Seamless Monitoring Research Articles

Reliable Contactless Monitoring of Heart Rate, Breathing Rate, and Breathing Disturbance During Sleep in Aging: Digital Health Technology Evaluation Study.

Longitudinal monitoring of vital signs provides a method for identifying changes to general health in an individual, particularly in older adults. The nocturnal sleep period provides a convenient opportunity to assess vital signs. Contactless technologies that can be embedded into the bedroom environment are unintrusive and burdenless and have the potential to enable seamless monitoring of vital signs. To realize this potential, these technologies need to be evaluated against gold standard measures and in relevant populations. We aimed to evaluate the accuracy of heart rate and breathing rate measurements of 3 contactless technologies (2 undermattress trackers, Withings Sleep Analyzer [WSA] and Emfit QS [Emfit]; and a bedside radar, Somnofy) in a sleep laboratory environment and assess their potential to capture vital signs in a real-world setting. Data were collected from 35 community-dwelling older adults aged between 65 and 83 (mean 70.8, SD 4.9) years (men: n=21, 60%) during a 1-night clinical polysomnography (PSG) test in a sleep laboratory, preceded by 7 to 14 days of data collection at home. Several of the participants (20/35, 57%) had health conditions, including type 2 diabetes, hypertension, obesity, and arthritis, and 49% (17) had moderate to severe sleep apnea, while 29% (n=10) had periodic leg movement disorder. The undermattress trackers provided estimates of both heart rate and breathing rate, while the bedside radar provided only the breathing rate. The accuracy of the heart rate and breathing rate estimated by the devices was compared with PSG electrocardiogram-derived heart rate (beats per minute) and respiratory inductance plethysmography thorax-derived breathing rate (cycles per minute), respectively. We also evaluated breathing disturbance indexes of snoring and the apnea-hypopnea index, available from the WSA. All 3 contactless technologies provided acceptable accuracy in estimating heart rate (mean absolute error <2.12 beats per minute and mean absolute percentage error <5%) and breathing rate (mean absolute error ≤1.6 cycles per minute and mean absolute percentage error <12%) at 1-minute resolution. All 3 contactless technologies were able to capture changes in heart rate and breathing rate across the sleep period. The WSA snoring and breathing disturbance estimates were also accurate compared with PSG estimates (WSA snore: r2=0.76; P<.001; WSA apnea-hypopnea index: r2=0.59; P<.001). Contactless technologies offer an unintrusive alternative to conventional wearable technologies for reliable monitoring of heart rate, breathing rate, and sleep apnea in community-dwelling older adults at scale. They enable the assessment of night-to-night variation in these vital signs, which may allow the identification of acute changes in health, and longitudinal monitoring, which may provide insight into health trajectories. RR2-10.3390/clockssleep6010010.

(Invited) 3D Printing for Tissue Scaffolds with Tunable Mechanical Durability and Sensing Capabilities

Pelvic organ prolapse (POP) is a prevalent condition among senior women, characterized by the descent or herniation of pelvic organs into the vaginal canal. It is a widespread issue, affecting a significant portion of the elderly female population, and yet, effective medical solutions for this condition remain elusive. This can be attributed to the complex nature of POP, where multiple factors, including tissue weakening and structural changes, contribute to its development. Despite the significant impact on women's quality of life, the absence of comprehensive treatment options underscores the urgency for innovative approaches to address this pressing health concern. In this context, 3D printing emerges as a valuable rapid prototyping tool, offering unique capabilities for designing and fabricating smart sensors and actuators. Its precision and versatility enable the creation of intricate devices that can be customized to specific medical applications. Moreover, 3D printing allows for the integration of sensors directly into biomedical devices, facilitating seamless monitoring and feedback mechanisms. Leveraging this technology, the research endeavors to harness the potential of 3D printing to develop advanced thermoelectric devices that can be seamlessly embedded within POP tissue scaffolds. These innovative devices will serve a dual purpose—detecting the health status of the tissue scaffolds and assessing their mechanical durability. In the realm of embedded medical devices, self-powered sensors hold particular significance due to their wireless powering capabilities and in-situ monitoring capabilities of human health. These sensors can operate without the need for external power sources or frequent battery replacements, making them ideal for long-term implantation within the human body. By tapping into the body's own energy sources or ambient environmental factors, self-powered sensors ensure continuous data collection and transmission, enhancing the efficiency and reliability of healthcare monitoring systems. Consequently, this talk aims to demonstrate the feasibility of 3D-printed thermoelectric devices as self-powered sensors, integrated into POP tissue scaffolds. These innovative devices will play a pivotal role in monitoring the tissue scaffold's condition, ensuring its mechanical integrity, and ultimately contributing to improved healthcare outcomes for women affected by POP.

