Sensor Transmission Research Articles

Advancing Healthcare: Integrating A Deep Neural Network With The Bio-Inspired Puffer Fish Optimization Algorithm For Early Detection And Prediction Of Chronic Kidney Disease

Chronic Kidney Disease (CKD) poses a significant global healthcare challenge, requiring advanced strategies for early detection and prognosis. This study introduces an innovative methodology that integrates a Deep Neural Network (DNN) with the bio-inspired Puffer Fish Optimization Algorithm (POA) to enhance CKD diagnosis and prognosis. Biomedical Sensors capture patient data, which is transmitted via the Internet of Medical Things (IoMT) for analysis. The data undergoes rigorous preprocessing, including imputation of missing values, feature encoding, data transformation, and outlier detection, ensuring dataset integrity. The processed data is used to classify CKD into various types, such as Glomerulonephritis, Hypertensive Nephropathy, Diabetic Nephropathy,Polycystic Kidney Disease, and Interstitial Nephritis, with classification optimized through POA to improve hyperparameter tuning and model performance. The DNN-POA model achieves a remarkable precision rate of 98%, offering unprecedented accuracy in CKD classification and providing insights into disease progression. This hybrid approach sets a new standard for personalized CKD management, confirming its robustness and generalizability for real-world clinical applications. However, the study is limited by its reliance on the quality and quantity of IoMT data, where variability in sensor performance and transmission could affect accuracy. Additionally, the model’s effectiveness needs validation across diverse demographic and geographic populations. This innovative hybrid strategy that incorporates deep learning techniques with POA optimization marks a significant advancement in early CKD diagnosis and personalized treatment strategies

A novel online time calibration framework via double-stage EKF for visual-inertial odometry

Abstract There is an unavoidable time offset between the camera stream and the inertial measurement unit (IMU) data due to the sensor triggering and transmission delays, which will seriously affect the accuracy of visual-inertial odometry (VIO). A novel online time calibration framework via double-stage EKF for VIO is proposed in this paper. First, the first-stage complementary Kalman filter is constructed by adapting the complementary characteristics between the accelerometer and the gyroscope in the IMU, where the rotation result predicted by the gyroscope is corrected through the measurement of the accelerometer so that the IMU can output a more accurate initial pose. Second, the unknown time offset is added to the state vector of the VIO system. The estimated pose of IMU is used as the prediction information, and the reprojection error of multiple cameras on the same feature point is used as the constraint information. During the operation of the VIO system, the time offset is continuously calculated and superimposed on the IMU timestamp to obtain the data synchronized by the IMU and the camera. The Schur complement model is used to marginalize the camera state that carries less information in the system state, avoiding the loss of prior information between images, and improving the accuracy of camera pose estimation. Finally, the effectiveness of proposed algorithm is verified using the EuRoC dataset and the real experimental data.

Highly Sensitive Gas Pressure Sensing with Temperature Monitoring Using a Slightly Tapered Fiber with an Inner Micro-Cavity and a Micro-Channel.

A highly sensitive optical fiber gas pressure sensor with temperature monitoring is proposed and demonstrated. It is based on a slightly tapered fiber with an inner micro-cavity forming an in-fiber Mach-Zehnder interferometer (MZI), and a micro-channel is drilled into the lateral wall of the in-fiber micro-cavity using a femtosecond laser to allow gas to flow in. Due to the dependence of the refractive index (RI) of air inside the micro-cavity on its gas pressure and the high RI sensitivity of the MZI, the device is extremely sensitive to gas pressure. To prevent fiber breakage, the MZI is housed in a silicate capillary tube with an air inlet. Multiple modes are excited by slightly tapering the inner micro-cavity, and the resonance dips in the sensor's transmission spectrum feature different linear gas pressure and temperature responses, so a sensitivity matrix algorithm can be used to achieve simultaneous demodulation of two parameters, thus resolving the temperature crosstalk. As expected, the experimental results demonstrated the reliability of the matrix algorithm, with pressure sensitivity reaching up to ~-12.967 nm/MPa and temperature sensitivity of ~89 pm/°C. The features of robust mechanical strength and high air pressure sensitivity with temperature monitoring imply that the proposed sensor has good practical and application prospects.

