In modern society, technology associated with smart sensors made from flexible materials is rapidly evolving. As a core component in the field of wearable smart devices (or ‘smart wearables’), flexible sensors have the advantages of excellent flexibility, ductility, free folding properties, and more. When choosing materials for the development of sensors, reduced weight, elasticity, and wearer’s convenience are considered as advantages, and are suitable for electronic skin, monitoring of health-related issues, biomedicine, human–computer interactions, and other fields of biotechnology. The idea behind wearable sensory devices is to enable their easy integration into everyday life. This review discusses the concepts of sensory mechanism, detected object, and contact form of flexible sensors, and expounds the preparation materials and their applicability. This is with the purpose of providing a reference for the further development of flexible sensors suitable for wearable devices.

Electroencephalography (EEG) technology, as a crucial tool for monitoring brain function, has undergone significant technological advancements and improvements in recent years. Among these advancements, electrode technology, as a core component of EEG systems, continues to drive its development. This paper systematically reviews and categorizes various types of EEG electrodes from the perspectives of electrode structure and materials, with a focus on principles, design characteristics, and pros and cons of semi-dry and dry electrodes. In addition, it delves into the interface characteristics of EEG electrodes, analyzing the impact of electrode interface dimensions, materials, and structures on the equivalent circuit models of electrode–skin interfaces, electrode–skin contact impedance, and motion artifact suppression capabilities. This paper also introduces methods for evaluating electrode performance, emphasizing approaches to verify the signal acquisition capabilities and stability of novel electrodes through specific experimental paradigms and comparative analyses. Through comprehensive review and in-depth analysis, this paper aims to provide profound theoretical references and practical guidance for further research and real-world applications of EEG electrode technology.

Wearable electrocardiogram (ECG) devices are universally used around the world for patients who have cardiovascular disease (CVD). At present, how to suppress motion artifacts is one of the most challenging issues in the field of physiological signal processing. In this paper, we propose an adaptive cancellation algorithm based on multi-inertial sensors to suppress motion artifacts in ambulatory ECGs. Firstly, this method collects information related to the electrode motion through multi-inertial sensors. Then, the part that is not related to the electrode motion is removed through wavelet transform, which improves the correlation of the reference input signal. In this way, the ability of the adaptive cancellation algorithm to remove motion artifacts is improved in the ambulatory ECG. Subsequent experimentation demonstrated that the wavelet adaptive cancellation algorithm based on multi-inertial sensors can effectively remove motion artifacts in ambulatory ECGs.

This study intends to explore the influence of a fabric electrode skin interface filling medium on ECG signals under dynamic and static conditions, so as to be able to better monitor both stable and dynamic ECG signals. In this paper, cotton fabric electrodes were prepared using electroless silver-plated conductive cotton fabric, and then the electrodes were integrated into a physiotherapy-grade elastic bandage to assemble the fabric ECG tape. The electrode impedance tester and the BIOPAC physiological recorder were used to measure the fabric electrode-skin impedance and electrocardiogram under dynamic and static conditions, respectively. Later, the influence of the fabric electrode coated medium on the acquisition effect of an electrocardiogram signal under dynamic and static conditions is discussed. The results show that in the absence of a dielectric reagent coating, the cotton fabric electrode is easily disturbed by external noise which leads to large electrode-skin contact impedance. However, the electrode-skin contact impedance is greatly reduced after coating the fabric electrode with the dielectric reagent. At the same time, after applying the conducive paste coating, the corresponding change curve of the fabric electrode became more stable, and the impedance value was less than 0.005 MΩ·cm², which meets the standard requirement for textile dry electrode skin impedance. In a state of rest, a clear and stable ECG can be collected regardless of whether the medium reagent is coated or not. However, only after applying the conductive paste can a stable ECG signal be collected under the three motion modes, or can a P wave, QRS wave group, or T wave be clearly distinguished. In addition, there is a high correlation with the disposable gel electrode, which can satisfy the requirements of the long-term monitoring of ECG signals under dynamic conditions.

Emotion recognition plays a significant role in artificial intelligence and human-computer interaction. Electroencephalography (EEG) signals, due to their ability to directly reflect brain activity, have become an essential tool in emotion recognition research. However, the low dimensionality of sparse EEG channel data presents a key challenge in extracting effective features. This paper proposes a sparse channel EEG-based emotion recognition method using the CNN-KAN- F2CA network to address the challenges of limited feature extraction and cross-subject variability in emotion recognition. Through a feature mapping strategy, this method maps features such as Differential Entropy (DE), Power Spectral Density (PSD), and Emotion Valence Index (EVI) - Asymmetry Index (ASI) to pseudo-RGB images, effectively integrating both frequency-domain and spatial information from sparse channels, providing multi-dimensional input for CNN feature extraction. By combining the KAN module with a fast Fourier transform-based F2CA attention mechanism, the model can effectively fuse frequency-domain and spatial features for accurate classification of complex emotional signals. Experimental results show that the CNN-KAN- F2CA model performs comparably to multi-channel models while only using four EEG channels. Through training based on short-time segments, the model effectively reduces the impact of individual differences, significantly improving generalization ability in cross-subject emotion recognition tasks. Extensive experiments on the SEED and DEAP datasets demonstrate the proposed method’s superior performance in emotion classification tasks. In the merged dataset experiments, the accuracy of the SEED three-class task reached 97.985%, while the accuracy for the DEAP four-class task was 91.718%. In the subject-dependent experiment, the average accuracy for the SEED three-class task was 97.45%, and for the DEAP four-class task, it was 89.16%.

Urine leakage volume is an important indicator reflecting the severity of incontinence in patients. Currently, there are limited smart diapers capable of continuous dynamic monitoring of urine volume. This study developed two types of urine volume sensors, resistive and capacitive, which were integrated with traditional diapers to assess urine leakage levels: mild leakage (0–5 mL), moderate leakage (6–12 mL), and severe leakage (above 12 mL). Three patterns of resistive urine volume sensors were designed, and the results showed that the A pattern could accurately determine urine volume and frequency levels. Additionally, three electrode spacing designs were tested for the capacitive urine volume sensors. The results indicated that the sensor with a 1 cm electrode spacing could determine the urine volume range, with each 1 mL increase in urine causing a capacitance rise of approximately 1.5–1.8 pF, with an error of about ± 0.5 mL per increment. Both resistive and capacitive methods showed high accuracy in monitoring urine volume and frequency. This study validated the feasibility of smart flexible fabric sensors in detecting urine volume and frequency, providing a potential solution for better assessing and managing the condition of incontinence patients.