Reliable Sensor Research Articles

EghiFit: Smartphone based Behaviour Monitoring and Health Recommendation in a Weight Loss Intervention Study

Background Current health recommender systems lack interactivity that relates to the current situation. Methods We designed and implemented an intervention study for obese patients that incorporates context information obtained from smartphone and smartwatch sensors, gamification, as well as joint goal management of patients and health coaches. We developed a health behaviour recommendation system comprising of a smartphone application, cloud platform for data management, and a data dashboard for coaches. Results We conducted a three months long study and analysed data from eight patients, focusing on system function, patient adherence, satisfaction and overall impact of the proposed system on changing health-related habits. Along with data analysis, we also provide patient feedback collected during interview round after the end of the study. Conclusion Patients could successfully implement the goals using the EghiFit app. Challenges regarding the data collection, recommendation synthesis, and patient engagement persist. Furthermore, reliable sensor data processing on current smartphone platforms is difficult due to system restrictions. Future research should further integrate sensor data, gaming, and health behaviour intervention design using smart devices.

Nano‐Patterned CuO Nanowire Nanogap Hydrogen Gas Sensor with Voids

AbstractHydrogen (H2) is increasingly employed in industrial applications, such as developing hydrogen fuel cells for vehicles and power plants. However, H2 explodes at a concentration limit of 4%, necessitating the development of ultrasensitive hydrogen sensors capable of early‐stage detection of hydrogen leaks. In this study, nano‐patterned polycrystalline cupric oxide (CuO) nanowire array nanogap gas sensors with voids are fabricated using electron‐beam lithography and ex situ oxidization by annealing, which could detect 5 ppb H2 and show response and recovery times of less than 10 s without a baseline shift. Combining a pre‐H2 annealing process in Ar/H2 for Cu nanowires and a low‐rate oxidation process enhances the crystallinity of CuO nanowires, facilitating the preparation of polycrystalline CuO nanowires with voids, which is a significantly practical approach for the improvements of gas sensing properties. Response and recovery times of <10 s can be obtained for a gap separation of ≈30 nm. These improvements are discussed based on a high electric field of ≈1.3 MV cm−1. The relationship between the normalized response and H2 concentration is discussed based on the power law. This paper presents highly reliable and fast H2 sensors without a baseline shift to meet the demands of the hydrogen industry.

Ultra Enhancement of the Resolution of the Divergence Beam-Based SPR Sensor Using a Wavelet Filter

Surface Plasmon Resonance (SPR) sensors are highly sensitive to refractive index variations, making them ideal for biosensing and chemical detection applications. However, their performance can be constrained by noise, resolution limits, and signal distortions, particularly in divergence beam-based configurations. This research presents an ultra-enhancement of the performance of divergence beam-based SPR sensors by employing advanced wavelet filtering techniques. Wavelets, with their multi-resolution analysis capability, are applied to denoise and refine the sensor signal, significantly improving key performance metrics, including resolution, mean square error (MSE), root mean square error (RMSE), signal-to-noise ratio (SNR), sensitivity matrix (SM), and combined sensitivity factor (CSF).The wavelet filter effectively decomposes the SPR signal into distinct frequency bands, separating noise while retaining high-frequency components required for accurate sensing. This results in a significant reduction in MSE and RMSE of 30 and 92.26%, respectively, while simultaneously improving SNR by 54.08%, maintaining signal quality. The wavelet filter significantly improved the resolution of the SPR sensor by 99.95% (1.3772×10-7 RIU), allowing for more precise detection of refractive index changes and boosting diverge beam-based SPR sensor performance. In addition, a sensitivity matrix (SM) and combined sensitivity factor (CSF) were improved by 42 and 41.94%, respectively, allowing for a more thorough evaluation of the sensor's performance across multiple operational parameters. Simulation and experimentation show that wavelet filtering surpasses standard filtering approaches in terms of noise suppression and signal clarity. This technology has considerable potential for improving the accuracy and reliability of SPR sensors in high-sensitivity applications

Modeling, assessment and characterization of soiling on PV Technologies. Toward a Better understanding of the Relation between dust deposition and performance losses