Fault diagnosis based on incomplete sensor variables with a hierarchical semi-supervised Gaussian mixture classifier

We propose a hierarchical semi-supervised variational semi-Bayesian Gaussian mixture classifier based on the partially incomplete and unlabeled samples for the fault diagnosis of mechanical and electrical systems. These systems are typically complex in structure and are monitored by multiple sensors simultaneously. Some sensor variables in the collected data may be incomplete due to sensor malfunctions or transmission errors. Additionally, labeling the data can be a time-consuming and labor-intensive task, resulting in many unlabeled samples. To address these challenges, the missing sensor variables and the unavailable labels are treated as hidden values and handled within the framework of the Expectation Maximization algorithm. We employ a semi-Bayesian technique with variational inference to estimate the model parameters. Specifically, we introduce prior distribution to the mean and covariance matrix to address the possible singularity of the empirical covariance matrix. The weighting coefficients are left without putting prior distribution so that their values can decay to zero, effectively removing redundant components of the mixture model. The factorized distribution is utilized to approximate the posterior distribution of the model parameters, as well as the missing labels and other latent variables. Numerical and real case studies are carried out to verify the effectiveness of the proposed technique.

Pengembangan Mesh Network Sebagai Ekspansi Protokol LoRaWAN di Politeknik Negeri Malang

In realizing a smart campus, Politeknik Negeri Malang (Polinema) has implemented digital technology in its learning and administrative processes with the goal of improving efficiency, speed, and operational ease. One of the key areas being developed is the Internet of Things (IoT), which is expected to function autonomously to support various campus activities. However, the main challenges in IoT implementation are the limitations in communication range and device power consumption, which cause issues in sensor and actuator data transmission, especially when data cannot be optimally received between nodes. To address these challenges, Polinema is ex-ploring the application of LoRaWAN technology. Although LoRaWAN is effective, it experiences a decline in data transmission quality when the sender and receiver are located in multi-story buildings, which can lead to delays, packet loss, or other disruptions. As a solution, the use of a Mesh Network is proposed to enhance the range and stability of data transmission. This study collected data using RSSI, SNR, and delay parameters in the civil engineering building at Polinema, with sensor data visualized through Grafana. The results show that the system can be well-integrated without conflicts between WiFi and LoRa. The average transmission time was approximately 29 seconds, with no packet loss detected. Additionally, changing the transmission method to confirmed uplink was necessary to maintain data integrity, while adjusting transmis-sion intervals was crucial to avoid scheduling issues. These findings indicate that implementing a Mesh Network as an extension of the LoRaWAN protocol can significantly improve the perfor-mance of IoT systems in Polinema’s indoor environment.

To Validate Use of Transmittance Sensor of Peripheral Pulse Oximeter for Forehead Pulse Oximetry in Newborns

Introduction Peripheral pulse oximeters with continuous monitoring of heart rate and saturation have revolutionized neonatal care. Motion artifacts, hypothermia, and poor perfusion are some of the barriers for reliable reading in the transmittance type of peripheral pulse oximeters. This study was planned to assess the effectiveness of transmittance sensors applied over the forehead for monitoring the heart rate and saturation. Material and Methods An observation study was conducted over a period of 1 month. Two pulse oximeters (Masimo RAD97) were applied simultaneously, one over the periphery (right hand/wrist) and the other over the forehead using an innovative headband. A total of 540 readings of heart rate and saturation (SpO2) from each site were recorded. A difference of more than 5 beats/minute in heart rate and 2% saturation were considered clinically significant. Results Forty-five neonates with mean gestational age of 35.3 ± 3.2 weeks and birth weight of 2109 ± 683 grams were enrolled. Forehead pulse oximeter could pick up the heart rate and SpO2 readings in all the babies. A statistically significant difference of 3.8 beats/minute in heart rate and 2.5% in SpO2 was noted ( p-value < 0.0001). The difference in heart rate was not clinically significant. Conclusion We propose that the transmittance type peripheral pulse oximeter sensors can be used over the forehead. It has the potential to avoid erroneous readings due to motion artifacts, hypothermia, or shock. Saturation nomograms for the forehead pulse oximetry need to be established before it can be used to monitor and manage the neonates for the same.

