Technique For Continuous Monitoring Research Articles

Evolving cybersecurity frontiers: A comprehensive survey on concept drift and feature dynamics aware machine and deep learning in intrusion detection systems

Intrusion Detection Systems (IDS) have become pivotal in safeguarding information systems against evolving threats. Concurrently, Concept Drift presents a significant challenge in machine learning, affecting the adaptability and accuracy of predictive models in dynamic environments. Understanding the synergy between IDS and Concept Drift is crucial for developing robust security systems. The motivation behind this survey is driven by the emerging complexities in cyber threats and the dynamic nature of data streams, which necessitate advanced IDS capable of adapting to Concept and Feature Drift. Our analysis reveals a glaring omission in the existing literature—the integration of Concept Drift and Feature Drift within IDS. Most studies have focused on Concept Drift in a general context or on IDS but have yet to comprehensively consider the implications of data dynamics. This oversight has led to a fragmented understanding and suboptimal approaches to tackling modern cyber threats. To address this, we propose a comprehensive review that delves into the role of machine learning in IDS, explicitly focusing on Concept and Feature Drift. We have proposed a framework that includes all the necessary components for a drift-aware IDS. The framework incorporates dynamic feature selection, adaptive learning algorithms, and continuous monitoring techniques to handle Concept Drift and Feature Drift effectively. The survey highlights state-of-the-art methodologies and current challenges in integrating these concepts. The methodology involves an exhaustive analysis of published works from 2019 to 2024, comparing and contrasting various models and approaches. This includes a detailed examination of Concept Drift-aware IDS methods, dynamic feature selection techniques, and the impact of high dimensionality in IDS. These quantitative improvements underscore the necessity for developing adaptive and resilient IDS. The survey uncovers under-represented areas in current research, paving the way for future investigations. By highlighting these gaps and providing comparative data, the survey sets a clear direction for upcoming research efforts to foster the development of more dynamic and adaptable IDS solutions. The quantitative experimental evaluation of the proposed framework is planned to be conducted in a future article, where we will assess its effectiveness and performance in real-world scenarios.

On-Site Sensor Sensitivity Adjustment Technique for a Maintenance-Free Heat Flow Monitoring in Building Systems

In this paper, an advanced solution for measuring heat flow through opaque building elements is presented. The solution is based on the implementation of a computer-aided technique for continuous monitoring of heat flow sensor performance and silent checking of their accuracy. In principle, the technique provides an ex-post compensation of potential deviations and inaccuracies detected during the measurement, which can be done without interfering with the ongoing experiment. As a consequence, traditional ‘non-smart’ sensors can be turned into advanced sensors with self-sensing or self-adjustment features at nearly zero additional costs. The high efficiency of the proposed approach was validated against experimental data obtained from an independent set of advanced high-sensitive sensors. Considering the validation results, the proposed technique brings an entirely new potential for maintenance-free applications for thermal performance monitoring in the building sector, typically for long-term experiments or measurements under dynamic environments.

Influence of Grease Contamination on Acoustic Emission Monitoring of Low-speed Roller Bearings

Large-scale low-speed rolling element bearings form crucial connections in offshore installations such as heavy-lifting cranes, single point mooring systems, and wind turbines. Due to the stochastic nature of wind and waves, the applied loads on these bearings are hardly predictable. Furthermore, the remote nature of the offshore environment requires a high standard of operational safety and reliability, reinforcing the need for advanced inspection and monitoring methods. Acoustic emission (AE) is a continuous monitoring technique for condition assessment of low-speed bearings, as it may detect the stress waves associated with developing degradation within the rolling elements. This paper presents an investigation on the influence of grease contamination on acoustic emission (AE) monitoring of low-speed bearings. It discusses several experiments that have been conducted in various regimes of particle contaminated lubrication, and proposes a strategy for condition monitoring. The presented experiments have been conducted with differing bearing geometries in multiple testing environments. The contamination particles have been both naturally developed and artificially introduced. The results total of 9 experimental cases are discussed and compared. All experiments follow the same data-acquisition principles, utilising arrays of three AE transducers sensitive in a range between 40-580 kHz. Data is recorded while operating the bearing at low speed under load. Abrasive wear of the rolling elements and crushing of the particles are concluded to be the main sources of AE due to particle contamination. Processing consists of waveform cross-correlation clustering and feature analysis. The results suggest that hard abrasive particles make significant contribution to the AE signals, which is associated with abrasive wear of the rollers and raceways. Comparisons with the contribution of softer and finer particles are presented as well.

