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1. Multifunctional 3D printed BaTiO3 platonic solids packaging for implantable microdevices

   Albert Kim Sayemul Islam, Sumnoon Ahmed ( June 2022 , Hilton Head Workshop 2022: A Solid-State Sensors, Actuators and Microsystems Workshop)

   We present a multifunctional packaging technique for implantable microdevices. The packaging is composed of 3D printed bulk piezoelectric barium titanate (BaTiO3) ceramic with unique geometry shaped (i.e., regular convex polyhedrons; Platonic solid). The BaTiO3 ceramic provides not only a seamless packaging for essential electronics but also a power source for those electronics through the conversion of incoming ultrasound. Ultrasound has been an attractive powering source for many implantable microdevices [1]. However, most ultrasonic receivers are rectangular or disc, not in ideal form factors; ultrasound is often deflected within the path, and the miniature implants might shift and rotate, resulting in an angular misalignment. Tailoring a three-dimensional polyhedral architecture (i.e., Platonic solid) for an mm-scale ultrasonic receiver can dramatically enhance its omnidirectionality. Utilizing the 3D printing technique, we devised a dodecahedron shaped BaTiO3 ceramic with the center void space for electronics embodiment. As a proof of concept, an LC (inductor-capacitor pair) resonator is implemented as a representative implantable microdevice [2, 3]. The LC resonator has been utilized in physiological sensing by employing either a capacitive or inductive sensor. These sensors are typically powered by inductive coupling or batteries which can be impracticable when the implant is placed deep inside the tissues.more »Instead, the ultrasound-mediated interrogation scheme can compensate for these inadequacies. When ultrasound impacts the dodecahedron BaTiO3 ultrasonic receiver, it energizes the embedded LC oscillator, generating resonance radio frequency (RF) waves, which can be detected by an external antenna (Fig. 1).« less
2. 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)

   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.
3. Clinical viability of magnetic bead implants in muscle

   https://doi.org/10.3389/fbioe.2022.1010276  

   Taylor, Cameron R. ; Clark, William H. ; Clarrissimeaux, Ellen G. ; Yeon, Seong Ho ; Carty, Matthew J. ; Lipsitz, Stuart R. ; Bronson, Roderick T. ; Roberts, Thomas J. ; Herr, Hugh M. ( October 2022 , Frontiers in Bioengineering and Biotechnology)

   Human movement is accomplished through muscle contraction, yet there does not exist a portable system capable of monitoring muscle length changes in real time. To address this limitation, we previously introduced magnetomicrometry, a minimally-invasive tracking technique comprising two implanted magnetic beads in muscle and a magnetic field sensor array positioned on the body’s surface adjacent the implanted beads. The implant system comprises a pair of spherical magnetic beads, each with a first coating of nickel-copper-nickel and an outer coating of Parylene C. In parallel work, we demonstrate submillimeter accuracy of magnetic bead tracking for muscle contractions in an untethered freely-roaming avian model. Here, we address the clinical viability of magnetomicrometry. Using a specialized device to insert magnetic beads into muscle in avian and lagomorph models, we collect data to assess gait metrics, bead migration, and bead biocompatibility. For these animal models, we find no gait differences post-versus pre-implantation, and bead migration towards one another within muscle does not occur for initial bead separation distances greater than 3 cm. Further, using extensive biocompatibility testing, the implants are shown to be non-irritant, non-cytotoxic, non-allergenic, and non-irritating. Our cumulative results lend support for the viability of these magnetic bead implants for implantation in humanmore »muscle. We thus anticipate their imminent use in human-machine interfaces, such as in control of prostheses and exoskeletons and in closed-loop neuroprosthetics to aid recovery from neurological disorders.« less
4. A Smart Chair Sitting Posture Recognition System using Flex Sensors and FPGA Implemented Artificial Neural Network

   https://doi.org/10.1109/JSEN.2020.2980207  

   Hu, Qisong ; Tang, Xiaochen ; Tang, Wei ( January 2020 , IEEE sensors journal)

   Sitting is the most common status of modern human beings. Some sitting postures may bring health issues. To prevent the harm from bad sitting postures, a local sitting posture recognition system is desired with low power consumption and low computing overhead. The system should also provide good user experience with accuracy and privacy. This paper reports a novel posture recognition system on an office chair that can categorize seven different health-related sitting postures. The system uses six flex sensors, an Analog to Digital Converter (ADC) board and a Machine Learning algorithm of a two-layer Artificial Neural Network (ANN) implemented on a Spartan-6 Field Programmable Gate Array (FPGA). The system achieves 97.78% accuracy with a floating-point evaluation and 97.43% accuracy with the 9-bit fixed-point implementation. The ADC control logic and the ANN are constructed with a maximum propagation delay of 8.714 ns. The dynamic power consumption is 7.35 mW when the sampling rate is 5 Sample/second with the clock frequency of 5 MHz.
5. Electromechanical Characterization of 3D Printable Conductive Elastomer for Soft Robotics

   https://doi.org/10.1109/RoboSoft48309.2020.9116027  

   Kim, Suhan ; Kim, Sukjun ; Majditehran, Houriyeh ; Patel, Dinesh K. ; Majidi, Carmel ; Bergbreiter, Sarah ( May 2020 , IEEE International Conference on Soft Robotics (RoboSoft))

   Soft, stretchable sensors, such as artificial skins or tactile sensors, are attractive for numerous soft robotic applications due to the low material compliance. Conductive polymers are a necessary component of many soft sensors, and this work presents the electromechanical characterization of 3D-printable conductive polymer composites. Dog-bone shaped samples were 3D printed using a digital light processing (DLP)-based 3D printer for characterization. The 3D printable resin consists of monomer, crosslinker, conductive nano-filler, and a photo-initiator. The characterization was performed in two tracks. First, the effect of two different crosslinkers was investigated with different compositions and second, the effect of concentration of conductive nano-fillers was explored. Crosslinkers were chosen by referring to previous studies, and carbon nanotubes (CNTs) were utilized as conductive nano-fillers. The samples were 3D printed and characterized using an electromechanical test setup. To demonstrate utility for 3D printed soft robotics, a capacitance-based joystick sensor composed of both conductive and non-conductive resins was 3D printed.