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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.

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.

In this paper, we propose an improved clustering algorithm for wireless sensor networks (WSNs) that aims to increase network lifetime and efficiency. We introduce an enhanced fuzzy spider monkey optimization technique and a hidden Markov model-based clustering algorithm for selecting cluster heads. Our approach considers factors such as network cluster head energy, cluster head density, and cluster head position. We also enhance the energy-efficient routing strategy for connecting cluster heads to the base station. Additionally, we introduce a polling control method to improve network performance while maintaining energy efficiency during steady transmission periods. Simulation results demonstrate a 1.2% improvement in network performance using our proposed model.

Employing a combination of Polyethylene terephthalate (PET) thermoforming and 3D-printed cylindrical patterns, we carefully engineer a linear resistive temperature sensor. This intricate process involves initial PET thermoforming, yielding a hollow cylindrical chamber. This chamber is then precisely infused with a composite fluid of graphite and water glue. Ensuring electrical connectivity, both ends are affixed with metal wires and securely sealed using a hot gun. This cost-effective, versatile sensor adeptly gauges temperature shifts by assessing composite fluid resistance alterations. Its PET outer surface grants immunity to water and solubility concerns, enabling application in aquatic and aerial settings without extra encapsulation. Rigorous testing reveals the sensor’s linearity and stability within a 10 °C to 60 °C range, whether submerged or airborne. Beyond 65 °C, plastic deformation arises. To mitigate hysteresis, a 58 °C operational limit is recommended. Examining fluidic composite width and length effects, we ascertain a 12 Ω/°C sensitivity for these linear sensors, a hallmark of their precision. Impressive response and recovery times of 4 and 8 s, respectively, highlight their efficiency. These findings endorse thermoforming’s potential for fabricating advanced temperature sensors. This cost-effective approach’s adaptability underscores its viability for diverse applications.

Vision relies on precise eye movements controlled by six extraocular muscles (EOMs). Training kits and functional eye models are crucial for ophthalmological studies. The study investigates the potential of shape memory material (SMM) in ophthalmology, focusing on eye model mechanics and four thermally actuated smart EOMs. SMM was chosen over other actuators because of its high-power density (36 W/kg), high actuation forces (100 gf), and high displacements (200%). Finite element method (FEM) simulation on a 3D eye model was performed to analyze the thermo-mechanical properties of smart muscle, revealing the shape memory effect and super elasticity which result in the four eye movements of elevation, depression, adduction, and abduction. FEM simulation, compared to the eye model prototype, yielded a 16 mm deformation in smart muscle with a thermal actuation of 313 K (40 °C). FEM analysis, while combining smart muscle with the eye model, resulted in a 6.8517 mm deformation in the simulation. Prototype deformation was acquired as 7.262 mm. The successful actuation of the eye using thermally actuated smart muscles proves its potential for ophthalmology kits.

Two dimensional (2D) materials have offered unique electrical, chemical, mechanical and physical properties over the past decade owing to their ultrathin, flexible, and multilayer structure. These layered materials are being used in numerous electronic devices for various applications, and this review will specifically focus on the resistive random access memories (RRAMs) based on 2D materials and their nanocomposites. This study presents the device structures, conduction mechanisms, resistive switching properties, fabrication technologies, challenges and future aspects of 2D-materials-based RRAMs. Graphene, derivatives of graphene and MoS2 have been the major contributors among 2D materials for the application of RRAMs; however, other members of this family such as hBN, MoSe2, WS2 and WSe2 have also been inspected more recently as the functional materials of nonvolatile RRAM devices. Conduction in these devices is usually dominated by either the penetration of metallic ions or migration of intrinsic species. Most prominent advantages offered by RRAM devices based on 2D materials include fast switching speed (10 years), high electrical endurance (>108 voltage cycles) and extended mechanical robustness (500 bending cycles). Resistive switching properties of 2D materials have been further enhanced by blending them with metallic nanoparticles, organic polymers and inorganic semiconductors in various forms.

Soft robots have received an increasing attention due to their advantages of high flexibility and safety for human operators but the fabrication is a challenge. Recently, 3D printing has been used as a key technology to fabricate soft robots because of high quality and printing multiple materials at the same time. Functional soft materials are particularly well suited for soft robotics due to a wide range of stimulants and sensitive demonstration of large deformations, high motion complexities and varied multi-functionalities. This review comprises a detailed survey of 3D printing in soft robotics. The development of key 3D printing technologies and new materials along with composites for soft robotic applications is investigated. A brief summary of 3D-printed soft devices suitable for medical to industrial applications is also included. The growing research on both 3D printing and soft robotics needs a summary of the major reported studies and the authors believe that this review article serves the purpose.

Rehabilitation in the form of locomotion assistance and gait training through robotic exoskeletons requires both precision and accuracy to achieve effective results. The essential challenge is to ensure robust tracking of the reference signal, i.e., of the gait or locomotion. This paper presents the design of model-based (MB) and model-free (MF) robust control strategies to achieve desired performance and robustness in terms of transient behavior and steady-state/tracking error, implementable to the locomotion assistance and gait training by exoskeletons. The dynamic responses of the exoskeleton system were investigated with both the control strategies. The study was carried out with a variety of reference signals and performance was evaluated to identify the best suited approach for rehabilitation exoskeletons. In case of the model-based control, a mathematical model of the system was developed using a bond graph modeling technique and a lead compensated H-infinity reference gain controller was designed to ensure robust tracking performance. In the model-free control strategy, however, the system function is approximated using radial basis function neural networks (RBFNNs) and an adaptive proportional-derivative RBFNN controller was designed to achieve the desired results with minimum tracking error. Both strategies make the system robust and stable. However, the MF control strategy is faster for all reference inputs as compared to the MB control strategy i.e., faster to approach the peak value and settle, and rapidly approaches the zero steady-state/tracking error. The rise time in the case of a sinusoidal input for model-free control is 0.4 s faster than the rise time in model-based control. Similarly, the settling time is 3.9 s faster in the case of model-free control, which is a prominent difference and can provide better rehabilitation results.

Soft bots have the expedient ability of adopting intricate postures and fitting in complex shapes compared to mechanical robots. This paper presents a unique in situ UV curing three-dimensional (3D) printed multi-material tri-legged soft bot with spider mimicked multi-step dynamic forward gait using commercial bio metal filament (BMF) as an actuator. The printed soft bot can produce controllable forward motion in response to external signals. The fundamental properties of BMF, including output force, contractions at different frequencies, initial loading rate, and displacement-rate are verified. The tri-pedal soft bot CAD model is designed inspired by spider’s legged structure and its locomotion is assessed by simulating strain and displacement using finite element analysis. A customized rotational multi-head 3D printing system assisted with multiple wavelength’s curing lasers is used for in situ fabrication of tri-pedal soft-bot using two flexible materials (epoxy and polyurethane) in three layered steps. The size of tri-pedal soft-bot is 80 mm in diameter and each pedal’s width and depth is 5 mm × 5 mm respectively. The maximum forward speed achieved is 2.7 mm s−1 @ 5 Hz with input voltage of 3 V and 250 mA on a smooth surface. The fabricated tri-pedal soft bot proved its power efficiency and controllable locomotion at three input signal frequencies (1, 2, 5 Hz). [ABSTRACT FROM AUTHOR]