Enabling Non-invasive Tracking of Vascular Endothelial Cells Derived from Induced Pluripotent Stem Cells Using Nuclear Imaging.

The absence of clinically applicable imaging techniques for continuous monitoring of transplanted cells poses a significant obstacle to the clinical translation of stem cell-based therapies for vascular regeneration. This study aims to optimize a clinically applicable, non-invasive imaging technique to longitudinally monitor vascular endothelial cells (ECs) for vascular regeneration in peripheral artery disease (PAD). Human induced pluripotent stem cells (HiPSCs) were employed to generate ECs (HiPSC-ECs). Lentiviral vectors encoding human sodium iodide symporter (hNIS) and enhanced green fluorescent protein (eGFP) genes were introduced to HiPSCs and HiPSC-ECs at varying multiplicities of infection (MOI). Through a combination of fluorescence microscopy and flow cytometry, an optimized transduction technique for introducing hNIS-eGFP into HiPSC-ECs was established. Subsequently, single-photon emission computed tomography (SPECT) was utilized for imaging of the transduced cells in vitro and in vivo after transplantation into the gastrocnemius muscle of nude mice. Lentiviral transduction resulted in sustained co-expression of hNIS and eGFP in HiPSC-ECs when transduced post-endothelial differentiation. An optimal MOI of five yielded over 90% hNIS-eGFP expression efficiency without compromising cell viability. hNIS-eGFP+ HiPSC-ECs exhibited 99mTc uptake and were detectable through SPECT in vitro. Additionally, intramuscular injection of hNIS-eGFP+ HiPSC-ECs with MatrigelTM into the hindlimbs of nude mice enabled real-time SPECT/CT tracking, from which a reduction in signal exceeding 80% was observed within 7days. This study establishes an optimized cell modification and imaging protocol for tracking transplanted cells. Future efforts will focus on enhancing cell survival and integration via improved delivery systems, thereby advancing the potential of cell-based therapies for PAD.

Technique for intraoperative monitoring of corticospinal tract integrity using an electrode with a dynamic balloon

Aim. To present a technique for continuous monitoring of corticospinal tract integrity using an electrode with a dynamic balloon, examine the advantages and disadvantages of the selected and alternative monitoring techniques.Materials and methods. At the 1st stage, an electrode combined with a dynamic balloon was prepared. At the 2nd stage, continuous monitoring of the corticospinal tract using direct stimulation of the cortex using ballon grid during the main stage of cerebral surgery was performed. At the 3rd stage, interpretation of the obtained neurophysiological responses was performed.Results. The presented technique of continuous monitoring of the integrity of the corticospinal tract through direct cortical stimulation using the developed device prevents false response decreases which significantly helps with the interpretation of the obtained results and increases information value of the technique. The described technique is based on the presented clinical observations of intracerebral tumor resection, as well as microsurgical aneurysm clipping using the technique. During surgery, no false decrease of the neurophysiological signal amplitude was observed due to dynamic ballon inflation and maintenance of close contact between the electrode and the surface of the brain. A surgery performed using this technique allowed to achieve a favorable neurological outcome in a patient in the postoperative period.Conclusion. The presented device allows to perform continuous neuromonitoring using direct cortical electrostimulation with a strip electrode at any level of brain retraction. The technique decreases the risk of primary and secondary injury of the corticospinal and corticobulbar tracts which increases safety of neurosurgical intervention and decreases risks of neurological complications.

Fuzzy Approach for Managing Renewable Energy Flows for DC-Microgrid with Composite PV-WT Generators and Energy Storage System

Recently, the implementation of software/hardware systems based on advanced artificial intelligence techniques for continuous monitoring of the electrical parameters of intelligent networks aimed at managing and controlling energy consumption has been of great interest. The contribution of this paper, starting from a recently studied DC-MG, fits into this context by proposing an intuitionistic fuzzy Takagi–Sugeno approach optimized for the energy management of isolated direct current microgrid systems consisting of a photovoltaic and a wind source. Furthermore, a lead-acid battery guarantees the stability of the DC bus while a hydrogen cell ensures the reliability of the system by avoiding blackout conditions and increasing interaction with the loads. The fuzzy rule bank, initially built using the expert’s knowledge, is optimized with the aforementioned procedure, maximizing external energy and minimizing consumption. The complete scheme, modeled using MatLab/Simulink, highlighted performance comparable to fuzzy Takagi–Sugeno systems optimized using a hybrid approach based on particle swarm optimization (to structure the antecedents of the rules) and minimum batch squares (to optimize the output).

