Biohybrid Robotic Hand to Investigate Tactile Encoding and Sensorimotor Integration.

For people who have experienced a spinal cord injury or an amputation, the recovery of sensation and motor control could be incomplete despite noteworthy advances with invasive neural interfaces. Our objective is to explore the feasibility of a novel biohybrid robotic hand model to investigate aspects of tactile sensation and sensorimotor integration with a pre-clinical research platform. Our new biohybrid model couples an artificial hand with biological neural networks (BNN) cultured in a multichannel microelectrode array (MEA). We decoded neural activity to control a finger of the artificial hand that was outfitted with a tactile sensor. The fingertip sensations were encoded into rapidly adapting (RA) or slowly adapting (SA) mechanoreceptor firing patterns that were used to electrically stimulate the BNN. We classified the coherence between afferent and efferent electrodes in the MEA with a convolutional neural network (CNN) using a transfer learning approach. The BNN exhibited the capacity for functional specialization with the RA and SA patterns, represented by significantly different robotic behavior of the biohybrid hand with respect to the tactile encoding method. Furthermore, the CNN was able to distinguish between RA and SA encoding methods with 97.84% ± 0.65% accuracy when the BNN was provided tactile feedback, averaged across three days in vitro (DIV). This novel biohybrid research platform demonstrates that BNNs are sensitive to tactile encoding methods and can integrate robotic tactile sensations with the motor control of an artificial hand. This opens the possibility of using biohybrid research platforms in the future to study aspects of neural interfaces with minimal human risk.

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 3×3 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.

Feeling the beat: a smart hand exoskeleton for learning to play musical instruments.

Individuals who have suffered neurotrauma like a stroke or brachial plexus injury often experience reduced limb functionality. Soft robotic exoskeletons have been successful in assisting rehabilitative treatment and improving activities of daily life but restoring dexterity for tasks such as playing musical instruments has proven challenging. This research presents a soft robotic hand exoskeleton coupled with machine learning algorithms to aid in relearning how to play the piano by 'feeling' the difference between correct and incorrect versions of the same song. The exoskeleton features piezoresistive sensor arrays with 16 taxels integrated into each fingertip. The hand exoskeleton was created as a single unit, with polyvinyl acid (PVA) used as a stent and later dissolved to construct the internal pressure chambers for the five individually actuated digits. Ten variations of a song were produced, one that was correct and nine containing rhythmic errors. To classify these song variations, Random Forest (RF), K-Nearest Neighbor (KNN), and Artificial Neural Network (ANN) algorithms were trained with data from the 80 taxels combined from the tactile sensors in the fingertips. Feeling the differences between correct and incorrect versions of the song was done with the exoskeleton independently and while the exoskeleton was worn by a person. Results demonstrated that the ANN algorithm had the highest classification accuracy of 97.13% ± 2.00% with the human subject and 94.60% ± 1.26% without. These findings highlight the potential of the smart exoskeleton to aid disabled individuals in relearning dexterous tasks like playing musical instruments.

Multichannel Sensorimotor Integration with a Dexterous Artificial Hand.

Background: People use their hands to perform sophisticated tasks like playing a musical instrument by integrating manifold and diverse sensations of touch with motor control strategies. In contrast, prosthetic hands lack the capacity for multichannel haptic feedback and multitasking functionality remains rudimentary. There is a dearth of research exploring the potential of upper limb absent (ULA) people to integrate multiple channels of haptic feedback into dexterous prosthetic hand control strategies. Methods: In this paper, we designed a novel experimental paradigm for three ULA people and nine additional subjects to investigate their ability to integrate two simultaneously activated channels of context-specific haptic feedback into their dexterous artificial hand control strategies. Artificial neural networks (ANN) were designed for pattern recognition of the array of efferent electromyogram signals that controlled the dexterous artificial hand. ANNs were also used to classify the directions that objects were sliding across two tactile sensor arrays on the index (I) and little (L) fingertips of the robotic hand. The direction of sliding contact at each robotic fingertip was encoded by different stimulation frequencies of wearable vibrotactile actuators for haptic feedback. The subjects were tasked with implementing different control strategies with each finger simultaneously depending upon the perceived directions of sliding contact. This required the 12 subjects to concurrently control individual fingers of the artificial hand by successfully interpreting two channels of simultaneously activated context-specific haptic feedback. Results: Subjects were able to accomplish this complex feat of multichannel sensorimotor integration with an overall accuracy of 95.53% ± 0.23%. While there was no statistically significant difference in the classification accuracy between ULA people and the other subjects, the ULA people required more time to correctly respond to the simultaneous haptic feedback slip signals, suggesting a higher cognitive load required by the ULA people. Conclusion: ULA people can integrate multiple channels of simultaneously activated and nuanced haptic feedback with their control of individual fingers of an artificial hand. These findings provide a step toward empowering amputees to multitask with dexterous prosthetic hands, which remains an ongoing challenge.

