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1. Texture Discrimination using a Flexible Tactile Sensor Array on a Soft Biomimetic Finger

   https://doi.org/10.1109/SENSORS43011.2019.8956704  

   Sankar, Sriramana ; Brown, Alisa ; Balamurugan, Darshini ; Nguyen, Harrison ; Iskarous, Mark ; Simcox, Talya ; Kumar, Deepesh ; Nakagawa, Andrei ; Thakor, Nitish ( October 2019 , IEEE Sensors)

   null (Ed.)

   Soft robotic fingers provide enhanced flexibility and dexterity when interacting with the environment. The capability of soft fingers can be further improved by integrating them with tactile sensors to discriminate various textured surfaces. In this work, a flexible 3x3 fabric-based tactile sensor array was integrated with a soft, biomimetic finger for a texture discrimination task. The finger palpated seven different textured plates and the corresponding tactile response was converted into neuromorphic spiking patterns, mimicking the firing pattern of mechanoreceptors in the skin. Spike-based feature metrics were used to classify different textures using the support vector machine (SVM) classifier. The sensor was able to achieve an accuracy of 99.21% when two features, mean spike rate and average inter-spike interval, from each taxel were used as inputs into the classifier. The experiment showed that an inexpensive, soft, biomimetic finger combined with the flexible tactile sensor array can potentially help users perceive their environment better. 

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2. Unsupervised Learning and Adaptive Classification of Neuromorphic Tactile Encoding of Textures

   https://doi.org/10.1109/BIOCAS.2018.8584702  

   Iskarous, Mark M. ; Nguyen, Harrison H. ; Osborn, Luke E. ; Betthauser, Joseph L. ; Thakor, Nitish V. ( October 2018 , Proceedings of IEEE Biomedical Circuits and Systems)

   null (Ed.)

   In this work, we investigated the classification of texture by neuromorphic tactile encoding and an unsupervised learning method. Additionally, we developed an adaptive classification algorithm to detect and characterize the presence of new texture data. The neuromorphic tactile encoding of textures from a multilayer tactile sensor was based on the physical structure and afferent spike signaling of human glabrous skin mechanoreceptors. We explored different neuromorphic spike pattern metrics and dimensionality reduction techniques in order to maximize classification accuracy while improving computational efficiency. Using a dataset composed of 3 textures, we showed that unsupervised learning of the neuromorphic tactile encoding data had high classification accuracy (mean=86.46%, sd=5 .44%). Moreover, the adaptive classification algorithm was successful at determining that there were 3 underlying textures in the training dataset. In this work, tactile information is transformed into neuromorphic spiking activity that can be used as a stimulation pattern to elicit texture sensation for prosthesis users. Furthermore, we provide the basis for identifying new textures adaptively which can be used to actively modify stimulation patterns to improve texture discrimination for the user. 

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3. Synthetic Muscle Electroactive Polymer (EAP) pressure sensing and controlled shape-morphing for robotic grippers

   https://doi.org/10.1117/12.2583295  

   Briggs, Calum ; Cheng, Tianyu ; Meredith, Margot ; Vicars, Peter N. ; Rasmussen, Lenore ; Zhong, Alexander ( March 2021 , Proc. SPIE 11587, Electroactive Polymer Actuators and Devices (EAPAD) XXIII,)

   Madden, John D. ; Anderson, Iain A. ; Shea, Herbert R. (Ed.)