Soiling is a critical, site-specific challenge that affects the performance and economic viability of photovoltaic power plants. This study evaluates the effectiveness of an outdoor microscopy method under the specific climatic conditions and dust composition of the Mid-south region of Morocco. Semi-empirical models were developed to correlate the Soiling Coverage Index with losses in optical and electrical performance. Additionally, a comprehensive analysis of local dust, encompassing its chemical composition, morphology, and size distribution, was conducted. Results indicate quartz as the predominant mineral in Benguerir city’s dust particles, with diameters ranging from 0 μm to 26 μm. Additionally, following a 2-week exposure with no rainfall, a dust density of 2.5 g/m2 accumulated on the deployed glass coupons, resulting in a soiling ratio of 6.9 % and a relative transmittance loss of 15.19 %. The surface coverage index, as calculated by the outdoor microscope, was 9.16 %. Furthermore, the evaluation of this metric revealed strong positive correlations (r2 = 0.95 to 0.99) with key soiling indicators such as dust density, soiling ratio, and transmittance loss. These findings underscore the efficacy of the outdoor microscope as a fast, low-cost, and reliable soiling monitoring sensor, offering valuable insights for future monitoring and mitigation efforts.

Anchor carbon dots inside NH2-MIL-88B via ship-in-a-bottle strategy for dual signal enhancement in colorimetric-fluorescent sensors

The increase in oxytetracycline (OTC) pollution has become a significant risk to ecological stability and human health because of excessive use. Therefore, developing a precise and reliable sensor for trace OTC detection is critical. In this work, a dual-mode sensor of colorimetric-fluorescent (CL-FL) with dual signal-on was developed for OTC detection. Firstly, a novel carbon dots encapsulating in cavities of MOFs composite (CDs@NH2-MIL-88B) was constructed by the ship-in-a-bottle approach, which enhanced peroxidase-like activity and fluorescent intensity. Further, combining with surface molecularly imprinted polymer (MIP), the dual-mode sensor was constructed to develop selectivity (CDs@NH2-MIL-88B@rMIP). Finally, the dual signal enhancement mechanism of the sensor comes from the interaction between OTC and identifying binding site involving strong hydrogen bonding and metal complexation from imprinted cavity and CDs@NH2-MIL-88B, respectively. The prepared sensor has a linear detection range of 1.00 × 10-9 - 1.00 × 10-5M with the limit of detection (LOD) of 1.47 × 10-10M for the CL mode and a detection range of 1.00 × 10-11 -1.00 × 10-7M with the LOD of 1.84 × 10-12M for the FL mode. Meanwhile, the sensor shows high sensitivity, accuracy and reliability in OTC detection. Overall, this work provides a promising CL-FL dual-mode sensing systems with signal-on in the detection of antibiotics and other hazardous pollutants.

A fast-aware multi-target response prediction approach and its application in aeronautical engineering

Response prediction is a fundamental yet challenging task in aeronautical engineering, requiring an accurate selection of sensor positions correlated with the target responses to achieve precise predictions. Unfortunately, in large-scale structures, the rigorous selection of reliable sensor candidates for multi-target responses remains largely unexplored. In this paper, we propose a flexible and generalized framework for selecting the most relevant sensors to the multi-target response and predicting the target response, referred to as the Fast-aware Multi-Target Response Prediction (FMTRP) approach in the spirit of divide-and-conquer. Specifically, first, a multi-task learning module is designed to predict multi-point response tasks at the same time. Simultaneously, we meticulously devise adaptive mechanisms to facilitate loss-term reweighting and encourage prioritization of challenging tasks in multiple prediction tasks. Second, to ensure ease of interpretation, we introduce a hybrid penalty to select sensors at the group-sparsity, individual-sparsity and element-sparsity levels. Finally, due to the substantial number of candidate sensors posing a significant computational burden, we develop a more efficient search strategy and support computation to make the proposed approach applicable in practice, leading to substantial runtime improvements. Extensive experiments on aircraft standard model response datasets and large airliner test flight datasets validate the effectiveness of the proposed approach in identifying sensor locations and simultaneously predicting responses at multiple points. Compared to state-of-the-art methods, the proposed approach achieves an accuracy of over 99% in sinusoidal excitation and exhibits the shortest runtime (3.514 s).

Development of Additively Manufactured Embedded Ceramic Temperature Sensors via Vat Photopolymerization

Current additive manufacturing (AM) techniques and methods, such as liquid-crystal display (LCD) vat photopolymerization, offer a wide variety of surface-sensing solutions, but customizable internal sensing is both scarce in presence and narrow in scope. In this work, a fabrication process for novel customizable embedded ceramic temperature sensors is investigated. The fabrication techniques and materials are evaluated, followed by extensive characterization via spectral analysis and thermomechanical testing. The findings indicate that LCD-manufactured ceramic sensors exhibit promising sensing properties, including strong linear thermal sensitivity of 0.23% per °C, with an R2 of at least 0.97, and mechanical strength, with a hardness of 570 HV, making them suitable for adverse environmental conditions. This research not only advances the field of AM for sensor development but also highlights the potential of LCD technology in rapidly producing reliable and efficient ceramic temperature sensors.