Human neuromarkers of tactile perception: state of the art in methods and findings.

Tactile perception is a multifaceted sense with complicated convergent/divergent peripheral pathways. Its neuromarkers remain poorly understood, due to the sense's inherent complexity and the confounding factor of intricate motor, cognitive and affective correlates. This gap hinders research evaluating interventions to restore touch in artificial hands. We inventorize state-of-the-art and recent innovations in control systems with soft and hard robotics that are poised to unlock more targeted non-invasive stimulations. We review neuromarkers observed for pressure, vibration, brushing, texture discrimination, pain, heat and cold, complemented with the covariates from movement, attention, working memory, multisensory and sensorimotor integration or competition (audition, vision) and affect. We analyze neural oscillations during sensory and (peripheral and central) electro-magnetic stimulation. This review matures a framework of reverse prediction, in which non-invasive observation of neural activity robustly and unobtrusively quantifies tactile perception.

Finite Element Analysis of the Effect of Dynamic Plating on Two-Level Anterior Cervical Discectomy Fusion Biomechanics.

BACKGROUND: The limitations of anterior cervical discectomy and fusion (ACDF) are related to mechanical failure of the construct after recurring subsidence and migration. In the present study, we evaluated the effect of the maximum rotation of variable angle screws on the range of motion (ROM), cage migration, and subsidence. METHODS: Five finite element models were developed from a C2-C7 cervical spine model. The first model was an intact C2-C7 spine model, and the second model was an altered C2-C7 model with C4-C6 cage insertion and a 2-level static plate. The other three models were altered C2-C7 models with the same C4-C6 cage insertion and a 2-level dynamic plate. RESULTS: The ROM of C4-C6 in the static plate model was reduced by ∼14° from the intact model but only reduced by ∼9° in the dynamic plate models. The maximum migration and subsidence at the cage-endplate interface in the dynamic plate models were lower than those in the static plate model for all moments. The von Mises stress of the C3-C4 and C6-C7 discs in the dynamic plate models was lower than that in the static plate model. CONCLUSIONS: Our results indicate that dynamic plating has promising potential (greater ROM and lower von Mises stress of discs) for stabilization in multilevel anterior cervical discectomy and fusion than static plating, although both dynamic and static plates showed lower ROM than the intact model. A lower screw rotational angle resulted in superior biomechanical performance (lower incidence of migration and subsidence) compared with a higher rotational angle in multilevel applications, regardless of loading.

Multichannel haptic feedback unlocks prosthetic hand dexterity.

Loss of tactile sensations is a major roadblock preventing upper limb-absent people from multitasking or using the full dexterity of their prosthetic hands. With current myoelectric prosthetic hands, limb-absent people can only control one grasp function at a time even though modern artificial hands are mechanically capable of individual control of all five digits. In this paper, we investigated whether people could precisely control the grip forces applied to two different objects grasped simultaneously with a dexterous artificial hand. Toward that end, we developed a novel multichannel wearable soft robotic armband to convey artificial sensations of touch to the robotic hand users. Multiple channels of haptic feedback enabled subjects to successfully grasp and transport two objects simultaneously with the dexterous artificial hand without breaking or dropping them, even when their vision of both objects was obstructed. Simultaneous transport of the objects provided a significant time savings to perform the deliveries in comparison to a one-at-a-time approach. This paper demonstrated that subjects were able to integrate multiple channels of haptic feedback into their motor control strategies to perform a complex simultaneous object grasp control task with an artificial limb, which could serve as a paradigm shift in the way prosthetic hands are operated.

Robotically Embodied Biological Neural Networks to Investigate Haptic Restoration with Neuroprosthetic Hands.