Reservoir geophysical monitoring supported by artificial general intelligence and Q-Learning for oil production optimization

<p>Artificial general intelligence (AGI), or strong AI, aims to replicate human-like cognitive abilities across diverse tasks and domains, demonstrating adaptability and learning like human intelligence. In contrast, weak AI refers to systems designed for specific tasks, lacking the broad cognitive flexibility of AGI. This paper introduces a novel approach to optimize oil production by integrating fundamental principles of AGI with geophysical data inversion and continuous monitoring techniques. Specifically, the study explored how AGI-inspired algorithms, combined with established reinforcement learning (RL) techniques, can enhance borehole electric/electromagnetic monitoring and reservoir fluid mapping technology. This integration aims to mitigate the risk of unwanted water invasion in production wells while optimizing oil extraction. The proposed methodology leverages real-time geophysical data analysis and automated regulation of oil production. The paper begins by outlining the key features of AGI and RL, and then discusses their application in electric/electromagnetic monitoring to define optimal production policies. The effectiveness of this approach was verified through synthetic tests, showing significant improvements in production efficiency, resource recovery, and environmental impact reduction.</p>

Towards continuous monitoring of TNF-α at picomolar concentrations using biosensing by particle motion

The ability to continuously monitor cytokines is desirable for fundamental research studies and healthcare applications. Cytokine release is characterized by picomolar circulating concentrations, short half-lives, and rapid peak times. Here, we describe the characteristics and feasibility of a particle-based biosensing technique for continuous monitoring of TNF-α at picomolar concentrations. The technique is based on the optical tracking of particle motion and uses an antibody sandwich configuration. Experimental results show how the analyte concentration influences the particle diffusivity and characteristic response time of the sensor, and how the sensitivity range depends on the antibody functionalization density. Furthermore, the data clarifies how antibodies supplemented in solution can shorten the characteristic response time. Finally, we demonstrate association rate-based sensing as a first step towards continuous monitoring of picomolar TNF-α concentrations, over a period of 2 h with delay times under 15 min. The insights from this research will enable the development of continuous monitoring sensors using high-affinity binders, providing the sensitivity and speed needed in applications like cytokine monitoring.

A systematic review on the assessment of cerebral autoregulation in patients with Large Vessel Occlusion.

As the majority of large vessel occlusion (LVO) patients are not treated with revascularization therapies or efficiently revascularized, complementary management strategies are needed. In this article we explore the importance of cerebral autoregulation (CA) assessment in the prediction and/or modification of infarct growth and hemorrhagic transformation. In patients with LVO, these are important factors that affect prognosis. A systematic search of the PubMed, EMBASE databases and a targeted Google search was conducted, resulting in the inclusion of 34 relevant articles. There is an agreement that CA is impaired in patients with LVO; several factors have been identified such as time course, revascularization status, laterality, disease subtype and location, some of which may be potentially modifiable and affect outcomes. The personalized CA assessment of these patients suggests potential for better understanding of the inter-individual variability. Further research is needed for the development of more accurate, noninvasive techniques for continuous monitoring and personalized thresholds for CA.

Guidance on left bundle branch pacing using continuous pacing technique and changes in lead V1 characteristics under real-time monitoring.

The changes in the morphology and characteristics of the V1 leads during left bundle branch capturing still need to be fully understood. This study aims to provide some suggestions about the LBB capture process through the morphology and characteristics of the V1 lead. LBBP using the continuous pacing and morphology monitoring technique during screw-in using a revolving connector (John Jiang's connecting cable). The morphology and features of V1 leads are recorded by continuous monitoring technology. The most common morphology in the LVSP stage is QR, while in the NS-LBBP (low output) stage and the NS-LBBP (lower output) stage, it is rSR. In the S-LBBP stage, it is rsR. The predominant morphology is with r/R waves in S-LBBP, which includes variations like rSR, rsR, rSr, rsr, rR, rs, rS, and R type, making up 96.7% of the total. The r waves in lead V1 are associated with agitated myocardium conducted from the left bundle branch. The initial r-wave in lead V1 may be a marker during the follow-up of patients with selective LBB capture.