Brainwaves in the Cloud: Cognitive Workload Monitoring Using Deep Gated Neural Network and Industrial Internet of Things

Monitoring and classifying cognitive workload in real time is vital for optimizing human–machine interactions and enhancing performance while ensuring safety, particularly in industrial scenarios. Considering this significance, the authors aim to formulate a cognitive workload monitoring system (CWMS) by leveraging the deep gated neural network (DGNN), a hybrid model integrating bi-directional long short-term memory (Bi-LSTM) and gated recurrent unit (GRU) networks. In our experimental setup, each of the four virtual users is equipped with a Raspberry Pi Zero W module to ensure efficient data transmission, thereby enhancing the reliability and efficacy of the monitoring process. This seamless monitoring framework utilizes the constrained application protocol (CoAP) and the Things Board platform to evaluate cognitive workload in real time. The most popular EEG benchmark dataset, the STEW is utilized for workload classification in this study. We employ the short-time Fourier transformation (STFT) to extract frequency bands corresponding to users in both high and low cognitive workload modes. The proposed DGNN models achieve a perfect accuracy of 99.45%, outperforming every previous state-of-the-art model. We meticulously monitored critical parameters, including latency, classification processing time, and cognitive workload levels. This research demonstrates the importance of continuous monitoring for increasing productivity and safety in industries by introducing a novel method of real-time cognitive workload monitoring. The implementation codes for each experiment are documented and made available for reproducibility.

Exploring electrical transport in thin film and bulk thermoelectric materials with an automated Seebeck coefficient and resistivity measurement platform

Accurate assessment of electrical properties, particularly the Seebeck coefficient and electrical resistivity, is critical in thermoelectric energy harvesting research, which involves converting thermal energy into electricity. This study presents an innovative automated system designed for the simultaneous measurement of the Seebeck coefficient and electrical resistivity as a function of temperature in both film and bulk samples. The system exhibits high precision across a wide range of electrical resistance, from milli Ohm (mΩ) to giga Ohm (GΩ). Furthermore, and in contrast with commercial systems, it provides excellent flexibility in accommodating samples with diverse lateral dimensions and thicknesses, ranging from a few millimetres to several centimetres. The measurement principle is based on analysing the current–voltage (I-V) characteristic of the samples. To ensure seamless monitoring and control of the measurement process, a user-friendly and adaptable Graphical User Interface (GUI) is integrated into the system, enhancing its overall user-friendliness and convenience.

Transfer Learning-Based Specific Emitter Identification for ADS-B over Satellite System

In future aviation surveillance, the demand for higher real-time updates for global flights can be met by deploying automatic dependent surveillance–broadcast (ADS-B) receivers on low Earth orbit satellites, capitalizing on their global coverage and terrain-independent capabilities for seamless monitoring. Specific emitter identification (SEI) leverages the distinctive features of ADS-B data. High data collection and annotation costs, along with limited dataset size, can lead to overfitting during training and low model recognition accuracy. Transfer learning, which does not require source and target domain data to share the same distribution, significantly reduces the sensitivity of traditional models to data volume and distribution. It can also address issues related to the incompleteness and inadequacy of communication emitter datasets. This paper proposes a distributed sensor system based on transfer learning to address the specific emitter identification. Firstly, signal fingerprint features are extracted using a bispectrum transform (BST) to train a convolutional neural network (CNN) preliminarily. Decision fusion is employed to tackle the challenges of the distributed system. Subsequently, a transfer learning strategy is employed, incorporating frozen model parameters, maximum mean discrepancy (MMD), and classification error measures to reduce the disparity between the target and source domains. A hyperbolic space module is introduced before the output layer to enhance the expressive capacity and data information extraction. After iterative training, the transfer learning model is obtained. Simulation results confirm that this method enhances model generalization, addresses the issue of slow convergence, and leads to improved training accuracy.

Computed tomography technologies to measure key structural features of polymeric biomedical implants from bench to bedside.