Neuroprosthetic limbs reconnect severed neural pathways for control of (and increasingly sensation from) an artificial limb. However, the plastic interaction between robotic and biological components is poorly understood. To gain such insight, we developed a novel noninvasive neuroprosthetic research platform that enables bidirectional electrical communications (action, sensory perception) between a dexterous artificial hand and neuronal cultures living in a multichannel microelectrode array (MEA) chamber. Artificial tactile sensations from robotic fingertips were encoded to mimic slowly adapting (SA) or rapidly adapting (RA) mechanoreceptors. Afferent spike trains were used to stimulate neurons in a region of the neuronal culture. Electrical activity from neurons at another region in the MEA chamber was used as the motor control signal for the artificial hand. Results from artificial neural networks (ANNs) showed that the haptic model used to encode RA or SA fingertip sensations affected biological neural network (BNN) activity patterns, which in turn impacted the behavior of the artificial hand. That is, the exhibited finger tapping behavior of this closed-loop neurorobotic system showed statistical significance (p<0.01) between the haptic encoding methods across two different neuronal cultures and over multiple days. These findings suggest that our noninvasive neuroprosthetic research platform can be used to devise high-throughput experiments exploring how neural plasticity is affected by the mutual interactions between perception and action.

Human-Inspired Robotic Eye-Hand Coordination Enables New Communication Channels Between Humans and Robots.

This paper concerns human-inspired robotic eye-hand coordination algorithms using custom built robotic eyes that were interfaced with a Baxter robot. Eye movement was programmed anthropomorphically based on previously reported research on human eye-hand coordination during grasped object transportation. Robotic eye tests were first performed on a component level where accurate position and temporal control were achieved. Next, 11 human subjects were recruited to observe the novel robotic system to quantify the ability of robotic eye-hand coordination algorithms to convey two kinds of information to people during object transportation tasks: first, the transported object's delivery location and second, the level of care exerted by the robot to transport the object. Most subjects correlated decreased frequency in gaze fixations on an object's target location with increased care of transporting an object, although these results were somewhat mixed among the 11 human subjects. Additionally, the human subjects were able to reliably infer the delivery location of the transported object purely by the robotic eye-hand coordination algorithm with an overall success rate of 91.4%. These results suggest that anthropomorphic eye-hand coordination of robotic entities could be useful in pedagogical or industrial settings.

Hierarchical Tactile Sensation Integration from Prosthetic Fingertips Enables Multi-Texture Surface Recognition.

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.

Cage-screw and anterior plating combination reduces the risk of micromotion and subsidence in multilevel anterior cervical discectomy and fusion-a finite element study.

BACKGROUND CONTEXT: Anterior cervical discectomy and fusion (ACDF) is widely used to treat patients with spinal disorders, where the cage is a critical component to achieve satisfactory fusion results. However, it is still not clear whether a cage with screws or without screws will be the best choice for long-term fusion as the micromotion (sliding distance) and subsidence (penetration) of the cage still take place repeatedly. PURPOSE: This study aims to examine the effect of cage-screws on the biomechanical characteristics of the human spine, implanted cage, and associate hardware by comparing the micromotion and subsidence. STUDY DESIGN: A finite element (FE) analysis study. METHODS: A FE model of a C3-C5 cervical spine with ACDF was developed. The spinal segment was modeled with the removal of the anterior longitudinal ligament (ALL), posterior longitudinal ligament (PLL), and discectomy was then implanted with a cage-screw system. Three models were analyzed: the first was the original spine (S1 model), the second, S2, was implanted with cages and anterior plating, and the third, S3, was implanted with a cage-screw system in addition to the anterior plate. All investigations were under 1 N•m in flexion, extension, lateral bending, and axial rotation situations. RESULTS: Finite element analysis (FEA) demonstrated that range of motion (ROM) at C3-C4 in the S2 model was significantly reduced more than that in the S3 model, while the ROM at both C4-C5 in the S3 model was reduced more than that in the S2 model in all simulations. The ROM at C3-C5 in the S1 model was reduced by over 5° in the S2 and S3 models in all loading conditions. The micromotion and subsidence at all contacts of C3-C5 in the S3 model were lower than that in the S2 model in all flexion, extension, bending, and axial simulations. The subsidence and micromotion could be seen in the barrier area of the S2 model, while they occurred near the edge of the screw in the S3 model. CONCLUSIONS: These results showed that the cage-screw and anterior plating combination has promising potential to reduce the risk of micromotion and subsidence of implanted cages in two or more level ACDFs. CLINICAL SIGNIFICANCE: The use of double segmental fixation with cage-screw anterior plating combination constructs may increase the stiffness of the construct and reduce the incidence of clinical and radiographic pseudarthrosis following multilevel ACDF, which in turn, could decrease the need for revision surgeries or supplemental posterior fixation.

