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Inductive proximity sensors are relevant in position-sensing applications in many industries but, in order to be used in harsh industrial environments, they need to be immune to electromagnetic interference (EMI). The use of conventional filters to mitigate these perturbations often compromises signal bandwidth, ranging from 100 Hz to 1.6 kHz. We have exploited recent advances in the field of artificial intelligence (AI) to study the ability of neural networks (NNs) to automatically filter out EMI features. This study offers an analysis and comparison of possible NN models (a 1D convolutional NN, a recurrent NN, and a hybrid convolutional and recurrent approach) for denoising EMI-perturbed signals and proposes a final model, which is based on gated recurrent unit (GRU) layers. This network is compressed and optimised to meet memory requirements, so that in future developments it could be implemented in application-specific integrated circuits (ASICs) for inductive sensors. The final RNN manages to reduce noise by 70% (MSEred) while occupying 2 KB of memory. [ABSTRACT FROM AUTHOR]

Biological motions of native muscle tissues rely on the nervous system to interface movement with the surrounding environment. The neural innervation of muscles, crucial for regulating movement, is the fundamental infrastructure for swiftly responding to changes in body tissue requirements. This study introduces a bioelectronic neuromuscular robot integrated with the motor nervous system through electrical synapses to evoke cardiac muscle activities and steer robotic motion. Serving as an artificial brain and wirelessly regulating selective neural activation to initiate robot fin motion, a wireless frequency multiplexing bioelectronic device is used to control the robot. Frequency multiplexing bioelectronics enables the control of the robot locomotion speed and direction by modulating the flapping of the robot fins through the wireless motor innervation of cardiac muscles. The robots demonstrated an average locomotion speed of ~0.52 ± 0.22 millimeters per second, fin-flapping frequency up to 2.0 hertz, and turning locomotion path curvature of ~0.11 ± 0.04 radians per millimeter. These systems will contribute to the expansion of biohybrid machines into the brain-to-motor frontier for developing autonomous biohybrid systems capable of advanced adaptive motor control and learning. Editor's summary: Typically, biohybrid machines built from cardiac muscle cells have relied on the involuntary, contractile actuation of the cells to continuously propel the robot. However, without the nervous system, this rhythmic pumping action could not be adjusted or controlled. To imbue control, Tetsuka et al. developed a biohybrid swimmer with a neurocardiac muscle junction. Acting as an artificial brain, a frequency multiplexing wireless system was used to stimulate the motor neurons, which in turn activated the cardiac muscle cells. The robot's speed could be controlled by modulating the frequency and pulse duration of the electrical signal, and direction was altered by transmitting signals to the left and right fins independently. —Melisa Yashinski [ABSTRACT FROM AUTHOR]

The integration of flexible and stretchable electronics into biohybrid soft robotics can spur the development of new approaches for fabricating biohybrid soft machines, thus enabling a wide variety of innovative applications. Inspired by flexible and stretchable wireless‐based bioelectronic devices, untethered biohybrid soft robots are developed that can execute swimming motions, which are remotely controllable by the wireless transmission of electrical power into a cell simulator. To this end, wirelessly‐powered, stretchable, and lightweight cell stimulators are designed to be integrated into muscle bodies without impeding the robots' underwater swimming abilities. The cell stimulators function by generating controlled monophasic pulses of up to ≈9 V in biological environments. By differentiating induced pluripotent stem cell‐derived cardiomyocytes directly on the cell stimulators using an accordion‐inspired, three‐dimensional (3D) printing construct, the native myofiber architecture are replicated with comparable robustness and enhanced contractibility. Wirelessly modulated electrical frequencies enables the control of speed and direction of the biohybrid soft robots. A maximum locomotion speed of ≈580 µm s−1 is achieved in robots possessing a large body size by adjusting the pacing frequency. This innovative approach will provide a platform for building untethered and biohybrid systems for various biomedical applications. [ABSTRACT FROM AUTHOR]

A Customized wound patch for Advanced tissue Regeneration with Electric field (CARE), featuring an autonomous robot arm printing system guided by a computer vision-enabled guidance system for fast image recognition is introduced. CARE addresses the growing demand for flexible, stretchable, and wireless adhesive bioelectronics tailored for electrotherapy, which is suitable for rapid adaptation to individual patients and practical implementation in a comfortable design. The visual guidance system integrating a 6-axis robot arm enables scans from multiple angles to provide a 3D map of complex and curved wounds. The size of electrodes and the geometries of power-receiving coil are essential components of the CARE and are determined by a MATLAB simulation, ensuring efficient wireless power transfer. Three heterogeneous inks possessing different rheological behaviors can be extruded and printed sequentially on the flexible substrates, supporting fast manufacturing of large customized bioelectronic patches. CARE can stimulate wounds up to 10 mm in depth with an electric field strength of 88.8 mV mm -1 . In vitro studies reveal the ability to accelerate cell migration by a factor of 1.6 and 1.9 for human dermal fibroblasts and human umbilical vein endothelial cells, respectively. This study highlights the potential of CARE as a clinical wound therapy method to accelerate healing., (© 2024 Wiley‐VCH GmbH.)