Pre-defined motion command profiles enable precise positioning and dynamic control in mechanical and mechatronic systems, maximizing efficiency and reliability. Real-world applications introduce dynamic factors like mechanical compliance, friction, and external disturbances that significantly impact system performance. Understanding these influences improves motion control strategy accuracy, robustness, and system stability. This study emphasises the role of systematic and stochastic disturbances in improving motion control and accuracy. It introduces a structured method for evaluating system behavior under realistic operational conditions using advanced vibration analysis and spatio-temporal similarity measures. Using vibration indicators like amplitude, frequency content, phase relationships, crest factor, and acceleration root mean square (RMS) values, a comprehensive framework is created to quantify motion profile deviations. These indicators identify resonant frequencies, transient disturbances, and system inconsistencies, improving compensation strategies and predictive maintenance. A key contribution of this research is the comparison of quantification methods for motion precision and robustness integrating vibration diagnostics and advanced motion similarity analysis to improve motion control and assessment. Multi-faceted motion deviation characterization is achieved by combining displacement, velocity, and acceleration measurements with statistical and mathematical analysis. To assess motion consistency, spatio-temporal similarity measures like Dynamic Time Warping (DTW), Hausdorff distance, and discrete Fréchet distance capture spatial alignment and temporal progression. These measures allow a more nuanced evaluation of motion quality than traditional error metrics, especially in variable-speed dynamics, sampling rate inconsistencies, and complex motion patterns. Frequency-domain methods like FFT and wavelet transforms detect oscillatory behaviors to improve motion analysis reliability. The study uses spectral analysis and time-frequency domain techniques to detect motion inconsistencies that may cause mechanical wear, instability, or energy waste. Crest factor analysis and phase relationship assessment can also detect misalignment, structural resonance, and transient perturbations that conventional metrics miss.

Despite the digital era and the emergence of simplified production paradigms enabled by digital twin technology, traditional manufacturing processes like lathe machining are invaluable because of their ability to meet various and multiple needs reliably and flexibly. To combine these processes into smart monitoring and controlling systems, including tool wear detection and fault diagnosis, precise multi-axis force measurement is necessary. Single-axis sensors are insufficient to measure the entire dynamics of cutting interactions. A new idea based on strain gauge technology of a self-decoupled three-axis force sensor is proposed in this paper, which is to measure cutting forces and vibration in a lathe operation. During the process of cutting, the sensor is designed to recognize independently the onset of forces at three orthogonal axes to enhance real-time process monitoring capabilities. The Timoshenko beam theory is used to design the mechanical structure, where sensitivity could be improved, and with minimal crosstalk. Finite Element Analysis (FEA) simulations were conducted to evaluate the sensor’s performance, the stress distribution, the modal assessments, and the interference error. The interference error of 0.31 percent indicated by the results of the simulation is extremely low, indicating a successful decoupling of the force components. These encouraging simulation results indicate that there is a high possibility of applying the proposed sensor design to intelligent manufacturing systems. It presents an initial point of departure into the embodiment of sophisticated monitoring platforms for conventional machining processes, eliminating the discontinuity between old and new smart manufacturing systems.

Industry 4.0, in its search for improvements in processes and efficient products, has increasingly invested in the use and development of high-performance materials for its production lines. We have seen this, with the introduction of CFRP in the aeronautical industry, since these composite materials have reduced the weight of aircraft and improved their performance. For the construction of large structures, drilling processes are also necessary to fix the parts. However, this machining process can end up causing failures in the structure as a whole. These structural failures occur due to fragmentation, tearing, or detachment of the matrix fiber, significantly reducing the quality and reliability of the final equipment. In this scenario, it is important to preventively detect these intrinsic production failures that end up condemning the final parts. One of the indirect detection methods is through acoustic emission. This work presents a feasibility study focused on the application of data-driven methods for delamination detection and tool wear monitoring in composite machining. A setup for a helical interpolation end milling drilling process were performed under varying machining conditions, from mild to severe, on CFRP composite plates. Acoustic emission (AE) signals were acquired at each machining pass. The methodology involved selecting an optimal frequency band, to obtain information about the wear of the drilling tool, through RMS and power spectral density (PSD) analysis, followed by using correlation indices to characterize tool wear progression. The results demonstrate the potential of spectral and statistical techniques to support real-time monitoring and decision-making in advanced composite manufacturing.

