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Body balancing is a complex task that includes the coordination of muscles, tendons, bones, ears, eyes, and the brain. Imbalance or disequilibrium is the inability to maintain the center of gravity. Perpetuating body balance plays an important role in preventing us from falling or swaying. Biomechanical tests and video analysis can be performed to analyze body imbalance. The musculoskeletal system is one of the fundamental systems by which our balance or equilibrium is sustained and our upright posture is maintained. Electromyogram (EMG) and ground reaction force (GRF) monitoring can be utilized in cases where a rapid response to body imbalance is a necessity. Body balance also depends on visual stimuli that can be either real or virtual. Researchers have used virtual reality (VR) to predict motion sickness and analyze heart rate variability, as well as in rehabilitation. VR can also be used to induce body imbalance in a controlled way. In this research, body imbalance was induced in a controlled way by playing an Oculus game and, simultaneously, EMG and GRF were recorded. Features were extracted from the EMG and were then fed to a machine learning algorithm. Several machine learning algorithms were tested and upon 10-fold cross-validation; a minimum accuracy of 71% and maximum accuracy of 98% were achieved by Gaussian Naïve Bayes and Gradient Boosting classifiers, respectively, in the classification of imbalance and its intensities. This research can be incorporated into various rehabilitative and therapeutic systems. [ABSTRACT FROM AUTHOR]

Diagnosis of bruxism is challenging because not all contractions of the masticatory muscles can be classified as bruxism. Conventional methods for sleep bruxism detection vary in effectiveness. Some provide objective data through EMG, ECG, or EEG; others, such as dental implants, are less accessible for daily practice. These methods have targeted the masseter as the key muscle for bruxism detection. However, it is important to consider that the temporalis muscle is also active during bruxism among masticatory muscles. Moreover, studies have predominantly examined sleep bruxism in the supine position, but other anatomical positions are also associated with sleep. In this research, we have collected EMG data to detect the maximum voluntary contraction of the temporalis and masseter muscles in three primary anatomical positions associated with sleep, i.e., supine and left and right lateral recumbent positions. A total of 10 time domain features were extracted, and six machine learning classifiers were compared, with random forest outperforming others. The models achieved better accuracies in the detection of sleep bruxism with the temporalis muscle. An accuracy of 93.33% was specifically found for the left lateral recumbent position among the specified anatomical positions. These results indicate a promising direction of machine learning in clinical applications, facilitating enhanced diagnosis and management of sleep bruxism. [ABSTRACT FROM AUTHOR]

A memristor is a fundamental electronic device that operates like a biological synapse and is considered as the solution of classical von Neumann computers. Here, a fully printed and flexible memristor is fabricated by depositing a thin film of metal–non-metal (chromium-nitrogen)-doped titanium dioxide (TiO2). The resulting device exhibited enhanced performance with self-rectifying and forming free bipolar switching behavior. Doping was performed to bring stability in the performance of the memristor by controlling the defects and impurity levels. The forming free memristor exhibited characteristic behavior of bipolar resistive switching with a high on/off ratio (2.5 × 103), high endurance (500 cycles), long retention time (5 × 103 s) and low operating voltage (±1 V). Doping the thin film of TiO2 with metal–non-metal had a significant effect on the switching properties and conduction mechanism as it directly affected the energy bandgap by lowering it from 3.2 eV to 2.76 eV. Doping enhanced the mobility of charge carriers and eased the process of filament formation by suppressing its randomness between electrodes under the applied electric field. Furthermore, metal–non-metal-doped TiO2 thin film exhibited less switching current and improved non-linearity by controlling the surface defects. [ABSTRACT FROM AUTHOR]

The resistive switching effect of various materials has been investigated because of their promising advantages such as high scalability, low cost, simple structure and less power consumption of such memory devices. In this study, the resistive switching effect in nanocomposites of zinc oxide nanowires (ZnO NWs) and zinc stannate nanocubes (ZnSnO3 NCs) has been explored. The device was fabricated on a flexible poly(ethylene terephthalate) substrate on which highly conductive (0.3 Ω cm) patterns of silver (Ag) were deposited as bottom electrodes using a reverse offset printing technique. Vertically aligned and highly dense ZnO NWs were grown on the Ag patterns using hydrothermal synthesis followed by spraying of ZnSnO3 NCs using an electrohydrodynamic atomization (EHDA) technique. Finally, the top Ag electrode was patterned using an EHD patterning technique to complete the fabrication of the device. The fabricated device exhibited bipolar and rewritable characteristics, and had a nonvolatile resistive switching memory with an off/on ratio of 5.8 × 102 and a retention time of 104 s at a small operating voltage (1.2 V). The electrical results obtained with the Ag–ZnO/ZnSnO3–Ag memory device were remarkable and they showed significant repeatability. The significantly stable, highly durable and promising results obtained with the ZnO NWs–ZnSnO3 NCs heterostructure make this semiconductive nanocomposite a potential candidate for use in future flexible memory devices. [ABSTRACT FROM AUTHOR]

Owing to the increasing interest in the nonvolatile memory devices, resistive switching based on hybrid nanocomposite of a 2D material, molybdenum disulphide (MoS2) and polyvinyl alcohol (PVA) is explored in this work. As a proof of concept, we have demonstrated the fabrication of a memory device with the configuration of PET/Ag/MoS2-PVA/Ag via an all printed, hybrid, and state of the art fabrication approach. Bottom Ag electrodes, active layer of hybrid MoS2-PVA nanocomposite and top Ag electrode are deposited by reverse offset, electrohydrodynamic (EHD) atomization and electrohydrodynamic (EHD) patterning respectively. The fabricated device displayed characteristic bistable, nonvolatile and rewritable resistive switching behavior at a low operating voltage. A decent off/on ratio, high retention time, and large endurance of 1.28 × 102, 105 sec and 1000 voltage sweeps were recorded respectively. Double logarithmic curve satisfy the trap controlled space charge limited current (TCSCLC) model in high resistance state (HRS) and ohmic model in low resistance state (LRS). Bendability test at various bending diameters (50-2 mm) for 1500 cycles was carried out to show the mechanical robustness of fabricated device. [ABSTRACT FROM AUTHOR]

Owing to the increasing interest in the nonvolatile memory devices, resistive switching based on hybrid nanocomposite of a 2D material, molybdenum disulphide (MoS 2 ) and polyvinyl alcohol (PVA) is explored in this work. As a proof of concept, we have demonstrated the fabrication of a memory device with the configuration of PET/Ag/MoS 2 -PVA/Ag via an all printed, hybrid, and state of the art fabrication approach. Bottom Ag electrodes, active layer of hybrid MoS 2 -PVA nanocomposite and top Ag electrode are deposited by reverse offset, electrohydrodynamic (EHD) atomization and electrohydrodynamic (EHD) patterning respectively. The fabricated device displayed characteristic bistable, nonvolatile and rewritable resistive switching behavior at a low operating voltage. A decent off/on ratio, high retention time, and large endurance of 1.28 × 10 2 , 10 5  sec and 1000 voltage sweeps were recorded respectively. Double logarithmic curve satisfy the trap controlled space charge limited current (TCSCLC) model in high resistance state (HRS) and ohmic model in low resistance state (LRS). Bendability test at various bending diameters (50-2 mm) for 1500 cycles was carried out to show the mechanical robustness of fabricated device.