A Comprehensive Review of State-of-the-art Optical Methods for Methane Gas Detection

Methane (CH<sub>₄</sub>), a potent greenhouse gas, significantly contributes to climate change and global warming. Its impact over 100 years surpasses carbon dioxide (CO<sub>₂</sub>) by 28 times. Addressing methane emissions, particularly from oil and gas production activities such as transmission pipelines, is imperative. One promising avenue is the development of reliable sensors to detect and mitigate methane leaks and prevent hazardous issues. Optical-based methods present notable advantages, including versatility and remote operation, making them pivotal in this endeavor. This review article provides a concise overview of optical-based methane identification technologies, encompassing sensing materials, absorption spectra, operational mechanisms, and recent advancements. Potential perspectives are explored, and inferences from this assessment are also derived. Emphasizing the significance of optical fiber-based methane detection methods, the authors advocate for further research to support ongoing efforts and foster innovation in this critical area.

Test-retest reliability and construct validity of Inosen’s Silat Momentum Test

Problem Statements: The kick in Pencak Silat has a magnitude called momentum, which requires a tool capable of measuring several variables based on the International System of Units (SI) in real-time. Nowadays, there are affordable and reliable sensor technologies to meet these needs. Purpose: The research aimed to design a test instrument that utilizes sensor technology to measure kick momentum in the sparring category of Pencak Silat following the applicable SI, and to test its reliability and validity. The resulting instrument is named Inosen’s Silat Momentum Test. Approach: This study followed Johnson and Nelson’s seven-test construction process. Twenty-four Pencak Silat athletes, consisting of 12 elite and 12 non-elite participants, were involved in the evaluation process. The test’s reliability was assessed by examining the correlation between the test and retest results. The construct validity test was conducted by comparing the results of the front kick tests of elite and non-elite Pencak Silat athletes. Data analysis was performed using Pearson correlation and independent t-tests using Minitab 21.4.1 software. Results: The results of the test and retest indicate a very high correlation (r = 0.923), demonstrating the consistent ability of Inosen’s Silat Momentum Test to measure kick momentum. The study found a statistically significant difference in the Inosen’s Silat Momentum Test scores (p < 0.05) between the elite fighter group (average score of 463.3 kg.m/s) and the non-elite fighter group (average score of 375 kg.m/s). The results indicate that Inosen’s Silat Momentum Test is an effective instrument for differentiating kick momentum based on fighter performance. Conclusions: Based on the findings, it is inferred that Inosen’s Silat Momentum Test is a reliable and valid instrument for measuring Pencak Silat kick momentum according to SI standards. Keywords: biomechanics, kick momentum, pencak silat, performance test, sensor technology.

Chemoresistive Gas Sensors Based on Electrospun 1D Nanostructures: Synergizing Morphology and Performance Optimization

Gas sensors are essential for safety and quality of life, with broad applications in industry, healthcare, and environmental monitoring. As urbanization and industrial activities intensify, the need for advanced air quality monitoring becomes critical, driving the demand for more sensitive, selective, and reliable sensors. Recent advances in nanotechnology, particularly 1D nanostructures like nanofibers and nanowires, have garnered significant interest due to their high surface area and improved charge transfer properties. Electrospinning stands out as a promising technique for fabricating these nanomaterials, enabling precise control over their morphology and leading to sensors with exceptional attributes, including high sensitivity, rapid response, and excellent stability in harsh conditions. This review examines the current research on chemoresistive gas sensors based on 1D nanostructures produced by electrospinning. It focuses on how the morphology and composition of these nanomaterials influence key sensor characteristics—sensitivity, selectivity, and stability. The review highlights recent advancements in sensors incorporating metal oxides, carbon nanomaterials, and conducting polymers, along with their modifications to enhance performance. It also explores the use of fiber-based composite materials for detecting oxidizing, reducing, and volatile organic compounds. These composites leverage the properties of various materials to achieve high sensitivity and selectivity, allowing for the detection of a wide range of gases in diverse conditions. The review further addresses challenges in scaling up production and suggests future research directions to overcome technological limitations and improve sensor performance for both industrial and domestic air quality monitoring applications.

