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1. Biohybrid Robotic Hand to Investigate Tactile Encoding and Sensorimotor Integration

   https://doi.org/10.3390/biomimetics9020078  

   Ades, Craig; Abd, Moaed A; Hutchinson, Douglas T; Tognoli, Emmanuelle; Du, E; Wei, Jianning; Engeberg, Erik D (February 2024, Biomimetics)

   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. 

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2. Teaching Cameras to Feel: Estimating Tactile Physical Properties of Surfaces from Images

   https://doi.org/10.1007/978-3-030-58583-9_1  

   Purri, Matthew and (November 2020, European Conference on Computer Vision)

   null (Ed.)

   The connection between visual input and tactile sensing is critical for object manipulation tasks such as grasping and pushing. In this work, we introduce the challenging task of estimating a set of tactile physical properties from visual information. We aim to build a model that learns the complex mapping between visual information and tactile physical properties. We construct a first of its kind image-tactile dataset with over 400 multiview image sequences and the corresponding tactile properties. A total of fifteen tactile physical properties across categories including friction, compliance, adhesion, texture, and thermal conductance are measured and then estimated by our models. We develop a cross-modal framework comprised of an adversarial objective and a novel visuo-tactile joint classification loss. Additionally, we introduce a neural architecture search framework capable of selecting optimal combinations of viewing angles for estimating a given physical property. 

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3. RoboGraphics: Dynamic Tactile Graphics Powered by Mobile Robots

   https://doi.org/10.1145/3308561.3353804  

   Guinness, Darren; Muehlbradt, Annika; Szafir, Daniel; Kane, Shaun K. (October 2019, ASSETS '19: The 21st International ACM SIGACCESS Conference on Computers and Accessibility)

   Tactile graphics are a common way to present information to people with vision impairments. Tactile graphics can be used to explore a broad range of static visual content but aren’t well suited to representing animation or interactivity. We introduce a new approach to creating dynamic tactile graphics that combines a touch screen tablet, static tactile overlays, and small mobile robots. We introduce a prototype system called RoboGraphics and several proof-of-concept applications. We evaluated our prototype with seven participants with varying levels of vision, comparing the RoboGraphics approach to a flat screen, audio-tactile interface. Our results show that dynamic tactile graphics can help visually impaired participants explore data quickly and accurately. 

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4. Identifying Benign and Malignant Breast Tumor Using Vibro-acoustic Tactile Imaging Sensor

   https://doi.org/10.1109/SENSORS52175.2022.9967083  

   Rahman, Nazia; Won, Chang-hee (October 2022, IEEE Sensors Conference)

   Tactile imaging sensor determines the tumor's mechanical properties such as size, depth, and Young's modulus based on the principle of total internal reflection of light. To improve the classifying accuracy of the Tactile imaging sensor, we introduce ultrasound signals and estimate the difference in the tumor tactile images. A developed vibro-acoustic tactile imaging sensor was used to classify benign and malignant tumors. We test the developed system on breast tumor phantoms. These vibrated tactile images are analyzed to improve the overall performance of tumor detection. 

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5. Engineered IPMC sensors: modeling, characterization, and application towards wearable postural-tactile measurement

   https://doi.org/10.1088/1361-665X/ad142b  

   Nagel, William S; Fakharian, Omid; Aureli, Matteo; Leang, Kam K (December 2023, Smart Materials and Structures)

   Abstract This paper focuses on the modeling and development of engineered ionic polymer-metal composite (eIPMC) sensors for applications such as postural and tactile measurement in mechatronics/robotics-assisted finger rehabilitation therapy. Specifically, to tailor the sensitivity of the device, eIPMCs, fabricated using a polymer-surface abrading technique, are utilized as the sensing element. An enhanced chemoelectromechanical model is developed that captures the effect of the abrading process on the multiphysics sensing behavior under different loading conditions. The fabricated sensors are characterized using scanning electron microscopy imaging and cyclic voltammetry and chronoamperometry. Results show significant improvement in the electrochemical properties, including charge storage, double layer capacitance, and surface conductance, compared to the control samples. Finally, prototype postural-tactile finger sensors composed of different eIPMC variants are created and their performance validated under postural and tactile experiments. The tailored eIPMC sensors show increased open-circuit voltage response compared to control IPMCs, with 7.7- and 4.7-times larger peak-to-peak bending response under postural changes, as well as a 3.2-times more sensitive response under compression during tactile loading, demonstrating the feasibility of eIPMC sensors. 

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