetic Hand

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Adaptive Sliding Manifold Slope via Grasped Object Stiffness Detection with a Prosthetic Hand

Upper limb amputees have expressed the desire for their prosthetic hands to better adapt to the parameters of different grasped objects. In response to this need, an adaptive sliding mode controller (ASMC) is developed that has a variable-slope manifold which is dependent upon the stiffness of the grasped object. The ASMC is experimentally compared to a sliding mode controller (SMC) which has a constant manifold slope over a wide range of grasped object stiffness, ranging from an empty hand to a steel bar. Experimental results indicate that both controllers have satisfactory percent overshoot characteristics; however, the ASMC has significantly less absolute error for all experiments performed with eight different levels of grasped object stiffness

Flexible Magnetic Skin Sensor Array for Torsion Perception

Prosthetic hands help upper limb amputees and people who were born without hands. Currently, these prostheses are rather rudimentary and do not provide adequate sensing capabilities compared to a human hand. People use their natural hands to perceive complex tactile phenomena such as shear and torsion using thousands of mechanoreceptors in their fingertips. The capability to detect torsional loads at the fingertips is a notable gap in prosthetic hand sensation. Flexible tactile sensors are a promising new technology that would be ideal for prosthetic hands since they allow for stretching and movement like human skin without damage to the sensor. Therefore, the purpose of this study is to determine whether a flexible magnetic sensor array combined with an artificial neural network (ANN) can detect and classify torsion. The flexible magnetic sensor is designed as a 3x3 array of magnets embedded in a stretchable elastomer which are situated atop a corresponding array of Hall effect sensors. Torques applied to the soft magnetic skin caused displacement of the magnetic fields that were perceived by the nine Hall effect sensors. In this study, ten different values of torque were applied to the flexible magnetic sensor array using a robotic arm to ensure consistency. Data were used to train an ANN to classify the applied torques. The ANN was trained ten times and could predict the applied torque with an average training classification accuracy of 97.48% ± 0.33%. Given the results of this study, this novel sensor design could enable more refined sensations of touch for people who use prosthetic hands

Human-Inspired Reflex to Autonomously Prevent Slip of Grasped Objects Rotated with a Prosthetic Hand

Autonomously preventing grasped objects from slipping out of prosthetic hands is an important feature for limb-absent people since they cannot directly feel the grip force applied to grasped objects. Oftentimes, a satisfactory grip force in one situation will be inadequate in different situations, such as when the object is rotated or transported. Over time, people develop a grip reflex to prevent slip of grasped objects when they are rotated with respect to gravity by their natural hands. However, this reflexive trait is absent in commercially available prosthetic hands. This paper explores a human-inspired grasp reflex controller for prosthetic hands to prevent slip of objects when they are rotated. This novel human-inspired grasped object slip prevention controller is evaluated with 6 different objects in benchtop tests and by 12 able-bodied subjects during human experiments replicating realistic tasks of daily life. An analysis of variance showed highly significant improvement in the number of successfully completed cycles for both the benchtop and human tests when the slip prevention reflex was active. An object sorting task, which was designed to serve as a cognitive distraction for the human subjects while controlling the prosthetic hand, had a significant impact on many of the performance metrics. However, assistance from the novel slip prevention reflex mitigated the effects of the distraction, offering an effective method for reducing both object slip and the required cognitive load from the prosthetic hand user

Enhanced Visual Feedback for Slip Prevention with a Prosthetic Hand

Upper limb amputees have no direct sense of the grip force applied by a prosthetic hand; thus, precise control of the applied grip force is difficult for amputees. Since there is little object deformation when rigid objects are grasped, it is difficult for amputees to visually gauge the applied grip force in this situation

Grasp-Dependent Slip Prevention for a Dexterous Artificial Hand via Wrist Velocity Feedback Read More: http://www.worldscientific.com/doi/abs/10.1142/S0219843614500169

A proportional controller is compared to a nonlinear backstepping controller with four different grasps for a dexterous anthropomorphic hand. A bioinspired grasp-dependent control scheme which autonomously modulates the grip force using wrist velocity feedback to prevent grasped object slip is also introduced. Four different grasp types are evaluated to illustrate how the wrist velocity feedback architecture must differ depending upon the manner in which objects are grasped. The backstepping controller can successfully increase grip force with wrist velocity in a robustly stable bioinspired fashion. Experimental results show that the developed backstepping controller improves the position tracking abilities for multiple periodic inputs, as well as reduces step input overshoot. The slip prevention capabilities of the backstepping controller are also demonstrated and compared to the proportional control scheme. Results of the slip prevention experiments show that both the grasp type and manipulator orientation with respect to gravity are significant factors in the performance of the controllers. The backstepping control scheme significantly improves slip prevention of grasped objects for multiple grasps and in two different orientations with respect to gravity. Read More: http://www.worldscientific.com/doi/abs/10.1142/S021984361450016

Human-Inspired Feedback Synergies for Environmental Interaction with a Dexterous Robotic Hand

Effortless control of the human hand is mediated by the physical and neural couplings inherent in the structure of the hand. This concept was explored for environmental interaction tasks with the human hand, and a novel human-inspired feedback synergy (HFS) controller was developed for a robotic hand which synchronized position and force feedback signals to mimic observed human hand motions. This was achieved by first recording the finger joint motion profiles of human test subjects, where it was observed that the subjects would extend their fingers to maintain a natural hand posture when interacting with different surfaces. The resulting human joint angle data were used as inspiration to develop the HFS controller for the anthropomorphic robotic hand, which incorporated finger abduction and force feedback in the control laws for finger extension. Experimental results showed that by projecting a broader view of the tasks at hand to each specific joint, the HFS controller produced hand motion profiles that closely mimic the observed human responses and allowed the robotic manipulator to interact with the surfaces while maintaining a natural hand posture. Additionally, the HFS controller enabled the robotic hand to autonomously traverse vertical step discontinuities without prior knowledge of the environment, visual feedback, or traditional trajectory planning techniques