Research on Highly Reliable Self-Powered Vibration Sensors for Geological Drilling

Vibration signals at the bottom of the drill string during geological drilling are crucial for lithological identification and drilling parameter optimization. However, existing downhole vibration sensors suffer from limitations in power supply and reliability. This study proposes a self-powered vibration sensor with high redundancy based on the triboelectric nanogenerator principle, which is capable of measuring both axial and transverse vibrations, thereby reducing the dependence on external power sources. The experimental results show that the sensor can measure axial vibration frequencies ranging from 0 to 11 Hz with an error of less than 4% and transverse vibration frequencies ranging from 0 to 5 Hz with an error of less than 5%. It can operate stably in temperatures from 0 to 180 °C and relative humidities from 0 to 95%. The sensor’s axial vibration measurement features six identical measurement structures, providing high redundancy and effectively enhancing its reliability. Furthermore, the sensor exhibits power generation capabilities. When an external load of 1 MΩ is applied to the axial measurement module and 10 MΩ to the transverse measurement module, the sensor achieves its maximum power output for both axial and transverse measurements, reaching 32.4 × 10−9 W and 2.1 × 10−9 W, respectively. Compared to traditional bottom-of-the-hole vibration sensors, this sensor possesses self-powering capabilities and high reliability, which can improve the operational efficiency and hold significant practical value for future applications.

Advancements in amphibious robot navigation through wheeled odometer uncertainty extension and distributed information fusion

The advancement and safeguarding of the water-land interface region is of paramount importance, and amphibious robots with the capacity for autonomous operation can play a pivotal role in this domain. However, the inability of the majority of reliable navigation sensors to adapt to the water-land interface environment presents a significant challenge for amphibious robots, as obtaining positional information is crucial for autonomous operation. To address this issue, we have proposed a positioning and navigation framework, designated as NAWR (Navigation Algorithm for Amphibious Wheeled Robots), with the objective of enhancing the navigation capabilities of amphibious robots. Firstly, a method for representing the odometer's confidence based on a simplified wheel-terrain interaction model has been developed. This method quantitatively assesses the reliability of each odometer by estimating the slip rate. Secondly, we have introduced an improved split covariance intersection filter (I-SCIF), which maximizes the utilization of navigation information sources to enhance the accuracy of positional estimation. Finally, we will integrate these two methods to form the NAWR framework and validate the effectiveness of the proposed methods through multiple robot field trials. The results from both field trials and ablation tests collectively demonstrate that the modules and overall approach within the NAWR framework effectively enhance the navigation capabilities of amphibious robots.

Anesthetic Gas Scavenging System for Gas Evacuation in the ICU.

Inhaled sedation is increasing in ICUs, with active carbon filters (ACFs) commonly used for evacuating halogenated gases. However, the potential benefits of a waste anesthetic gas system (WAGS) similar to the ones used in operating rooms should be explored. To limit the suction over the flow sensor where the WAGS is connected on ICU ventilators, an anesthetic gas receiving system (AGRS) is required, constituting with the WAGS an active gas receiving and scavenging system (AGRSS). Ensuring that this whole device does not compromise the flow sensor reliability is crucial. The aim of this study was to compare various gas evacuation devices and assess the reliability of AGRSS on ICU ventilators. In this experimental study, pressures and flows were recorded during the ventilation of a test lung using various ventilator settings and gas evacuation methods: no device (reference condition), ACF, the WAGS alone, AGRSS (WAGS and AGRS together), and the expiratory valve connected to the medical vacuum system with the AGRS in between. Visual comparisons of the pressure and flow curves followed by a statistical analysis comparing median pressures and flows of each device to the reference were performed. The test lung model demonstrated consistent comparability in pressures and flows among all devices, except for the WAGS alone, which exhibited discordance through significant overestimation or underestimation. These findings indicate that using a WAGS with the AGRS system appeared to be reliable for managing gas evacuation in ICUs without compromising pressure or flow delivery. The data from this experimental trial should be confirmed with clinical studies involving human subjects. Given the increasing use of inhaled sedation in ICUs, these results support the daily application of the WAGS with the AGRS for gas evacuation, similar to its established use in anesthesiology.