In pharmaceutical and biomedical industries, manual handling of dangerous chemicals is a leading cause of hazardous exposure to chemicals, toxic burning, and chemical contamination. To counteract these risks, we proposed a gesture-controlled bionic hand system to mimic human finger movements for safe and contactless chemical handling. This innovation system uses an ESP32 micro controller decodes the hand gestures that are detected by the system using computer vision via an integrated camera. A PWM servo driver converts these movements to motor commands such that accurate movements of the fingers can be achieved. Teflon and other corrosion-proof materials are utilized in the 3D printing of the bionic hand in order to withstand corrosive conditions. This new, low-cost, and non-surgical approach replaces the EMG sensors, gives real-time control, and enhances industrial and laboratory process safety. The project is a major milestone in the application of robotics and AI for automation and risk reduction in dangerous environments.

Landmines remain a significant hazard in contemporary warfare and post-conflict areas, jeopardizing the safety of both civilians and military personnel. This work suggests “Defence Pal,” a cost-effective and portable robotic prototype for landmine detection and environmental monitoring. Its primary objective is to minimize human risk while improving detection speed and accuracy. The system consists of a wireless-controlled vehicle equipped with a metal detector, gas sensors, and obstacle avoidance features, enabling real-time terrain surveillance while ensuring operator safety. Built with components including a Flysky FS-i6 transmitter and receiver, the prototype was tested under hazardous conditions. It demonstrated reliable detection of buried metallic objects and dangerous gases such as methane and carbon dioxide. The autonomous response system halts the robot and activates visual and auditory alarms upon detecting threats. Our experiments achieved average detection accuracies of 83% for metallic objects and 87% for hazardous gases, validating their performance. These results highlight the practicality and effectiveness of Defence Pal compared to conventional manual detection methods. The results confirm that Defence Pal is a practical, scalable, and cost-effective alternative to traditional manual detection methods for improving landmine identification and environmental hazard monitoring. Therefore, the novelty of this work lies in a low-cost dual-sensing prototype that enables simultaneous detection of gas and metal, providing a practical alternative to conventional single-target, high-cost systems.

In the last few decades, several wearable devices have been designed to monitor respiration rate to capture pulmonary signals with higher accuracy and reduce patients’ discomfort during use. In this article, we present the design and implementation of a device for real-time monitoring of respiratory system movements. When breathing, the circumference of the abdomen and thorax changes; therefore, we used a Force Sensing Resistor (FSR) attached to the Printed Circuit Board (PCB) to measure this variation as the patient inhales and exhales. The mechanical strain this causes changes the FSR electrical resistance accordingly. Also, for streaming this variable resistance on an Internet of Things (IoT) platform, Bluetooth Low Energy (BLE) 5 is utilized due to the adequate throughput, high accessibility, and possibility of power consumption reduction. In addition to the sensing mechanism, the device includes a compact, energy-efficient microcontroller and a 3-axis accelerometer that captures body movement. Power is supplied by a rechargeable Lithium-ion Polymer (LiPo) battery, and energy usage is optimized using a buck converter. For comfort and usability, the enclosure was 3D printed using Stereolithography (SLA) technology to ensure a smooth, ergonomic shape. This setup allows the device to operate reliably over long periods without disturbing the user. Altogether, the design supports continuous respiratory tracking in both clinical and home settings, offering a practical, low-power, and portable solution.