Sparse regularized graph pooling network optimal sensor placement method for diesel engine vibration fault perception system

Vibration sensor network optimization increases monitoring effectiveness and reduces sensor quantity and transmission burden. However, the traditional model-driven methods depend on precise finite element models, challenging for complex machinery. This paper presents a data-driven approach using the Sparse Regularized Graph Pooling Network (SRGPN), which conceptualizes sensor networks as graphs and uses graph pooling to identify optimal sensor combinations. A sparsity regularization term related to the number of sensors is included in the loss function, aiming for the minimal yet effective sensor combination. Additionally, a monitoring capability metric suited for diesel engines with multi-source impulse signals is proposed, reflecting the sensors’ monitoring capacity. Validated through simulations and tests on a diesel engine test bench, the results show that SRGPN optimally places sensors near excitation sources, balancing sensor count and monitoring needs. This approach shows potential for optimizing sensor placements in condition monitoring.

COSMO: Dynamic Uploading Scheduling in mmWave-Based Sensor Networks with Mobile Blockers

Wireless sensor networks (WSNs) leveraging millimeter wave (mmWave) communication for bandwidth-demanding applications is considered in this paper. Despite the large bandwidth, the delivery of delay-sensitive information collected by sensors may still face significant latency due to the vulnerability to intermittent link blockage. Hence, the guarantee of low age of information (AoI) in mmWave WSNs is not straightforward. In this paper, the wireless sensing and dynamic programming techniques are jointly exploited to relieve the above issue. The former tracks the human blockers and predicts the chance of link blockage; the latter optimizes the transmission of multiple sensors based on the prediction. Particularly, the long-term optimization of sampling, uplink time and power allocation policies in a sensor network can be formulated as an infinite-horizon Markov decision process (MDP) with discounted cost, where the state transition probabilities can be predicted via wireless sensing. A novel low-complexity solution framework, namely COSMO, with a guaranteed performance in the worst case, is proposed. Simulations show that compared with heuristic benchmarks, benefiting from the prediction of the link blockage, COSMO can significantly suppress the average system cost, which consists of both AoI and energy consumption.

Real-Time and Wireless Transmission of a Nitrogen-Doped Ti3C2Tx Wearable Gas Sensor for Efficient Detection of Food Spoilage and Ammonia Leakage.

With the rising popularity of smart homes, there is an urgent need for devices that can perform real-time online detection of ammonia (NH3) concentrations for food quality measurement. In addition, timely warning is crucial to preventing individual deaths from NH3. However, few studies can realize continuous monitoring of NH3 with high stability and subsequent application validation. Herein, we report on an integrated device equipped with a nitrogen-doped Ti3C2Tx gas sensor that shows great potential in detecting food spoilage and NH3 leakage. The nitrogen doping results in the lattice misalignment of Ti3C2Tx, subsequently realizing effective barrier height modulation and enhanced charge transfer efficiency of nitrogen-doped Ti3C2Tx. Density functional theory calculations confirm the greatly enhanced adsorption of NH3 on nitrogen-doped Ti3C2Tx. Our work can inspire the design of efficient gas sensors for real-time and wireless detection of food spoilage and NH3 leakage.

Combined Sweeping and Jumping Method to Enhance Node Insertion Algorithm for Wi-Fi Sensor Networks

Two dominant driving forces for evolving communication technologies in the current society have been the proliferation of wireless access networks to the Internet and the broadbandization of access and infrastructure networks. Through these evolutions of communication technologies, high-resolution contents are instantly delivered to wireless devices such as mobile phones, wireless tablets, and headsets. Recently, wireless sensor networks, where up to 1000 low-power sensors are wirelessly connected to each other, have been created and connected to the Internet, which presents a new challenge of efficiently coordinating the transmissions of many wireless sensors with minimal transmission overheads. Developing an efficient Medium Access Control (MAC) protocol governing the transmissions of wireless sensor networks is crucial for the success of wireless sensor networks for the realization of the Internet of Things (IoT). In 2023, the node insertion algorithm was proposed to efficiently derive the minimal number of serially connected multipolling sequences of many wireless sensors, by which Access Points (APs) can poll wireless sensors with minimal polling overheads. In this paper, the combined sweeping and jumping method is presented to dramatically enhance the searching performance of the node insertion algorithm. To validate the performance of the combined sweeping and jumping method, simulation results are presented for wireless sensor networks where wireless sensors with varying transmission ranges exist.