Implanted polymeric devices, designed to encourage tissue regeneration, require porosity. However, characterizing porosity, which affects many functional device properties, is non-trivial. Computed tomography (CT) is a quick, versatile, and non-destructive way to gain 3D structural information, yet various CT technologies, such as benchtop, preclinical and clinical systems, all have different capabilities. As system capabilities determine the structural information that can be obtained, seamless monitoring of key device features through all stages of clinical translation must be engineered intentionally. Therefore, in this study we tested feasibility of obtaining structural information in pre-clinical systems and high-resolution micro-CT (μCT) under physiological conditions. To overcome the low CT contrast of polymers in hydrated environments, radiopaque nanoparticle contrast agent was incorporated into porous devices. The size of resolved features in porous structures is highly dependent on the resolution (voxel size) of the scan. As the voxel size of the CT scan increased (lower resolution) from 5 to 50 μm, the measured pore size was overestimated, and percentage porosity was underestimated by nearly 50%. With the homogeneous introduction of nanoparticles, changes to device structure could be quantified in the hydrated state, including at high-resolution. Biopolymers had significant structural changes post-hydration, including a mean increase of 130% in pore wall thickness that could potentially impact biological response. By incorporating imaging capabilities into polymeric devices, CT can be a facile way to monitor devices from initial design stages through to clinical translation.

Design of Low Noise Low Power Instrumentation Amplifier for Biomedical Applications

Advancements in integrated circuit (IC) technology and circuit design are enabling devices to replace complex biological applications. Despite this progress, achieving necessary functionality at low power levels is crucial for fully integrated systems. Biomedical devices like pacemakers, which require ongoing battery charging, operate at nanowatt power levels over several years. To ensure long-term pacemaker survival, an Analog Front End (AFE) with low to moderate power consumption and high precision is essential. The front-end Instrumentation Amplifier (INA) is vital for signal acquisition, significantly improving the effectiveness of ECG signal collection systems.The primary aim of this research is to develop an integrated ECG instrumentation amplifier (INA) that can deliver top-notch performance while keeping noise and power consumption to a minimum. This amplifier will enable seamless monitoring of the ECG signal without interruptions. Using an AC connected Common Mode Feedback system [1]. in conjunction with an Operational Trans-conductance Amplifier (OTA) that uses a Chopper stabilization technique, the study's band pass filtering response within the ECG signal frequency range of 0-100Hz is obtained as anticipated. CMOS 45 nm technology is used throughout the entire process using Mentor Graphics EDA Tools

A Novel Method for Recognizing Physical Fitness Signals of Basketball Players Based on Photovoltaic Power Supply

This study proposes a novel method for recognizing the physical fitness signals of basketball players using a photovoltaic power supply. The traditional method for measuring physical fitness signals, such as heart rate and oxygen consumption, requires constant monitoring via wired sensors. This method is not suitable for sports like basketball, where player movement can interfere with the sensors and affect the accuracy of the measurements. To address this issue, we developed a small, lightweight device that utilizes a photovoltaic power supply to eliminate the need for wires and allow for seamless monitoring of physical fitness signals. The device can be attached to a player's clothing or equipment, and the energy generated from the photovoltaic power supply is used to power the device and collect data. We conducted a pilot study with professional basketball players and found that our method provided accurate and reliable measurements of physical fitness signals. It opens up new possibilities for sports performance monitoring, as coaches and trainers can now quickly and efficiently track the physical fitness of their players in real time.

In situ three-dimensional reconstruction of laser powder bed fusion parts by light field camera

The assessment of output quality in the laser powder bed fusion process entails the analysis of the three-dimensional morphology of the manufactured parts, which plays a crucial role in quality control to prevent metallurgical defects. This study proposes a novel approach of three-dimensional reconstruction for laser powder bed fusion parts using a light field camera. Initially, a consistency method for high-precision camera calibration was explored to determine the virtual camera pixel size and coefficient of effective focal length of light field camera. Subsequently, the model based light field epipolar-plane image Unet is designed to obtain disparity information without any data augmentation techniques. By leveraging both binocular and light field optical paths, a robust mapping relationship of the disparity, depth, and visually coherent three-dimensional contour was established. Finally, the three-dimensional contours of laser powder bed fusion parts were reconstructed by the established mapping with a single exposure of the light field camera. The proposed method enables three-dimensional contour visualization of laser powder bed fusion parts from multi-view perspectives, which facilitates in situ monitoring of the spattering distance, three-dimensional spatial points and lines of the printed layers and contours of printed parts. With a calibration error of 50 μm and resolution of 180 μm for the light field camera (Lytro illum camera), the maximum relative and absolute errors between reconstructed three-dimensional spatial points were 4.33 % and 260 μm respectively when compared with theoretical dimension, and 0.5 % and 210 μm respectively when compared with manufactured dimension. The maximum root-mean-square error of reconstructed three-dimensional contour surfaces was found to be 1.55 mm. This study represents the first application of a light field camera for in situ three-dimensional reconstruction of laser powder bed fusion parts, which notably can be obtained from only a single round of calibration and exposure, enabling seamless monitoring of their surface morphology and facilitating online quality control. These advancements have vast potential for enhancing high-end additive manufacturing.