Wearable sensors offer a promising platform for non-invasive glucose monitoring by indirectly predicting glucose levels from physiological signals. However, machine learning models trained on such data often suffer degraded performance when applied to new individuals due to distribution shifts in physiological patterns. This study investigates how the inter-subject distribution shift impacts the performance of glucose prediction models trained on wearable data. We utilize the BIGIDEAs dataset, which includes simultaneous recordings of glucose levels and multimodal physiological signals. Personalized XGBoost regression models were trained on data from 10 subjects and evaluated on 5 held-out subjects to assess cross-subject generalization. Distribution shifts in glucose profiles between training and test subjects were quantified using the Anderson-Darling (AD) statistic. Results show that models trained on one individual performed poorly when tested on others. Repeated measures correlation analysis revealed significant positive correlations between the AD statistic and model performance metrics, including RMSE, NRMSE, and MARD. Our findings highlight the challenge of inter-individual generalization and the need for distribution-aware models. We propose personalized calibration and subject phenotyping as future directions to enhance model generalizability.

Fluoride is a crucial inorganic anion found in drinking water, which may pose serious health hazards to human health if consumed in excess quantities. The quantification of fluoride in drinking water with high sensitivity, selectivity and cross-sensitivity is critical. Given these factors, the present work proposes a spectroelectrochemical sensing platform for fluoride sensing using 5,10,15,20-Tetraphenyl-21H,23H-porphine iron (III) chloride (FeTPP), and Tetrabutylammonium perchlorate (TBAP) as electrolyte. The proposed spectroelectrochemistry (SEC) is a hybrid platform that concurrently provides spectroscopic and electrochemical information about a system susceptible to oxidation and reduction. An ensemble–based multivariate prediction model was developed to simultaneously analyse electrochemical and spectroscopic data to predict fluoride concentration with enhanced reliability and precision. The prediction model provided promising results with a coefficient of determination of 0.9923 ± 0.0063 and MSE of 0.369 ± 0.0596. These encouraging results showed the promising performance of the proposed spectroelectrochemical platform in complex real-world applications.

This study introduces a hybrid sensor fusion approach that integrates Schreiber’s nonlinear
filter with traditional filtering methods to enhance the performance of IMU-based systems
in autonomous vehicles. As autonomous vehicles grow more dependent on Inertial Measurement Unit (IMU) data for real-time stability and control, the need for resilient and
accurate sensor fusion becomes critical. This research addresses that need by introducing a
method capable of maintaining robustness under highly dynamic and uncertain conditions.
Accelerometer and gyroscope data from an IMU are first fused using a complementary
filter. The fused signals are then refined by phase-space reconstruction and local manifold
projection, improving noise resilience and maintaining system dynamics. Two datasets
are used to assess the methodology: one was collected indoors with a smartphone, and
another was captured outdoors using a Bosch sensor in various environmental settings. The
proposed method demonstrates superior noise reduction, greater resistance to outliers, and
improved signal consistency compared to conventional complementary and Kalman filters.
The findings demonstrate how chaos-based nonlinear filtering may improve the reliability
of sensor fusion on a variety of sensing platforms in highly dynamic environments. Given
the importance of IMU data for maintaining vehicle stability, this study seeks to support
the development of more stable autonomous transportation systems.

Structures are susceptible to external impacts under long-term service, resulting in various types of damage. Online accurate assessment of the severity of damage is the basis for formulating subsequent maintenance and reinforcement plans. In this work, an online damage identification method based on the Adaptive Extended Kalman Filter (AEKF) is proposed. Initially, the vibration signals of a concrete-filled steel tubular (CFST) test structure subject to multiple lateral impacts are processed, and signals before and after damage inception are spliced to track damage evolution. Subsequently, the natural frequencies extracted from the signals before and after damage inception, and the amplitude of the damage itself are integrated into the state vector, to build a nonlinear state transfer and observation model and allow estimation of the dynamic flexural stiffness of the structure. To further improve the problem solution in the presence of signal losses caused by detachment or breakage of the sensors when damage occurs, the reconstruction of missing signals is accomplished by way of the weighted Matrix Pencil (MP), which ensures the continuity and stability of the AEKF filtering process. By comparing the results with the real damage state, the proposed method is shown to effectively track the gradual reduction of the flexural stiffness, and verifies the feasibility of the proposed method to provide a reliable support for online monitoring and damage assessment.