Joint optimization of UAV aided covert edge computing via a deep reinforcement learning framework

In this work, we consider an Unmanned Aerial Vehicle (UAV) aided covert edge computing architecture, where multiple sensors are scattered with a certain distance on the ground. The sensor can implement several computation tasks. In an emergency scenario, the computational capabilities of sensors are often limited, as seen in vehicular networks or Internet of Things (IoT) networks. The UAV can be utilized to undertake parts of the computation tasks, i.e., edge computing. While various studies have advanced the performance of UAV-based edge computing systems, the security of wireless transmission in future 6G networks is becoming increasingly crucial due to its inherent broadcast nature, yet it has not received adequate attention. In this paper, we improve the covert performance in a UAV aided edge computing system. Parts of the computation tasks of multiple ground sensors are offloaded to the UAV, where the sensors offload the computing tasks to the UAV, and Willie around detects the transmissions. The transmit power of sensors, the offloading proportions of sensors and the hovering height of the UAV affect the system covert performance, we propose a deep reinforcement learning framework to jointly optimize them. The proposed algorithm minimizes the system average task processing delay while guaranteeing that the transmissions of sensors are not detected by the Willie under the covertness constraint. Extensive simulations are conducted to verify the effectiveness of the proposed algorithm to decrease the average task processing delay with comparison with other algorithms.

Radiation Anomaly Detection of Sub-Band Optical Remote Sensing Images Based on Multiscale Deep Dynamic Fusion and Adaptive Optimization

Radiation anomalies in optical remote sensing images frequently occur due to electronic issues within the image sensor or data transmission errors. These radiation anomalies can be categorized into several types, including CCD, StripeNoise, RandomCode1, RandomCode2, ImageMissing, and Tap. To ensure the retention of image data with minimal radiation issues as much as possible, this paper adopts a self-made radiation dataset and proposes a FlexVisionNet-YOLO network to detect radiation anomalies more accurately. Firstly, RepViT is used as the backbone network with a vision transformer architecture to better capture global and local features. Its multiscale feature fusion mechanism efficiently handles targets of different sizes and shapes, enhancing the detection ability for radiation anomalies. Secondly, a feature depth fusion network is proposed in the Feature Fusion part, which significantly improves the flexibility and accuracy of feature fusion and thus enhances the detection and classification performance of complex remote sensing images. Finally, Inner-CIoU is used in the Head part for edge regression, which significantly improves the localization accuracy by finely adjusting the target edges; Slide-Loss is used for classification loss, which enhances the classification robustness by dynamically adjusting the category probabilities and markedly improves the classification accuracy, especially in the sample imbalance dataset. Experimental results show that, compared to YOLOv8, the proposed FlexVisionNet-YOLO method improves precision, recall, mAP0.5, and mAP0.5:0.9 by 3.5%, 7.1%, 4.4%, and 13.6%, respectively. Its effectiveness in detecting radiation anomalies surpasses that of other models.

The Impact of an Electric Machine Body on EM Wave Propagation in RTMS

The metallic components of an electric machine exert a notable impact on the electromagnetic transmission between an external recipient and an internal shaft sensor. The RTMS (rotor temperature monitoring system) is aimed at boosting power transfer in the machine, regardless of the considerable impact of its components. This research assesses the influence of the components of an electric machine on shaft sensor transmission and specifically addresses the packaging needs of the receiver antenna. The study includes a comparative analysis of two antennas. HFSS (high frequency structure simulator) simulation was utilized to identify the antenna with a superior propagation factor. The principal contribution of this paper is the assessment of how the electrical machine body affects the transmission and propagation of rotor sensor signals, establishing a connection between these effects and the packaging criteria for the receiver antenna. Moreover, the paper presents an antenna design that capitalizes on the electrical machine body to enhance the power transmission effectiveness. The final section of this paper encompasses the experimental results obtained from the implementation of the RN171 antenna on the electrical machine, provides valuable insights into the propagation characteristics, and contributes to a comprehensive understanding of the electromagnetic dynamics within the system.