Drought tolerance and recovery capacity of two ornamental shrubs: Combining physiological and biochemical analyses with online leaf water status monitoring for the application in urban settings

When plants are transferred from nursery to urban environments, they often face drought stress due to inadequate maintenance, such as insufficient irrigation. Using drought tolerant species may help mitigate the adverse impact of drought stress in urban settings. Additionally, utilizing novel technologies for water status monitoring may help optimize irrigation schedules to prevent transplanting failures. This study investigated the physiological and biochemical responses of two ornamental shrubs, Photinia x fraseri and Viburnum tinus, subjected to water stress of increasing severity and rewatering. Water relations, gas exchanges, chlorophyll fluorescence and biochemical analyses were conducted alongside real-time monitoring of water status using leaf-water-meter sensors (LWM).The progression of water stress had a notable negative impact on leaf gas exchanges and water relations in both species. Notably, P. fraseri avoided photoinhibition by reducing chlorophyll content and actual efficiency of PSII. Adjustments in leaf phenolic compounds played a significant role in enhancing drought tolerance of both species due to their antioxidant and photoprotective properties.Upon rewatering, both species exhibited complete recovery in their physiological functions, underscoring their remarkable tolerance and resilience to drought stress. Additionally, LWM sensors efficiently tracked the dehydration levels, exhibiting a rising trend during the water stress progression and a subsequent decline after rewatering for both species. These findings confirm the reliability of LWM sensors in monitoring physiological status of plants in outdoor contexts, making them a suitable tool for use in urban settings.

Method for Distributed Node Metric Measurement in Wireless Sensor Networks Based on Multiple Attributes

Traditional sensors require large power inputs and accuracy cannot be guaranteed. In this study, we introduce a revolutionary technique to enhance the accuracy of node measurements in Wi-Fi sensor networks (WSNs) while minimizing power consumption. Our approach focuses on a mixture of key attributes, including overall strength performance, landmark strength, and node geolocation. We have set quantitative measures for these attributes, which is the foundation of our sizing method. The basic framework for evaluating node allocation indicators is superior, mainly based on the multifaceted attributes of individual nodes and adjacent nodes, promoting selection in data transmission and processing. This framework has a powerful statistical collection mechanism that can accumulate every intrinsic attribute, including external attributes such as overall power performance, signal energy, and geographic region. Using these collected records, our techniques include selecting methods for metric calculations. It is worth noting that we have merged a set of adaptive strategies that can dynamically adjust measurement parameters based on discovered community conditions. The adaptability of this strategy ensures normal operational performance in various community states, achieving a balance between measurement accuracy and energy conservation. This method ensures a significant improvement in the operational performance of wireless sensor networks and has broad applicability in the field of reliable high-intensity sensor networks.

On the Energy Behaviors of the Bellman–Ford and Dijkstra Algorithms: A Detailed Empirical Study

The Single-Source Shortest Paths (SSSP) graph problem is a fundamental computation. This study attempted to characterize concretely the energy behaviors of the two primary methods to solve it, the Bellman–Ford and Dijkstra algorithms. The very different interactions of the algorithms with the hardware may have significant implications for energy. The study was motivated by the multidisciplinary nature of the problem. Gaining better insights should help vital applications in many domains. The work used reliable embedded sensors in an HPC-class CPU to collect empirical data for a wide range of sizes for two graph cases: complete as an upper-bound case and moderately dense. The findings confirmed that Dijkstra’s algorithm is drastically more energy efficient, as expected from its decisive time complexity advantage. In terms of power draw, however, Bellman–Ford had an advantage for sizes that fit in the upper parts of the memory hierarchy (up to 2.36 W on average), with a region of near parity in both power draw and total energy budgets. This result correlated with the interaction of lighter logic and graph footprint in memory with the Level 2 cache. It should be significant for applications that rely on solving a lot of small instances since Bellman–Ford is more general and is easier to implement. It also suggests implications for the design and parallelization of the algorithms when efficiency in power draw is in mind.

Convolutional variational autoencoder and multi-scale attention convolutional neural network based diagnostics on filament current sensors for mass spectrometers

Fault detection of the filament current sensor of the spaceborne mass spectrometer is of great significance to the safe operation of the mass spectrometer. Neglecting sensor failures may affect the normal operation of the mass spectrometer, which in turn affects the health of the astronauts and the space missions. This paper proposes an enhanced intelligent diagnosis method based on filament current sensor for mass spectrometer via improved deep learning network, aiming to improve the accuracy and reliability of the filament current sensor when the fault samples are insufficient. The improved convolutional variational autoencoder (CVAE) model not only enhances the data for four typical sensor fault signals, namely bias, drift, missing and random, but also solves the problem of insufficient samples when spike, Precision degradation and stuck fault occur in sensors. Short-time Fourier transform (STFT) is utilized to transform one-dimensional data into two-dimensional spectrograms, which gives more fault characteristics to the data set. The improved multi-scale attention mechanism convolutional neural network (MSAM-CNN) model is constructed to perform fault diagnosis on the generated spectrogram, which solves the problem of low accuracy of fault identification in traditional convolutional neural network (CNN) models. The results of ablation and comparison experiments show that the samples generated by CVAE have smaller Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE), the enhanced samples improve the classification and clustering of MSAM-CNN, and compared to other diagnostic models, MSAM-CNN achieves the highest accuracy, precision, recall, and F1-score of 99.2%, 99.3%, 98.8%, and 0.991.