Robotic Replica of a Human Spine Uses Soft Magnetic Sensor Array to Forecast Intervertebral Loads and Posture after Surgery.

Cervical disc implants are conventional surgical treatments for patients with degenerative disc disease, such as cervical myelopathy and radiculopathy. However, the surgeon still must determine the candidacy of cervical disc implants mainly from the findings of diagnostic imaging studies, which can sometimes lead to complications and implant failure. To help address these problems, a new approach was developed to enable surgeons to preview the post-operative effects of an artificial disc implant in a patient-specific fashion prior to surgery. To that end, a robotic replica of a person's spine was 3D printed, modified to include an artificial disc implant, and outfitted with a soft magnetic sensor array. The aims of this study are threefold: first, to evaluate the potential of a soft magnetic sensor array to detect the location and amplitude of applied loads; second, to use the soft magnetic sensor array in a 3D printed human spine replica to distinguish between five different robotically actuated postures; and third, to compare the efficacy of four different machine learning algorithms to classify the loads, amplitudes, and postures obtained from the first and second aims. Benchtop experiments showed that the soft magnetic sensor array was capable of precisely detecting the location and amplitude of forces, which were successfully classified by four different machine learning algorithms that were compared for their capabilities: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), and Artificial Neural Network (ANN). In particular, the RF and ANN algorithms were able to classify locations of loads applied 3.25 mm apart with 98.39% ± 1.50% and 98.05% ± 1.56% accuracies, respectively. Furthermore, the ANN had an accuracy of 94.46% ± 2.84% to classify the location that a 10 g load was applied. The artificial disc-implanted spine replica was subjected to flexion and extension by a robotic arm. Five different postures of the spine were successfully classified with 100% ± 0.0% accuracy with the ANN using the soft magnetic sensor array. All results indicated that the magnetic sensor array has promising potential to generate data prior to invasive surgeries that could be utilized to preoperatively assess the suitability of a particular intervention for specific patients and to potentially assist the postoperative care of people with cervical disc implants.

Compliant Underwater Manipulator with Integrated Tactile Sensor for Nonlinear Force Feedback Control of an SMA Actuation System.

Design, sensing, and control of underwater gripping systems remain challenges for soft robotic manipulators. Our study investigates these critical issues by designing a shape memory alloy (SMA) actuation system for a soft robotic finger with a directly 3D-printed stretchable skin-like tactile sensor. SMA actuators were thermomechanically trained to assume a curved finger-like shape when Joule heated, and the flexible multi-layered tactile sensor was directly 3D-printed onto the surface of the fingertip. A nonlinear controller was developed to enable precise fingertip force control using feedback from the compliant tactile sensor. Underwater experiments were conducted using closed-loop force feedback from the directly 3D-printed tactile sensor with the SMA actuators, showing satisfactory force tracking ability. Furthermore, a 3D finite element model was developed to more deeply understand the shape memory thermal-fluidic-structural multi-physics simulation of the manipulator underwater. An application for human control via electromyogram (EMG) signals also demonstrated an intuitive way for a person to operate the submerged robotic finger. Together, these results suggested that the soft robotic finger could be used to carefully manipulate fragile objects underwater.

Shape memory alloy tube actuators inherently enable internal fluidic cooling for a robotic finger under force control.

This paper presents the design, control and evaluation of a novel robotic finger actuated by shape memory alloy (SMA) tubes which intrinsically afford an internal conduit for fluidic cooling. The SMA tubes are thennomechanically programmed to flex the robotic finger when Joule heated. A superelastic SMA plate provides a spring return motion to extend the finger when cooling liquid is pumped through the internal channel of the SMA tube actuators. The mechanical design and nonlinear force controller are presented for this unique robotic finger. Sinusoidal and step response experiments demonstrate excellent error minimization when operated below the bandwidth which was empirically determined to be 6 rad s-1. Disturbance rejection experiments are also performed to demonstrate the potential to minimize externally applied forces. This method of internal liquid cooling of Joule heated SMA tubes simultaneously increases the system bandwidth and expands the potential uses of SMA actuators for robotic applications. The results show that this novel robotic finger is capable of precise force control and has a high strength to weight ratio. The finger can apply a force of 4.35 N and has a mass of 30 g. Implementing this design into wearable prosthetic devices could enable lightweight, high strength applications previously not achievable.

Thrust force characterization of free-swimming soft robotic jellyfish.