Sensor Transmission Scheduling Over a Time-Varying Channel

Sensor Transmission Scheduling Over a Time-Varying Channel

Prototipe Otomasi Pemantauan Suhu Dan Pemberian Tekanan Gas Pada Pemeraman Pisang Cavendish

In Indonesia, banana production is the highest compared to other fruits. One of the variations is the Cavendish Banana, which has high economic value and has the potential to support Indonesia's economic recovery. Due to the high demand for Cavendish Banana fruit, farmers use ethylene gas to accelerate fruit ripening. The ripening process is done by manually directing the ethylene gas taken from the container to the ripening room through a pipe system. The purpose of this research is to create a prototype of an automatic system for ethylene gas usage and temperature control, where compressor pressure is an alternative to ethylene gas pressure. This system operates automatically according to the schedule that has been set on the RTC Module. There is a Pressure Transmitter Sensor in charge of measuring pressure with an error rate of 3.5%, a Selenoid Valve that plays a role in flowing air pressure into a box equipped with a DC fan as a spreader, and a DHT22 Sensor used for temperature monitoring with an error rate of 2.8%. Information regarding time, temperature, and pressure values are displayed on a 20x4 LCD screen. System testing involves time scheduling where the device is turned on one minute after the system is activated. Subsequently, the device was switched on again after different times of 2 and 5 minutes. The pressure value was set at 30Psi. Based on the results of the system automation testing, the device was successful with a success rate of 80%, so this automation system is ready to be implemented directly on the Cavendish Banana fruit ripening device.

Adaptive Multi-Head Self-Attention Based Supervised VAE for Industrial Soft Sensing With Missing Data

Variational auto-encoders (VAEs) have been widely used in soft sensing due to their ability to provide a probabilistic description of the hidden space. However, VAEs are static models that do not consider process dynamics, which can limit the ability of VAEs to accurately model complex industrial processes. To tackle this problem, this paper proposes a model called adaptive multi-head self-attention based supervised VAE (AMSA-SVAE). In AMSA-SVAE, an adaptive multi-head self-attention mechanism (AMSA) is proposed based on the multi-head self-attention mechanism (MSA). AMSA can dynamically extract different attention information depending on specific tasks. By adjusting the attention weights based on the input sequence, AMSA allows for more accurate and efficient modeling of complex industrial processes. Then, AMSA is used as the encoder and decoder of SVAE for soft sensing. Furthermore, with the data generation capabilities of VAE, an adaptive multi-head self-attention based VAE (AMSA-VAE) framework is proposed to address the issue of missing data. The AMSA-VAE is used to dynamically fill in missing data, thereby extending the capabilities of AMSA-SVAE. Finally, the performance of AMSA-SVAE is verified by a set of real industrial data, and the ability of AMSA-VAE framework is demonstrated by simulating different degrees of data missing rates. By combining the dynamic modeling capabilities of AMSA-SVAE with the data generation capabilities of AMSA-VAE, the proposed approach provides a robust solution to the challenges of incomplete data in soft sensing. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> — Soft sensors are widely used to measure key parameters in industrial processes, but missing values in the data are common due to sensor failures or transmission signal interference. This poses a significant challenge for traditional soft sensors, which require complete data to accurately model. Meanwhile, the dynamic nature of industrial process data further complicates the modeling process. To solve these challenges, this paper proposes an AMSA-SVAE model for soft sensing and an AMSA-VAE framework for filling in the missing values in the data, thereby extending the capabilities of AMSA-SVAE to handle missing data. When facing a dataset with missing values, AMSA-VAE framework is first used to fill in the missing values before the filled complete data is fed into AMSA-SVAE for modeling. Finally, the proposed approaches are evaluated through two sets of experiments using a real industrial dataset, showing the excellent performance of AMSA-SVAE and AMSA-VAE framework in modeling dynamic industrial process data and addressing the missing data problem.

Migratory contingents of brown trout reveal variable exposure to anthropogenic threats along a fjord‐river continuum

AbstractBrown trout is a partially migratory salmonid that makes use of diverse habitats to maximise growth and fitness. One of the most substantial threats to brown trout is infection with pathogens from open net‐pen fish farming, which creates hotspots for pathogen reproduction and transmission. Western Norway is a global hotspot for both fish farming and wild salmonids, which generates conflicts due to the impacts of the farms on the behaviour, survival, and fitness of salmonids that overlap with farming activities. In this study, we tagged adult brown trout (&gt;35 cm) at two spatiotemporal intervals that corresponded to two different life history stages: springtime in the river when trout were completing overwintering and summer in the fjord when trout were in their marine feeding phase. The tagging revealed three different behaviours, fish that remained in freshwater, fish that migrated between freshwater and the fjord, and fish that remained in the estuary. Although some trout moved &gt;100 km to the outer fjord areas, most trout remained relatively close to the river. Depth sensor transmitters in a subset of trout also revealed that the trout remained in the upper water column. Most of the horizontal and vertical movements therefore resulted in spatial overlap with fish farming for the migratory trout, but not for resident trout that remained in the estuary or in freshwater. Findings reveal the challenges of managing a fish with such behavioural plasticity but the urgency of recognising how important inner fjord habitats are for migratory brown trout.