Long-term reliable wireless H2 gas sensor via repeatable thermal refreshing of palladium nanowire

The increasing significance of hydrogen (H2) gas as a clean energy source has prompted the development of high-performance H2 gas sensors. Palladium (Pd)-based sensors, with their advantages of selectivity, scalability, and cost-effectiveness, have shown promise in this regard. However, the long-term stability and reliability of Pd-based sensors remain a challenge. This study not only identifies the exact cause for performance degradation in palladium (Pd) nanowire H2 sensors, but also implements and optimizes a cost-effective recovery method. The results from density functional theory (DFT) calculations and material analysis confirm the presence of C = O bonds, indicating performance degradation due to carbon dioxide (CO2) accumulation on the Pd surface. Based on the molecular behavior calculation in high temperatures, we optimized the thermal treatment method of 200 °C for 10 minutes to remove the C = O contaminants, resulting in nearly 100% recovery of the sensor’s initial performance even after 2 months of contamination.

Development of 2D Ir-DMG nanosheets as a colorimetric sensor probe for Ni (II) sensing and a highly sensitive, reliable, and portable colorimetric sensor device for environmental analysis

The era of nanomaterials made a revolutionary change in colorimetric sensing with ultra-high sensitivity, improved reactivity, and enhanced photoactivity. The first-ever novel 2D metal–organic nanosheets were synthesized using IrCl3 and Dimethylglyoxime (DMG) by probe ultrasonication (PUS) followed by a solvothermal wet-chemical approach. This material has shown a rapid color change from yellow to crimson red with Ni (II) after the formation of complex. The UV–visible absorption spectra are the conventional methodology for colorimetric sensors and here, it was given a perfect linear relationship with an R2 of 0.99 and an LOD of 1.60 µM (0.1 ppm). The average calculated molar extinction coefficient for this system was 1889.30 M−1 cm−1. This is comparatively high absorptivity value. In addition, a novel Arduino-based colorimetric sensor device and corresponding software were developed under the name of “Chrom Metrics”. This Arduino device is unique since it can sense all wavelengths and the combined RGB delta E values. Therefore, it can provide more information/rationale for colorimetry than other devices/methods. The same Ir-DMG & Ni (II) system showed a perfect linear relationship with an R2 of 0.98 and a LOD of 0.85 µM (0.05 ppm) by the data obtained from this sensor device. Thus, this new device is easier and more accurate, highly efficient, rapid, highly selective, and sensitive.

Self-assembled conductive coating and bifacial microstructures Collaboratively Adjusting the sensing performance and high reliability of the flexible pressure sensors

Flexible pressure sensors are being studied intensively for applications such as smart wearables and health monitoring. While ensuring the excellent sensing performance, the reliability (durability and environmental resistance) of the sensors are also key indicators that determine whether they can be used in practice. In this research, a strategy to adjust the sensing performance and high reliability of the flexible pressure sensors by self-assembled conductive coating and bifacial microstructures (BM) is proposed. Polydimethylsiloxane/carbon nanotube films with BM are first modified to reduce the surface contact angle. Then poly(dimethyl diallyl ammonium chloride) (PDAC) and hydroxylated carbon nanotubes/carboxylated cellulose nanofibers/glycerol (CNT-OH/CNF-C/GL) are successively assembled on the BM surface to construct conductive coatings (PCCG). The sensor based on PCCG and BM (the PCCG-BM sensor) shows a high sensitivity (9.97 kPa−1), a wide detection range (0–330 kPa), and a fast response time (59 ms). The PCCG-BM sensor has excellent repeatability, and it still outputs stable current change signals after 240,000 pressure cycles. What’s more, the PCCG-BM sensor is treated with extreme environments such as bending, stretching, washing, and high pressure, and the sensor still maintains excellent performances. This preparation strategy provides a new perspective for the fabrication of high-performance and high-reliability flexible sensors.