Five unique soft robotic jellyfish were manufactured with eight pneumatic network tentacle actuators extending radially from their centers. These jellyfish robots were able to freely swim untethered in the ocean, to steer from side to side, and to swim through orifices more narrow than the nominal diameter of the jellyfish. Each of the five jellyfish robots were manufactured with a different composition of body and tentacle actuator Shore hardness. A three-factor study was performed with these five jellyfish robots to determine the impact that actuator material Shore hardness, actuation frequency, and tentacle stroke actuation amplitude had upon the measured thrust force. It was found that all three of these factors significantly impacted mean thrust force generation, which peaked with a half-stroke actuation amplitude at a frequency of 0.8 Hz.

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.

Robotic Finger Force Sensor Fabrication and Evaluation Through a Glove.

This force-feedback approach compares the effect on the sensing ability through a worn glove of the force application of an i-Limb Ultra robotic hand for several experimental scenarios. A Takktile sensor was integrated into a fabricated fingertip to measure the applied force of the i-Limb Ultra. A controller was then designed using MATLAB/Simulink to manipulate the finger motion of the i-Limb to apply force to an external load cell. Testing was performed to check the force measurements and sensing ability/quality for two cases: hand with no glove and hand with a nitrile glove. Each of these scenarios were tested by applying fingertip force in 3 different modes: open/close with no contact, continuous tapping and constant force.

Armband with Soft Robotic Actuators and Vibrotactile Stimulators for Bimodal Haptic Feedback from a Dexterous Artificial Hand.

The haptic sense relies upon a plurality of receptors and pathways to produce a complex perceptual experience of contact, pressure, taps, vibrations and flutters. This complexity is yet to be reproduced in haptic feedback interfaces that are used by people controlling a dexterous robotic hand, be it for limb-absence or teleoperation. The goal of the present bimodal haptic armband is to convey both low-frequency pressure changes and high-frequency vibrations from a dexterous robotic hand to a human's upper arm, so as to guide his/her control of the artificial limb. To that end, we design and manufacture four novel soft robotic armbands combining inflatable air chambers and vibrotactile stimulators. We develop control systems for both pathways. We conduct a series of benchtop tests to determine the pneumatic and vibrotactile performance and select from competing designs and materials. We test two of the resulting bimodal haptic armband on human subjects and confirm their ability to use both aspects of this haptic information. Arguing that dexterous artificial hands are presently not used to their fullest capability by the dearth of haptic information in users, this work aims to achieve a more realistic tactile experience for a fluent, more natural usage of robotic artificial hands.

Direction of Slip Detection for Adaptive Grasp Force Control with a Dexterous Robotic Hand.

A novel method of tactile communication among human-robot and robot-robot collaborative teams is developed for the purpose of adaptive grasp control of dexterous robotic hands. Neural networks are applied to the problem of classifying the direction objects slide against different tactile fingertip sensors in real-time. This ability to classify the direction that an object slides in a dexterous robotic hand was used for adaptive grasp synergy control to afford context dependent robotic reflexes in response to the direction of grasped object slip. Case studies with robot-robot and human-robot collaborative teams successfully demonstrated the feasibility; when object slip in the direction of gravity (towards the ground) was detected, the dexterous hand increased the grasp force to prevent dropping the object. When a human or robot applied an upward force to cause the grasped object to slip upward, the dexterous hand was programmed to release the object into the hand of the other team member. This method of adaptive grasp control using direction of slip detection can improve the efficiency of human-robot and robot-robot teams.

Force and Pressure Control of Soft Robotic Actuators.

Soft Robotic Actuators (SRAs) have piqued the interest of researchers in recent years. SRAs are generally constructed of soft elastomers and use air or water as a mean of actuation. Due to the inherent properties of these actuators, they are ideal for HumanRobot Interactions (HRI), exoskeletons for rehabilitation and for grasping delicate objects. Since SRA's are actuated using a fluid, being able to effectively control the rate of actuation, pressure and the force applied is necessary so that the actuator and the object being grasped does not get damaged. This paper aims to evaluate three types of controllers, an open-loop controller, pressure-feedback controller, and a force-feedback controller, all used to control an SRA. A custom test stand was built to hold the SRA and test it with all three controllers. The pressure-feedback controller was set to limit the pressure to 8.9 kPa and the force was limited to 0.147 N in the force-feedback controller. Since the open-loop controller had no feedback, the SRA was actuated at a specified frequency while force and pressure measurements were taken. The force-feedback and the pressure-feedback controllers accurately controlled the actuators and the open loop-controller was able to actuate the SRA reliably.