Non-Invasive Continuous Optical Monitoring of Cerebral Blood Flow after Traumatic Brain Injury in Mice Using Fiber Camera-Based Speckle Contrast Optical Spectroscopy.

Neurocritical care focuses on monitoring cerebral blood flow (CBF) to prevent secondary brain injuries before damage becomes irreversible. Thus, there is a critical unmet need for continuous neuromonitoring methods to quantify CBF within the vulnerable cortex continuously and non-invasively. Animal models and imaging biomarkers can provide valuable insights into the mechanisms and kinetics of head injury, as well as insights for potential treatment strategies. For this purpose, we implemented an optical technique for continuous monitoring of blood flow changes after a closed head injury in a mouse model, which is based on laser speckle contrast imaging and a fiber camera-based approach. Our results indicate a significant decrease (~10%, p-value < 0.05) in blood flow within 30 min of a closed head injury. Furthermore, the low-frequency oscillation analysis also indicated much lower power in the trauma group compared to the control group. Overall, blood flow has the potential to be a biomarker for head injuries in the early phase of a trauma, and the system is useful for continuous monitoring with the potential for clinical translation.

Autonomous fault detection and diagnosis for permanent magnet synchronous motors using combined variational mode decomposition, the Hilbert-Huang transform, and a convolutional neural network

The continuous and online monitoring of the condition of electrical machines is key to their safe operation. This study introduces a novel fault detection and diagnosis technique for continuous monitoring of faults in permanent magnet synchronous motors (PMSM). The proposed method relies solely on built-in sensors (stator phase currents only) to detect three types of faults: inter-turn short circuit, partial demagnetisation, and static eccentricity. Our fault detection and diagnosis strategy was developed by combining variational mode decomposition (VMD), the Hilbert-Huang transform (HHT) and a convolutional neural network (CNN). The VMD is first applied to the stator phase current signals to analyse the characteristic behaviour of the current signals by decomposing the current signals into several intrinsic mode functions. The intrinsic mode functions of the healthy and faulty signals are compared, and that with the frequency shift characteristics is selected. HHT is then applied to extract the fault feature by calculating the instantaneous frequency. Finally, the instantaneous frequency feature is fed into the CNN, which is designed to detect and classify motor faults. Experimental results clearly show that the variation of the instantaneous frequency of the PMSM, working at different operating states, can be utilised for condition monitoring and fault detection.

Improved Electrical Impedance Tomography Reconstruction via a Bayesian Approach With an Anatomical Statistical Shape Model.

electrical impedance tomography (EIT) is a promising technique for rapid and continuous bedside monitoring of lung function. Accurate and reliable EIT reconstruction of ventilation requires patient-specific shape information. However, this shape information is often not available and current EIT reconstruction methods typically have limited spatial fidelity. This study sought to develop a statistical shape model (SSM) of the torso and lungs and evaluate whether patient-specific predictions of torso and lung shape could enhance EIT reconstructions in a Bayesian framework. torso and lung finite element surface meshes were fitted to computed tomography data from 81 participants, and a SSM was generated using principal component analysis and regression analyses. Predicted shapes were implemented in a Bayesian EIT framework and were quantitatively compared to generic reconstruction methods. Five principal shape modes explained 38% of the cohort variance in lung and torso geometry, and regression analysis yielded nine total anthropometrics and pulmonary function metrics that significantly predicted these shape modes. Incorporation of SSM-derived structural information enhanced the accuracy and reliability of the EIT reconstruction as compared to generic reconstructions, demonstrated by reduced relative error, total variation, and Mahalanobis distance. As compared to deterministic approaches, Bayesian EIT afforded more reliable quantitative and visual interpretation of the reconstructed ventilation distribution. However, no conclusive improvement of reconstruction performance using patient specific structural information was observed as compared to the mean shape of the SSM. The presented Bayesian framework builds towards a more accurate and reliable method for ventilation monitoring via EIT.

Longitudinal Studies of Wearables in Patients with Diabetes: Key Issues and Solutions.