Advancing precision agriculture through multi-objective optimization using butterfly algorithm

Wireless Sensor Networks (WSNs) are transforming precision agriculture by enabling seamless monitoring and control of key factors such as temperature, humidity, solar radiation, soil moisture, and various dissolved compounds. This technology enhances efficiency and productivity while reducing costs. However, optimizing coverage area and energy efficiency in precision agriculture WSNs presents significant challenges. To address these challenges, our work focuses on developing innovative solutions inspired by advanced algorithms and state-of-the-art techniques for WSNs. Our primary objective is to improve area coverage and reduce energy consumption in precision agriculture WSNs. We are developing algorithms that can adapt to diverse agricultural landscapes. Through simulations, we aim to evaluate the performance and impact of our novel algorithm on precision agriculture applications. These simulations will provide valuable insights into the effectiveness of our algorithm in enhancing coverage area and energy efficiency in WSNs. Furthermore, our research aims to contribute to the broader field of WSNs by providing a detailed analysis of the challenges and opportunities in optimizing coverage area and energy efficiency in agricultural settings. By leveraging advanced algorithms and techniques, we aim to enhance the capabilities of WSNs in precision agriculture, leading to more sustainable and efficient farming practices.

Examination of the Effect of Task Complexity and Coping Capacity on Driving Risk: A Cross-Country and Transportation Mode Comparative Study.

The i-DREAMS project established a 'Safety Tolerance Zone (STZ)' to maintain operators within safe boundaries through real-time and post-trip interventions, based on the crucial role of the human element in driving behavior. This paper aims to model the inter-relationship among driving task complexity, operator and vehicle coping capacity, and crash risk. Towards that aim, data from 80 drivers, who participated in a naturalistic driving experiment carried out in three countries (i.e., Belgium, Germany, and Portugal), resulting in a dataset of approximately 19,000 trips were collected and analyzed. The exploratory analysis included the development of Generalized Linear Models (GLMs) and the choice of the most appropriate variables associated with the latent variables "task complexity" and "coping capacity" that are to be estimated from the various indicators. In addition, Structural Equation Models (SEMs) were used to explore how the model variables were interrelated, allowing for both direct and indirect relationships to be modeled. Comparisons on the performance of such models, as well as a discussion on behaviors and driving patterns across different countries and transport modes, were also provided. The findings revealed a positive relationship between task complexity and coping capacity, indicating that as the difficulty of the driving task increased, the driver's coping capacity increased accordingly, (i.e., higher ability to manage and adapt to the challenges posed by more complex tasks). The integrated treatment of task complexity, coping capacity, and risk can improve the behavior and safety of all travelers, through the unobtrusive and seamless monitoring of behavior. Thus, authorities should utilize a data system oriented towards collecting key driving insights on population level to plan mobility and safety interventions, develop incentives for road users, optimize enforcement, and enhance community building for safe traveling.

A Flexible and Biodegradable Piezoelectric‐Based Wearable Sensor for Non‐Invasive Monitoring of Dynamic Human Motions and Physiological Signals

AbstractRecent progress in flexible sensors and piezoelectric materials has enabled the development of continuous monitoring systems for human physiological signals as wearable and implantable medical devices. However, their non‐degradable characteristics also lead to the generation of a significant amount of non‐decomposable electronic waste (e‐waste) and necessitate a secondary surgery for implant removal. Herein, a flexible and biodegradable piezoelectric material for wearable and implantable devices that addresses the problem of secondary surgery and e‐waste while providing a high‐performance platform for continuous and seamless monitoring of human physiological signals and tactile stimuli is provided. The novel composition of bioresorbable poly(l‐lactide) and glycine leads to flexible piezoelectric devices for non‐invasive measurement of artery pulse signals in near‐surface arteries and slight movement of the muscle, including the trachea, esophagus, and movements of joints. The complete degradability of piezoelectric film in phosphate‐buffered saline at 37 °C is also shown. The developed pressure sensor exhibits high sensitivity of 13.2 mV kPa−1 with a response time of 10 ms and shows good mechanical stability. This piezoelectric material has comparable performance to commonly used non‐degradable piezoelectric materials for measuring physiological signals. It can also be used in temporary implantable medical devices for monitoring due to its degradable nature.

