The number of people who lose a limb is over 1 million every year throughout the world and is most commonly due to an accident, disease, or from birth. Current prosthetic development focuses on the mobility aspect, with little attention given to the material used in the artificial joint, which must be successful over the long term for the overall success of the prosthetic limb. The metals traditionally employed in prosthetics are Cobalt-Chromium-Molybdenum (Co-Cr-Mo) and Titanium alloy (Ti-6Al-4V), polymers are Ultra-High Molecular Weight Polyethylene (UHMWPE) and Polyether Ether Ketone (PEEK), and ceramics are Alumina$\left(\text{Al}_{2} \mathrm{O}_{3}\right)$and Zirconia$\left(\text{ZrO}_{2}\right)$. Five composite samples offering varied proportions of constituent materials were fabricated and tested in this research. Statistical validation using one-way ANOVA and p-values ($p<0.05$) confirmed that the observed differences among samples were significant. Sample A (60% metal, 30% ceramic, 10% polymer) exhibited the highest hardness (96 HV) and tensile strength (195 MPa) with lower cytocompatibility (85%). Sample E (20% metal, 60% ceramic, 20% polymer) showed the highest cell viability and adhesion (96% and 92%, respectively) but lower tensile strength (150 MPa). Sample C (40% metal, 40% ceramic, 20% polymer) had the best overall performance:$88 \text{HV}, 176 \text{MPa}$, bioactivity index of 99, and 91% viable cells. Results confirmed that material balance is the core factor in improving both structural integrity and biological response. While accelerated fatigue, cyclic loading, and long-term wear tests under simulated joint conditions remain a limitation of this study, they are recommended for future validation. Findings indicate that composite design should be customized to specific prosthetic requirements. This study forms a critical input in the development of advanced load-bearing artificial joints with enhanced performance and reduced rejection risk.

This paper compares Sliding Mode Control (SMC) with a properly adjusted Proportional-Integral (PI) controller for voltage and frequency regulation of a gridconnected microgrid under three different types of disturbances: a step change in load (1.5 s), a short duration voltage sag (2.0-2.3 s), and fluctuation in PV power generation (3.0 s). The battery energy storage system (BESS) injects fast active power support to the grid system. It is found that SMC keeps the bus voltage very close to its nominal value- 11 kV during the fault with much less oscillation compared to PI, maintaining tight regulation over the entire horizon where PI has large steady-state ripple. SMC makes frequency deviation possible to$<\approx 2 \text{Hz}$during load increase and brings back 50 Hz quickly, whereas PI deviation goes up to$\approx \mathbf{6 H z}$and takes time to settle. At a change of PV, the BESS gives out power up to about$\pm \mathbf{3 5 0 ~ k W}$. This will be able to counter a drop in PV from approximately 450 kW to approximately 150 kW. Since SMC has the ability to keep frequency and voltage very close to their nominal values for quite some time, it is thus able to sustain such big falls in power for quite some time. In general, comparing it with PI, SMC possesses superior disturbance rejection and robustness, achievable recovery that is faster plus lower variance, hence stability improvement of both voltage and frequency while operating a microgrid.

The ILRABJ model developed biomechanics, machine learning, and biomimetic control systems to monitor the lower limb non-invasively. Its goal is to predict, evaluate, and enhance limb performance. Conditions like locomotor syndrome significantly affect quality of life and increase healthcare costs. The STBLS test faces issues such as human bias and requires specialized equipment and supervision. ILRABJ uses depth cameras to gather skeletal joint data, focusing on hip and knee movements during squats and single-leg stands. It analyses balance, joint angles, and shakiness through machine learning techniques like RF regressors and feedforward neural networks. ILRABJ model mimics muscle dynamics to improve joint torque and stiffness calculations through its biomimetic leg dynamic model, which uses PAM. It achieves strong accuracy in assessing lower limb functions, with performance metrics exceeding 93%.

Humanoid systems must manage model uncertainty and sensor noise safely, smoothly, and accurately. Conventional sliding-mode control is robust, but high-frequency switching causes chattering, force spikes upon contact onset, and actuator stress. Impedance and admittance control improve comfort but challenge with modeling errors and tracking precision. Higher-order sliding modes and extensive filtering can reduce chattering, but with increased complexity and computation. This study provides a Hybrid Sliding-Mode Controller that reduces chattering using forceaware sliding and boundary layer saturation. It has an admittance/force estimator for stable contact transitions and a fuzzy gain scheduler that only increases switching gain when motion and force errors grow. The proposed approach provides the robustness of conventional sliding control while offering direct contact force control. Lyapunov-based analysis in an adjustable region confirms ultimate boundedness. In contact simulations, the proposed controller reduces (Root Mean Square) RMS tracking error by 88.0%, steady-state mean absolute error by 89.7%, and total variation of the control signal, which indicates chattering, by 86.1%. Results suggest improved human-robot collaboration with smoother interaction, reduced force overshoot, and realistic real-time applications.