Glucose monitoring is key to the management of diabetes mellitus to maintain optimal glucose control whilst avoiding hypoglycemia. Non-invasive continuous glucose monitoring techniques have evolved considerably to replace finger prick testing, but still require sensor insertion. Physiological variables, such as heart rate and pulse pressure, change with blood glucose, especially during hypoglycemia, and could be used to predict hypoglycemia. To validate this approach, clinical studies that contemporaneously acquire physiological and continuous glucose variables are required. In this work, we provide insights from a clinical study undertaken to study the relationship between physiological variables obtained from a number of wearables and glucose levels. The clinical study included three screening tests to assess neuropathy and acquired data using wearable devices from 60 participants for four days. We highlight the challenges and provide recommendations to mitigate issues that may impact the validity of data capture to enable a valid interpretation of the outcomes.

Assessment of soil loss using RUSLE around Mongolian mining sites: a case study on soil erosion at the Baganuur lignite and Erdenet copper–molybdenum mines

Mining constitutes an integral part of Mongolia’s national economy and dominates the country’s export revenue. At the same time, a wide range of mining impacts on soil, water resources, the atmosphere and the biosphere have been documented across the country. This case study addresses the long-term soil degradation around two mining sites located in the semi-arid steppe zone of Mongolia: the open-cast lignite mine of Baganuur about 140 km east of Ulaanbaatar, and the open-pit copper–molybdenum mine of Erdenet about 240 km northwest of Ulaanbaatar, both of which started commercial extraction in the late 1970s. For the assessment of soil erosion, the RUSLE model was applied in different seasons for the period from 1989 to 2018 at 3-year intervals, considering both climatic variation and the expansion of the mines based on maps and satellite imagery. Rainfall erosivity was identified as the most dominant factor driving soil erosion in the study regions, with mining leading to local increases in soil erodibility. The highest soil erosion rates were found in both areas in July 2018, reaching 7.88 t ha–1 month–1 in the Erdenet area and 9.46 t ha–1 month–1 in the Baganuur area. The spatial patterns of soil erosion showed higher soil loss rates were in the vicinity of the mines and adjoining industrial sites. Particularly high soil losses were identified in July 1998, July and August in 2013 and July 2018 in both mining areas. The combination of the RUSLE model, remote sensing and ground truth data as and their processing by GIS was found to be a time-saving and cost-effective technique for continuous monitoring of soil erosion and planning of preventive measures in and around mining areas.

Tumor cell membrane-coated continuous electrochemical sensor for GLUT1 inhibitor screening

Glucose transporter 1 (GLUT1) overexpression in tumor cells is a potential target for drug therapy, but few studies have reported screening GLUT1 inhibitors from natural or synthetic compounds. With current analysis techniques, it is difficult to accurately monitor the GLUT1 inhibitory effect of drug molecules in real-time. We developed a cell membrane-based glucose sensor (CMGS) that integrated a hydrogel electrode with tumor cell membranes to monitor GLUT1 transmembrane transport and screen for GLUT1 inhibitors in traditional Chinese medicines (TCMs). CMGS is compatible with cell membranes of various origins, including different types of tumors and cell lines with GLUT1 expression knocked down by small interfering RNA or small molecules. Based on CMGS continuous monitoring technique, we investigated the glucose transport kinetics of cell membranes with varying levels of GLUT1 expression. We used CMGS to determine the GLUT1-inhibitory effects of drug monomers with similar structures from Scutellaria baicalensis and catechins families. Results were consistent with those of the cellular glucose uptake test and molecular-docking simulation. CMGS could accurately screen drug molecules in TCMs that inhibit GLUT1, providing a new strategy for studying transmembrane protein-receptor interactions.

Capturing the Dynamics of Trust and Team Processes in Human-Human-Agent Teams via Multidimensional Neural Recurrence Analyses

As collaborative technologies evolve from supportive tools to interactive teammates, there is a growing need to understand how trust and team processes develop in human-agent teams. To contribute effectively, these systems must be able to support human teammates in a task without disrupting the delicate interpersonal states and team processes that govern successful collaboration. In order to break down the complexity of monitoring multiple actors in human-agent collaborations, there is a need to identify interpretable, generalizable measures that can monitor the emergence of interpersonal and team-level processes that underlie effective teaming. We address this gap by using functional Near-Infrared Spectroscopy to concurrently measure brain activity of two individuals in a human-human-agent team during a complex, ecologically valid collaborative task, with a goal of identifying quantitative markers of cognition- and affect-based trust alongside team processes of coordination, strategy formulation, and affect management. Two multidimensional extensions of recurrence quantification analysis, a nonlinear method based in dynamical systems theory, are presented to quantify interpersonal coupling and team-level regularity as reflected in the hemodynamics of three cortical regions across multiple time-scales. Mixed-effects regressions reveal that neural recurrence between individuals uniquely reflects changes in self-reported trust, while team-level neural regularity inversely predicts self-reported team processes. Additionally, we show that recurrence metrics capture temporal dynamics of affect-based trust consistent with existing theory, showcasing the interpretability and specificity of these metrics for disentangling complex team states and processes. This paper presents a novel, interpretable, and computationally efficient model-free method capable of differentiating between latent trust and team processes a complex, naturalistic task setting. We discuss the potential applications of this technique for continuous monitoring of team states, providing clear targets for the future development of adaptive human-agent teaming systems.