Plant manufacturer integrates laser measurement technology

AbstractProduction systems for the luxury food and tobacco industries are complex, achieve very high throughput rates and often run 24/7. In order to ensure the products' high quality, the Körber ‘Technologies’ division counts on seamless monitoring of the entire manufacturing process in the plant.

Urban Impervious Surface Extraction Based on Deep Convolutional Networks Using Intensity, Polarimetric Scattering and Interferometric Coherence Information from Sentinel-1 SAR Images

Urban impervious surface area is a key indicator for measuring the degree of urban development and the quality of an urban ecological environment. However, optical satellites struggle to effectively play a monitoring role in the tropical and subtropical regions, where there are many clouds and rain all year round. As an active microwave sensor, synthetic aperture radar (SAR) has a long wavelength and can penetrate clouds and fog to varying degrees, making it very suitable for monitoring the impervious surface in such areas. With the development of SAR remote sensing technology, a more advanced and more complex SAR imaging model, namely, polarimetric SAR, has been developed, which can provide more scattering information of ground objects and is conducive to improving the extraction accuracy of impervious surface. However, the current research on impervious surface extraction using SAR data mainly focuses on the use of SAR image intensity or amplitude information, and rarely on the use of phase and polarization information. To bridge this gap, based on Sentinel-1 dual-polarized data, we selected UNet, HRNet, and Deeplabv3+ as impervious surface extraction models; and we input the intensity, coherence, and polarization features of SAR images into the respective impervious surface extraction models to discuss their specific performances in urban impervious surface extraction. The experimental results show that among the intensity, coherence, and polarization features, intensity is the most useful feature in the extraction of urban impervious surface based on SAR images. We also analyzed the limitations of extracting an urban impervious surface based on SAR images, and give a simple and effective solution. This study can provide an effective solution for the spatial-temporal seamless monitoring of an impervious surface in cloudy and rainy areas.

Large-Scale Distributed System and Design Methodology for Real-Time Cluster Services and Environments

The demand for a large-scale distributed system, such as a smart grid, which includes real-time interconnection, is rapidly increasing. To provide a seamless connected environment, real-time communication and optimal resource allocation of cluster microgrid platforms (CMPs) are essential. In this paper, we propose two techniques for real-time interconnection and optimal resource allocation for a large-scale distributed system. In particular, to configure a CMP, we analyze the data transfer rate and utilization rate from the intelligent electronic device (IED), collecting the power production data to the individual controller. The details provided in this paper are used to design a sample value, i.e., raw data transfer, on the basis of the IEC 61850 protocol for mapping. The choice of sampled values is to attain the critical time requirement, data transmission of current transformers, voltage transformers, and protective relaying of less than 1 s without complicating the real-time implementation. Furthermore, in this paper, a way to determine the optimal number of physical resources (i.e., CPU, memory, and network) for a given system is discussed. CPU ranged from 0.9 to 0.98 while each cluster increased from 10 to 1000. With the same condition, memory utilized almost 100% utilization from 0.98 to 1. Lastly, the network utilization rate was 0.96 and peaked at 1 at most. Based on the results, we confirm that a large-scale distributed system can provide a seamless monitoring service to distribute messages for each IED, and this can provide a configuration for CMP without exceeding 100% utilization.

AN IOT-BASED WEARABLE FALL DETECTION SYSTEM AND ALERTING SYSTEM

Seamless monitoring is one of the major challenges for human community because of increasing the strength of sixty years old people and occurrence of many accidents in falls-risk related works like construction, manufacture and others. Wearable IoT devices are useful for continuous health monitoring, analyzing the behaviours, and fall detection. Falls are one of the foremost causes for injuries and death in our life. Present work proposes the human movement monitoring by using the pressure sensor for avoiding human falls and also the health report prepared from historical data’s uploaded in cloud by support-vector machine (SVM) algorithm. The proposed wearable IoT device for seamless monitoring system is proving 99% accuracy, less complex and compatible to use over other IoT wearable devices.