Big Data Based English Oral Teaching by Voice Network Analysis in 6G Wireless Sensor Transmission Model

Big Data Based English Oral Teaching by Voice Network Analysis in 6G Wireless Sensor Transmission Model

An intelligent BIM-enabled digital twin framework for real-time structural health monitoring using wireless IoT sensing, digital signal processing, and structural analysis

Structural health monitoring (SHM) of civil infrastructure is critically important due to its direct influence on public safety and economic activities. Exiting Building Information Modeling (BIM)-based SHM systems often use offline data processing techniques to analyze and visualize structural health data. Despite some of them adopting Internet of Things (IoT) to enable real-time sensor data collection, sensor data quality still remains uncertain due to a lack of sensor signal preprocessing in those existing systems. Additionally, the IoT-based SHM systems often disregard structural analysis domain knowledge, which is important for accurate and precise SHM. Therefore, there is still a need to improve existing systems and practices by enabling more efficient and reliable data collection and processing as well as providing more representative SHM data visualization in BIM. As such, this paper proposes an intelligent BIM-enabled digital twin (DT) framework that integrates wireless IoT sensing and communication, digital signal processing (DSP), and structural analysis domain knowledge. The proposed system (1) leverages IoT sensing and wireless communication to enable autonomous and real-time SHM sensor data collection and transmission, (2) applies and compares multiple DSP techniques to preprocess the sensor data/signals, and (3) innovatively embraces structural analysis expertise into structural behavior visualization in BIM by performing sensor data interpolation for enabling the visualization of structural behaviors at different locations of a structural element/component. The proposed BIM-enabled DT framework was demonstrated and tested for monitoring and visualizing the structural deformations of critical structural components using a prototyped structural frame subject to bending forces. The developed framework could be used and extended for any structural elements (such as beams, columns, trusses, slabs, arches, bracings, walls, footings, foundations, and girders, among others) and could be applied to any kind of structure. Experimental results showed that the proposed framework could effectively monitor and intuitively visualize the structural deformations under different load configurations with a high DT updating frequency of 5 Hz. The innovation of this study is reflected by integrating structural analysis expertise with IoT-enabling sensing data analytics in order to improve the representativeness of real-time structural behavior visualization in BIM and to advance the DT-based SHM systems in a faster and more adaptive direction. Ultimately, this paper contributes to the body of knowledge by developing a generic and easily extendable BIM-enabled DT framework for SHM with high sensor data quality and improved visualization to advance the existing practices of BIM-based SHM for civil infrastructure asset management.

Innovative wearable solutions: Semi-releasing ion-conductive lignin hydrogel sensors for enhanced practicability

The severe negative effects of impurities adhering to the external surface of wearable devices can significantly influence the signal transmission, performance, and lifespan of hydrogel sensors. Herein, we developed an ion-conducting hydrogel sensor with a strong adhesive side and a non-adhesive side, similar to a “semi-releasing material.” This hydrogel, formulated using deep eutectic solvents obtained from choline chloride and acrylic acid, contained lignin. This versatile material, exhibiting properties similar to semi-releasing materials, was treated with an AlCl3 solution on one side. Additionally, the hydrogel was successfully used as a highly adhesive strain sensor for real-time monitoring of various human activity signals. Moreover, the hydrogel demonstrated excellent environmental tolerance and conductivity. Lignin extracted from wood flour endowed the hydrogel sensor with excellent adhesion energy (up to 427.1 J/m2) and UV resistance. Treatment of hydrogels with AlCl3 completely eliminated their adhesiveness, thereby enhancing fracture elongation and tensile strength. This improvement can be attributed to the absence of carboxyl groups and the formation of a metal-phenolic network. The implementation of this convenient and efficient strategy provides a more feasible approach to address challenges related to impurity adhesion and signal transmission in flexible wearable devices.