As an innovative technology, the impedance-based technique has been extensively studied for the structural health monitoring (SHM) of various civil structures. The technique’s advantages include cost-effectiveness, ease of implementation on a complex structure, robustness to early-stage failures, and real-time damage assessment capabilities. Nonetheless, very few studies have taken those advantages for monitoring the health status and the structural condition of wind turbine structures. Thus, this paper is motivated to give the reader a general outlook of how the impedance-based SHM technology has been implemented to secure the safety and serviceability of the wind turbine structures. Firstly, possible structural failures in wind turbine systems are reviewed. Next, physical principles, hardware systems, damage quantification, and environmental compensation algorithms are outlined for the impedance-based technique. Afterwards, the current status of the application of this advanced technology for health monitoring and damage identification of wind turbine structural components such as blades, tower joints, tower segments, substructure, and the foundation are discussed. In the end, the future perspectives that can contribute to developing efficient SHM systems in the green energy field are proposed.

This study proposes a cost-effective prestress monitoring method for post-tensioned reinforced concrete (RC) beams using a smart strand. Firstly, the concept of a piezoelectric-based smart strand and its implementation for prestress force monitoring are developed. The smart strand is prepared by embedding inexpensive and high-sensitivity electromechanical impedance (EMI) sensors in a steel strand. Next, the feasibility of the proposed method is experimentally verified for prestress force monitoring of a simple supported post-tensioned RC beam. A smart strand prototype is fabricated and embedded into a 6.4 m RC beam which is then prestressed with different levels. For each prestress level, the EMI responses of the smart tendon are measured and the EMI features are extracted for prestress force monitoring. The results showed that the EMI signals of the smart strand showed strong resonant peaks that varied sensitively to the prestress level of the beam. The prestress change in the prestressed RC beam was successfully estimated by using linear regression models of the EMI features.

This study proposes a low-cost, wearable multimodal system for comprehensive cardiovascular monitoring, integrating electrocardiogram (ECG), phonocardiogram (PCG), and photoplethysmogram (PPG) signals. By leveraging the complementary strengths of these modalities, the system extracts key physiological markers: pre-ejection period (PEP) from ECG–PCG fusion to assess myocardial electromechanical timing; pulse transit time (PTT) from PCG–PPG fusion to reflect arterial stiffness; and pulse arrival time (PAT) from ECG–PPG fusion to characterize heart–arterial coupling. Experimental results show that PEP is significantly associated with body mass index (BMI) but not age, suggesting that age-related changes primarily affect the arterial system rather than myocardial function. In contrast, both PTT and PAT demonstrate moderate negative correlations with age and BMI, supporting their relevance as noninvasive indicators of vascular aging. Additionally, PAT exhibits a significant sex-based difference, highlighting physiological disparities between male and female cardiovascular systems. Overall, the proposed fusion-based approach demonstrates technical feasibility and clinical potential for scalable, preventive cardiovascular healthcare, enabling early risk detection, continuous at-home monitoring, and improved long-term health management.

This study investigates the dynamic characteristics of a smart PZT interface mounted on a prestressed anchorage to verify the numerical feasibility of the admittance-based anchor force monitoring technique. Firstly, the admittance-based anchor force monitoring technique through a single-mount PZT interface is outlined. The admittance response of the PZT interface-anchorage system is theoretically derived to show the proof-of-concept of the technique for anchor force monitoring. Secondly, a finite element model corresponding to a well-established experimental model in the literature is constructed. The effect of anchor force is equivalently treated by the contact stiffness and damping parameters at the bottom surface of the anchorage. Thirdly, the admittance and the impedance responses are numerically analyzed and compared with the experimental data to evaluate the accuracy of the numerical modelling technique. Fourthly, the local dynamics of the PZT interface are analyzed by modal analysis to determine vibration modes that are sensitive to the change in the contact stiffness (i.e., representing the anchor force). Finally, the admittance responses corresponding to the sensitive vibration modes are numerically analyzed under the change in the contact stiffness. The frequency shift and the admittance change are quantified by statistical damage indices to verify the numerical feasibility of the anchor force monitoring technique via the smart PZT interface. The study is expected to provide a reference numerical model for the design of the single-point mount PZT interface.

This study addresses the growing importance of hand gesture recognition across diverse fields, such as industry, education, and healthcare, targeting the often-neglected needs of the deaf and dumb community. The primary objective is to improve communication between individuals, thereby enhancing the overall quality of life, particularly in the context of advanced healthcare. This paper presents a novel approach for real-time hand gesture recognition using bio-impedance techniques. The developed device, powered by a Raspberry Pi and connected to electrodes for impedance data acquisition, employs an impedance chip for data collection. To categorize hand gestures, Convolutional Neuron Network (CNN), XGBoost, and Random Forest were used. The model successfully recognized up to nine distinct gestures, achieving an average accuracy of 97.24% across ten subjects using a subject-dependent strategy, showcasing the efficacy of the bioimpedance-based system in hand gesture recognition. The promising results lay a foundation for future developments in nonverbal communication between humans and machines as it contributes to the advancement of technology for the benefit of individuals with hearing impairments, addressing a critical social need.

Numerous investigations have been conducted to enhance the motor imagery-based brain–computer interface (BCI) classification performance on various aspects. However, there are limited studies comparing their proposed feature selection framework performance on both objective and subjective datasets. Therefore, this study aims to provide a novel framework that combines spatial filters at various frequency bands with double-layered feature selection and evaluates it on published and self-acquired datasets. Electroencephalography (EEG) data are preprocessed and decomposed into multiple frequency sub-bands, whose features are then extracted, calculated, and ranked based on Fisher’s ratio and minimum-redundancy-maximum-relevance (mRmR) algorithm. Informative filter banks are chosen for optimal classification by linear discriminative analysis (LDA). The results of the study, firstly, show that the proposed method is comparable to other conventional methods through accuracy and F1-score. The study also found that hand vs. feet classification is more discriminable than left vs. right hand (4–10% difference). Lastly, the performance of the filter banks common spatial pattern (FBCSP, without feature selection) algorithm is found to be significantly lower (p = 0.0029, p = 0.0015, and p = 0.0008) compared to that of the proposed method when applied to small-sized data.