Wearable Walking Assistant for Freezing of Gait With Environmental IoT Monitoring: A Contribution to the Discussion.

Parkinson's disease (PD) is the second most common neurodegenerative disease, significantly increasing in the last three decades. Worldwide, seven to ten million people are affected by PD. In people living with PD, freezing of gait (FoG) significantly impacts activities of daily living, potentially leading to falls, injuries, and loss of autonomy. FoG prevalence rates vary widely, reaching at least 50% of patients with PD. Current therapeutic options have limited effectiveness, and their complement with innovative technology-based solutions in the real world is demanded to enhance daily functioning for people living with PD. This article provides a narrative review of current technological developments for people living with PD and, derived from that evidence, presents a perspective on integrating wearable technology and IoT to support telemonitoring and self-management of people living with PD in their daily living environment. Complementing current therapeutic options with technology-based solutions in PD patients' real-world environment is crucial to enhancing the quality of life of people living with PD. In that way, wearable technology and IoT might constitute resources of excellence in seamless monitoring and self-management in people's home environments.

In-Cabin Monitoring System for Autonomous Vehicles.

In this paper, we have demonstrated a robust in-cabin monitoring system (IMS) for safety, security, surveillance, and monitoring, including privacy concerns for personal and shared autonomous vehicles (AVs). It consists of a set of monitoring cameras and an onboard device (OBD) equipped with artificial intelligence (AI). Hereafter, this combination of a camera and an OBD is referred to as the AI camera. We have investigated the issues for mobility services in higher levels of autonomous driving, what needs to be monitored, how to monitor, etc. Our proposed IMS is an on-device AI system that indigenously has improved the privacy of the users. Furthermore, we have enlisted the essential actions to be considered in an IMS and developed an appropriate database (DB). Our DB consists of multifaced scenarios important for monitoring the in-cabin of the higher-level AVs. Moreover, we have compared popular AI models applied for object and occupant recognition. In addition, our DB is available on request to support the research on the development of seamless monitoring of the in-cabin higher levels of autonomous driving for the assurance of safety and security.

Considerations and Multi-Criteria Decision Analysis for the Installation of Collocated Permanent GNSS and SAR Infrastructures for Continuous Space-Based Monitoring of Natural Hazards

Over the past few decades, the global population and the built environment’s vulnerability to natural hazards have risen dramatically. As a result, decisive actions, such as the SENDAI framework, have emerged to foster a global culture of successful disaster risk reduction policies, including actions to mitigate the social and economic impact of geohazards. The effective study of natural disasters requires meticulous and precise monitoring of their triggering factors, with ground- and space-based techniques. The integration of GNSS and SAR observations through the establishment of permanent infrastructures, i.e., Continuously Operating Reference Stations (CORS) networks and arrays of Corner Reflectors (CRs), may form a seamless ground displacement monitoring system. The current research literature provides fragmented guidelines, regarding the co-location of SAR and GNSS permanent infrastructures. Furthermore, there exist no guidelines for the determination of the most suitable locations using a holistic approach, in terms of criteria and required data. The purpose of this paper is to present a semi-automatic multicriteria site suitability analysis and evaluation of candidate sites for the installation of a permanent CORS and two CRs; one for each pass, taking into account various parameters and criteria. The first results demonstrate that the collocation of SAR and GNSS permanent infrastructures, utilizing a holistic criteria-based approach, is successful and complies with all the literature’s requirements.

Reusable and pH-Stable Luminescent Sensors for Highly Selective Detection of Phosphate.

Phosphate sensors have been actively studied owing to their importance in water environment monitoring because phosphate is one of the nutrients that result in algal blooms. As with other nutrients, seamless monitoring of phosphate is important for understanding and evaluating eutrophication. However, field-deployable phosphate sensors have not been well developed yet due to the chemical characteristics of phosphate. In this paper, we report on a luminescent coordination polymer particle (CPP) that can respond selectively and sensitively to a phosphate ion against other ions in an aquatic ecosystem. The CPPs with an average size of 88.1 ± 12.2 nm are embedded into membranes for reusable purpose. Due to the specific binding of phosphates to europium ions, the luminescence quenching behavior of CPPs embedded into membranes shows a linear relationship with phosphate concentrations (3–500 μM) and detection limit of 1.52 μM. Consistent luminescence signals were also observed during repeated measurements in the pH range of 3–10. Moreover, the practical application was confirmed by sensing phosphate in actual environmental samples such as tap water and lake water.