Real-Time Monitoring Using Multiplexed Multi-Electrode Bioelectrical Impedance Spectroscopy for the Stratification of Vascularized Composite Allografts: A Perspective on Predictive Analytics.

Vascularized composite allotransplantation addresses injuries to complex anatomical structures such as the face, hand, and abdominal wall. Prolonged static cold storage of vascularized composite allografts (VCA) incurs damage and imposes transportation limits to their viability and availability. Tissue ischemia, the major clinical indication, is strongly correlated with negative transplantation outcomes. Machine perfusion and normothermia can extend preservation times. This perspective introduces multiplexed multi-electrode bioimpedance spectroscopy (MMBIS), an established bioanalytical method to quantify the interaction of the electrical current with tissue components, capable of measuring tissue edema, as a quantitative, noninvasive, real-time, continuous monitoring technique to provide crucially needed assessment of graft preservation efficacy and viability. MMBIS must be developed, and appropriate models explored to address the highly complex multi-tissue structures and time-temperature changes of VCA. Combined with artificial intelligence (AI), MMBIS can serve to stratify allografts for improvement in transplantation outcomes.

Online SFRA for Reliability of Power Systems: Characterization of a Batch of Healthy and Damaged Induction Motors for Predictive Maintenance

Asynchronous motors represent a large percentage of motors used in the electrical industry. Suitable predictive maintenance techniques are strongly required when these motors are critical in their operations. Continuous non-invasive monitoring techniques can be investigated to avoid the disconnection of the motors under test and service interruption. This paper proposes an innovative predictive monitoring system based on the online sweep frequency response analysis (SFRA) technique. The testing system applies variable frequency sinusoidal signals to the motors and then acquires and processes the applied and response signals in the frequency domain. In the literature, SFRA has been applied to power transformers and electric motors switched off and disconnected from the main grid. The approach described in this work is innovative. Coupling circuits allow for the injection and acquisition of the signals, while grids feed the motors. A comparison between the transfer functions (TFs) of healthy motors and those with slight damage was performed with a batch of 1.5 kW, four-pole induction motors to investigate the technique's performance. The results show that the online SFRA could be of interest for monitoring induction motors' health conditions, especially for mission-critical and safety-critical applications. The overall cost of the whole testing system, including the coupling filters and cables, is less than EUR 400.

Continuous Glucose Monitoring in Hypoxic Environments Based on Water Splitting-Assisted Electrocatalysis

Conventional enzyme-based continuous glucose sensors in interstitial fluid usually rely on dissolved oxygen as the electron-transfer mediator to bring electrons from oxidase to electrode while generating hydrogen peroxide. This may lead to several problems. First, the sensor may provide biased detection results owing to fluctuation of oxygen in interstitial fluid. Second, the polymer coatings that regulate the glucose/oxygen ratio can affect the dynamic response of the sensor. Third, the glucose oxidation reaction continuously produces corrosive hydrogen peroxide, which may compromise the long-term stability of the sensor. Here, we introduce an oxygen-independent nonenzymatic glucose sensor based on water splitting-assisted electrocatalysis for continuous glucose monitoring. For the water splitting reaction (i.e., hydrogen evolution reaction), a negative pretreatment potential is applied to produce a localized alkaline condition at the surface of the working electrode for subsequent nonenzymatic electrocatalytic oxidation of glucose. The reaction process does not require the participation of oxygen; therefore, the problems caused by oxygen can be avoided. The nonenzymatic sensor exhibits acceptable sensitivity, reliability, and biocompatibility for continuous glucose monitoring in hypoxic environments, as shown by the in vitro and in vivo measurements. Therefore, we believe that it is a promising technique for continuous glucose monitoring, especially for clinically hypoxic patients.