This paper proposes a simulation model that enhances monitoring and warning systems, providing significant assistance to persons with epilepsy and their careers. Smartphones may be categorized as Internet of Things (IoT) devices that fulfill data processing and communication functionalities across both private and public domains. The next iteration of smartphones incorporates sensor systems. The analysis of motion shown by mobile objects may be ascertained via the use of accelerometers and other integrated sensors inside these components. Accelerometers has the capability to identify many physiological occurrences, such as epileptic convulsions and unexpected assaults. A significant proportion of patients diagnosed with epilepsy have epileptic episodes, resulting in impaired equilibrium and potential harm or physical impairment. The data obtained from the acceleration sensor included in the smartphone was used in both the construction and subsequent assessment of the Epilepsy alert system. The data was obtained by collecting measurements from three acceleration sensors that were implanted into mobile phones, using the MATLAB program. A sample size of 20 participants was chosen to serve as representatives of persons diagnosed with epilepsy across 13 unique settings. Furthermore, a total of 30 people were chosen as datasets, whilst 6 individuals were allocated for the purpose of testing the system. The MATLAB programming language was used to compute speeds and distances as a means of evaluating and presenting the outcomes of data processing for smartphones. The use of automated correlation analysis between the movement of the patient and the reference data was employed as a means to determine if the observed movement exhibits the qualities of normalcy or abnormality. The described technique for detecting epilepsy had a matching rate of up to 85 percent.

This paper proposes an Internet of Medical Things (IoMT) Seizure Detection Algorithm that uses smartphone acceleration sensors to detect early seizures. The propose algorithm used based on MATLAB Mobile, which seamlessly accesses MATLAB capabilities and uses MATLAB Drive for cloud-based data storage and analysis. Time-domain and frequency-domain analysis turn acceleration impulses into meaningful feature vectors. Then, machine learning classifiers like SVMs or deep learning models are used to train the system on a seizure and non-seizure dataset. The algorithm is calibrated to distinguish s [email protected] eizure occurrences from regular activity. The IoMT technique uses real-time streaming to send sensor data to the cloud-based MATLAB Drive for continuous monitoring. The MATLAB Mobile app allows smartphone users to remotely monitor seizure activity using the seizure detection algorithm. The proposed IoMT Seizure Detection Algorithm is tested utilizing a broad dataset of seizure episodes and physical activities. Accuracy, sensitivity, specificity, and the ROC curve are evaluated. The algorithm’s robustness and effectiveness in detecting seizure occurrences highlight its potential as a noninvasive, cost-effective early seizure detection system. MATLAB Mobile and MATLAB Drive integration shows that IoMT-based healthcare solutions implemented in real-world circumstances, improving patient care and medical insights. The auto-correlation between the patient’s motion and the [] data helped clinicians decide if the patient’s motion was typical or not. Up to 85% consistency was found while using our proposed method for detecting epilepsy.

Every designer aspires to produce designs that are superior to those of their rivals in terms of quality, speed, or efficiency. Using an ANFIS (Adaptive Neural Inference System) controller and a proportional, integrated, derived (2DO-PID) 2-degree of freedom controller, this study suggests a high-performance design for a 6-DOF manipulator. Finding the best value for the controller settings that smoothly regulate the robot's movements to the desired aim is the primary objective of this exercise. The first step in the design process is to naturally determine the best values for the parameters of a traditional PID controller. The creation of a high-resolution 2DOF-PID controller is the next phase. It performs better than the conventional correct order using a mysterious physics control technique. The parameters of the 2DOF-PID controller are estimated based on the undeniably significant nature of the control effect. The final stage in achieving the high performance of the control system under consideration is the hybrid 2DOF-PID and ANFIS controller, which uses the prior output as a predictive point. The use of both modern and vintage consoles. Six-degree-of-freedom elbow curves are supported. Because the manipulator's trajectory exceeded the settling time and affected the movement, it was possible to minimize. MATLAB 2021b and Robotics Toolbox 9 were used to design and simulate the entire remote-control system. The controller's optimal design is built using a 3-dimensional model of a 6-DOF manipulator created with MATLAB/virtual Simulink's reality (VR) technology. MATLAB generates the manipulator instructions, which are then used to generate a real trajectory with a virtual reality model.

An electrical drive consists of a motor powered by electricity, a power, with an energy-transmitting shaft. In modern electrically driven systems, power electronics converters serve as power controllers. The two main types of electric drives are AC drives and DC actuators. This study presents highly effective design and modeling techniques for individually stimulated DC motor speed control. To create a controller that regulates speed for a Separately Excited DC (SEDC) motor, a converter called a chopper circuit can be used. The controller sends a signal to the chopper discharge circuit, which then adjusts the voltage sent to the motor's armature to drive the chopper at the desired speed. The current controller and speed controller are two different types of control loops. A proportional-integral controller is used. Fast control has been rendered possible with this controller by doing away with the delay. A DC motor that is independently excited is made, and a current & speed regulator is constructed to enable a steady state plus high-speed control of the DC motor. The model is analyzed and tested in MATLAB (Simulink) under various speed and torque situations. Satisfactory outcomes are obtained, demonstrating the chopper approach's ability to regulate the rotation speed generated by an SEDC motor.