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1. Texture Discrimination with a Soft Biomimetic Finger Using a Flexible Neuromorphic Tactile Sensor Array That Provides Sensory Feedback

   https://doi.org/10.1089/soro.2020.0016  

   Sankar, Sriramana; Balamurugan, Darshini; Brown, Alisa; Ding, Keqin; Xu, Xingyuan; Low, Jin Huat; Yeow, Chen Hua; Thakor, Nitish (September 2020, Soft Robotics)

   null (Ed.)

   The compliant nature of soft fingers allows for safe and dexterous manipulation of objects by humans in an unstructured environment. A soft prosthetic finger design with tactile sensing capabilities for texture discrimination and subsequent sensory stimulation has the potential to create a more natural experience for an amputee. In this work, a pneumatically actuated soft biomimetic finger is integrated with a textile neuromorphic tactile sensor array for a texture discrimination task. The tactile sensor outputs were converted into neuromorphic spike trains, which emulate the firing pattern of biological mechanoreceptors. Spike-based features from each taxel compressed the information and were then used as inputs for the support vector machine classifier to differentiate the textures. Our soft biomimetic finger with neuromorphic encoding was able to achieve an average overall classification accuracy of 99.57% over 16 independent parameters when tested on 13 standardized textured surfaces. The 16 parameters were the combination of 4 angles of flexion of the soft finger and 4 speeds of palpation. To aid in the perception of more natural objects and their manipulation, subjects were provided with transcutaneous electrical nerve stimulation to convey a subset of four textures with varied textural information. Three able-bodied subjects successfully distinguished two or three textures with the applied stimuli. This work paves the way for a more human-like prosthesis through a soft biomimetic finger with texture discrimination capabilities using neuromorphic techniques that provide sensory feedback; furthermore, texture feedback has the potential to enhance user experience when interacting with their surroundings. 

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

   https://doi.org/10.3390/s21134324  

   Abd, Moaed A.; Paul, Rudy; Aravelli, Aparna; Bai, Ou; Lagos, Leonel; Lin, Maohua; Engeberg, Erik D. (July 2021, Sensors)

   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. 

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3. Tri-modal thin-film flexible electronic skin to augment robotic grasping

   https://doi.org/10.1109/MEMSYS.2018.8346698  

   Yu, Caroline; Cavallari, Marco R.; Kymissis, Ioannis (January 2018, MEMS 2018)

   Robotic grasping is successful when a robot can sense and grasp an object without letting it slip. Beyond industrial robotic tasks, there are two main robotic grasping methods. The first is planning-based grasping where the object geometry is known beforehand and stable grasps are calculated using algorithms [1]. The second uses tactile feedback. Currently, there are capacitive sensors placed beneath stiff pads on the front of robotic fingers [2]. With post-execution grasp adjustment procedures to estimate grasp stability, a support vector machine classifier can distinguish stable and unstable grasps. The accuracy across the classes of tested objects is 81% [1]. We are proposing to improve the classifier's accuracy by wrapping flexible sensors around the robotic finger to gain information from the edges and sides of the finger. 

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4. Towards scalable soft e-skin: Flexible event-based tactile-sensors using wireless sensor elements embedded in soft elastomer

   https://doi.org/10.1109/BioRob49111.2020.9224353  

   Slepyan, Ariel; Thakor, Nitish (November 2020, IEEE BIOROB Conference)

   null (Ed.)

   Scalable, high-density electronic skins (e-skins) are a desirable goal of tactile sensing. However, a realization of this goal has been elusive due to the trade-off between spatial and temporal resolution that current tactile sensors suffer from. Additionally, as tactile sensing grids become large, wiring becomes unmanageable, and there is a need for a wireless approach. In this work, a scalable, event-based, passive tactilesensing system is proposed that is based on radio-frequency identification (RFID) technology. An RFID-based tactile sensing hand is developed with 19 pressure sensing taxels. The taxels are read wirelessly using a single ‘hand-shaped’ RFID antenna. Each RFID tag is transformed into a pressure sensor by disconnecting the RFID chip from its antenna and embedding the chip and antenna into soft elastomer with an air gap introduced between the RFID chip and its antenna. When a pressure event occurs, the RFID chip contacts its antenna and receives power and communicates with the RFID reader. Thus, the sensor is transformed into a biomimetic event-based sensor, whose response is activated only when used. Further, this work demonstrates the feasibility of constructing event-based, passive sensing grids that can be read wirelessly. Future tactile sensing e-skins can utilize this approach to become scalable and dense, while retaining high temporal resolution. Moreover, this approach can be applied beyond tactile sensing, for the development of scalable and high-density sensors of any modality. 

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5. 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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