   Ras Labs makes Synthetic Muscle™, which is a class of electroactive polymer (EAP) based materials and actuators that sense pressure (gentle touch to high impact), controllably contract and expand at low voltage (1.5 V to 50 V, including use of batteries), and attenuate force. We are in the robotics era, but robots do have their challenges. Currently, robotic sensing is mainly visual, which is useful up until the point of contact. To understand how an object is being gripped, tactile feedback is needed. For handling fragile objects, if the grip is too tight, breakage occurs, and if the grip is too loose, the object will slip out of the grasp, also leading to breakage. Rigid robotic grippers using a visual feedback loop can struggle to determine the exact point and quality of contact. Robotic grippers can also get a stuttering effect in the visual feedback loop. By using soft Synthetic Muscle™ based EAP pads as the sensors, immediate feedback was generated at the first point of contact. Because these pads provided a soft, compliant interface, the first point of contact did not apply excessive force, allowing the force applied to the object to be controlled. The EAP sensor could also detect a change in pressure location on its surface, making it possible to detect and prevent slippage by then adjusting the grip strength. In other words, directional glide provided feedback for the presence of possible slippage to then be able to control a slightly tighter grip, without stutter, due to both the feedback and the soft gentleness of the fingertip-like EAP pads themselves. The soft nature of the EAP fingertip pad also naturally held the gripped object, improving the gripping quality over rigid grippers without an increase in applied force. Analogous to finger-like tactile touch, the EAPs with appropriate coatings and electronics were positioned as pressure sensors in the fingertip or end effector regions of robotic grippers. This development of using Synthetic Muscle™ based EAPs as soft sensors provided for sensors that feel like the pads of human fingertips. Basic pressure position and magnitude tests have been successful, with pressure sensitivity down to 0.05 N. Most automation and robots are very strong, very fast, and usually need to be partitioned away from humans for safety reasons. For many repetitive tasks that humans do with delicate or fragile objects, it would be beneficial to use robotics; whether it is for agriculture, medical surgery, therapeutic or personal care, or in extreme environments where humans cannot enter, including with contagions that have no cure. Synthetic Muscle™ was also retrofitted as actuator systems into off-the-shelf robotic grippers and is being considered in novel biomimetic gripper designs, operating at low voltages (less than 50 V). This offers biomimetic movement by contracting like human muscles, but also exceeds natural biological capabilities by expanding under reversed electric polarity. Human grasp is gentle yet firm, with tactile touch feedback. In conjunction with shape-morphing abilities, these EAPs also are being explored to intrinsically sense pressure due to the correlation between mechanical force applied to the EAP and its electronic signature. The robotic field is experiencing phenomenal growth in this fourth phase of the industrial revolution, the robotics era. The combination of Ras Labs’ EAP shape-morphing and sensing features promises the potential for robotic grippers with human hand-like control and tactile sensing. This work is expected to advance both robotics and prosthetics, particularly for collaborative robotics to allow humans and robots to intuitively work safely and effectively together. 

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4. Biomimetic sensory feedback through peripheral nerve stimulation improves dexterous use of a bionic hand

   https://doi.org/10.1126/scirobotics.aax2352  

   George, J. A. ; Kluger, D. T. ; Davis, T. S. ; Wendelken, S. M. ; Okorokova, E. V. ; He, Q. ; Duncan, C. C. ; Hutchinson, D. T. ; Thumser, Z. C. ; Beckler, D. T. ; et al ( July 2019 , Science Robotics)

   We describe use of a bidirectional neuromyoelectric prosthetic hand that conveys biomimetic sensory feedback. Electromyographic recordings from residual arm muscles were decoded to provide independent and proportional control of a six-DOF prosthetic hand and wrist—the DEKA LUKE arm. Activation of contact sensors on the prosthesis resulted in intraneural microstimulation of residual sensory nerve fibers through chronically implanted Utah Slanted Electrode Arrays, thereby evoking tactile percepts on the phantom hand. With sensory feedback enabled, the participant exhibited greater precision in grip force and was better able to handle fragile objects. With active exploration, the participant was also able to distinguish between small and large objects and between soft and hard ones. When the sensory feedback was biomimetic—designed to mimic natural sensory signals—the participant was able to identify the objects significantly faster than with the use of traditional encoding algorithms that depended on only the present stimulus intensity. Thus, artificial touch can be sculpted by patterning the sensory feedback, and biologically inspired patterns elicit more interpretable and useful percepts. 

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5. Neuromorphic vision and tactile fusion for upper limb prosthesis control

   https://doi.org/10.1109/NER.2019.8716890  

   Hays, Mark ; Osborn, Luke ; Ghosh, Rohan ; Iskarous, Mark ; Hunt, Christopher ; Thakor, Nitish V. ( March 2019 , IEEE Transactions on Biomedical Circuits and Systems)

   null (Ed.)

   A major issue with upper limb prostheses is the disconnect between sensory information perceived by the user and the information perceived by the prosthesis. Advances in prosthetic technology introduced tactile information for monitoring grasping activity, but visual information, a vital component in the human sensory system, is still not fully utilized as a form of feedback to the prosthesis. For able-bodied individuals, many of the decisions for grasping or manipulating an object, such as hand orientation and aperture, are made based on visual information before contact with the object. We show that inclusion of neuromorphic visual information, combined with tactile feedback, improves the ability and efficiency of both able-bodied and amputee subjects to pick up and manipulate everyday objects.We discovered that combining both visual and tactile information in a real-time closed loop feedback strategy generally decreased the completion time of a task involving picking up and manipulating objects compared to using a single modality for feedback. While the full benefit of the combined feedback was partially obscured by experimental inaccuracies of the visual classification system, we demonstrate that this fusion of neuromorphic signals from visual and tactile sensors can provide valuable feedback to a prosthetic arm for enhancing real-time function and usability. 

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