Smart Sensor Systems Research Articles

Current monitoring for a fused filament fabrication additive manufacturing process using an Internet of Things system

The Fourth Industrial Revolution (Industry 4.0) has led to the transformation of manufacturing systems into digital ecosystems. The Internet of Things (IoT) and other emerging technologies play a significant role in this shift. Smart sensor systems are necessary to integrate their resources into the digital world to move small and medium size manufacturers toward digital transformation. The proposed work offers an IoT-based current monitoring system for a fused filament fabrication (FFF) additive manufacturing process to assist in the digital transformation efforts. In this work, we investigate how a widely available microcontroller paired with a non-invasive split core current transformer can create functional data streams to support critical industry-related applications. We tested the IoT solution using two different sampling algorithms and different sampling windows to assess how they influence the output of the device. Our results indicate the successful current draw monitoring of static resistive power loads and an FFF additive manufacturing process. More specifically, the IoT device was able to differentiate between different levels of current draw and produces output that is suitable for a wide range of manufacturing machinery applications such as power monitoring, downtime recognition, task recognition, and condition monitoring.

Enhancing long-term stability of photoacoustic gas sensor using an extremum-seeking control algorithm

Smart sensor systems have gained increasing importance in various fields, including healthcare, environmental monitoring, industrial automation, and security. Photoacoustic gas sensors are an emerging type of optical sensor used in various applications due to its enhanced performance characteristics. However, the accuracy and reliability of gas concentration measurements from photoacoustic gas sensors may be impacted by several known limitations, including drift of the gas cell resonant frequency over extended periods of time. Researchers have proposed various solutions, including optimization methods and signal processing algorithms, to address this and others issues. In this paper, we propose a novel solution using an extremum-seeking control algorithm to manage the laser modulation frequency of photoacoustic gas sensors. By tracking the changing resonant frequency of the gas cell, long-term stability can be achieved, making it suitable for environmental monitoring, petroleum exploration, and industrial process control. Our approach has the potential to improve the accuracy and reliability of long-term measurements obtained from photoacoustic gas sensors, providing a stable and reliable method for gas concentration estimation.

Toward a Smart Sensing System to Monitor Small Animal's Physical State via Multi-Frequency Resonator Array.

This article presents a highly scalable and rack-mountable wireless sensing system for long-term monitoring (i.e., sense and estimate) of small animal/s' physical state (SAPS), such as changes in location and posture within standard cages. The conventional tracking systems may lack one or more features such as scalability, cost efficiency, rack-mount ability, and light condition insensitivity to work 24/7 on a large scale. The proposed sensing mechanism relies on relative changes of multiple resonance frequencies due to the animal's presence over the sensor unit. The sensor unit can track SAPS changes based on changes in electrical properties in the sensors near fields, appearing in the resonance frequencies, i.e., an Electromagnetic (EM) Signature, within the 200 MHz-300 MHz frequency range. The sensing unit is located underneath a standard mouse cage and consists of thin layers of a reading coil and six resonators tuned at six distinct frequencies. ANSYS HFSS software is used to model and optimize the proposed sensor unit and calculate the Specific Absorption Rate (SAR) obtained under 0.05 W/kg. Multiple prototypes have been implemented to test, validate, and characterize the performance of the design by conducting in vitro and in vivo experiments on Mice. The in-vitro test results have shown a 15 mm spatial resolution in detecting the mouse's location over the sensor array having maximum frequency shifts of 832 kHz and posture detection with under 30° resolution. The in-vivo experiment on mouse displacement resulted in frequency shifts of up to 790 kHz, indicating the SAPS's capability to detect the Mice's physical state.

ELSAH (electronic smart patch system for wireless monitoring of molecular biomarkers for healthcare and wellbeing): definition of possible use cases.

The ELSAH (electronic smart patch system for wireless monitoring of molecular biomarkers for healthcare and wellbeing) project has received funding from EU's Horizon 2020 research and innovation program (grant agreement no. 825549). Its aim is to develop a wearable smart patch-based microneedle sensor system that can simultaneously measure several biomarkers in users' dermal interstitial fluid. This system could have several use cases based on continuous glucose and lactate monitoring: early detection of (pre-) diabetes mellitus, increasing physical performance through optimal carbohydrate intake, achieving a healthier lifestyle through behavioral changes based on the interpretation of glucose data, performance diagnostics (lactate threshold test), control of optimal training intensities corresponding with certain lactate levels, or warning of diseases/health threats, such as the metabolic syndrome or sepsis associated with increased lactate levels. The ELSAH patch system has a high potential of increasing health and wellbeing in users.

Comparison of Low-Cost 5G Electromagnetic Field Sensors

This paper compares different low-cost sensors that can measure (5G) RF-EMF exposure. The sensors are either commercially available (off-the-shelf Software Defined Radio (SDR) Adalm Pluto) or constructed by a research institution (i.e., imec-WAVES, Ghent University and Smart Sensor Systems research group (S³R), The Hague University of Applied Sciences). Both in-lab (GTEM cell) and in-situ measurements have been performed for this comparison. The in-lab measurements tested the linearity and sensitivity, which can then be used to calibrate the sensors. The in-situ testing confirmed that the low-cost hardware sensors and SDR can be used to assess the RF-EMF radiation. The variability between the sensors was 1.78 dB on average, with a maximum deviation of 5.26 dB. Values between 0.09 V/m and 2.44 V/m were obtained at a distance of about 50 m from the base station. These devices can be used to provide the general public and governments with temporal and spatial 5G electromagnetic field values.

Review on Discrimination of Hazardous Gases by Smart Sensing Technology

Real-time detection of hazardous gases in the ambient and indoors has become the prime motive for curbing the problem of air pollution. Keeping the concentration of hazardous gases in control is the main task before human society so as to keep environmental balance. Researchers are concentrating on smart sensors because they can detect and forecast the presence of gas in real-time, provide correct information about gas concentration, and detect a target gas from a mixture of gases. This smart gas sensor system can have applications in the field of military, space, underwater, indoor, outdoors, factories, vehicles, and wearable smart devices. This study reviews recent advances in smart sensor technology with respect to the material structure, sensing technique, and discrimination algorithm. Focus is given on reducing the power consumption and area of a sensor circuitry with the help of different techniques.

Smart Sensors System Based on Smartphones and Methodology for 3D Modelling in Shallow Water Scenarios

The aim of the paper was the implementation of low-cost smart sensors for the collection of bathymetric data in shallow water and the development of a 3D modelling methodology for the reconstruction of natural and artificial aquatic scenarios. To achieve the aim, a system called GNSS > Sonar > Phone System (G > S > P Sys) was implemented to synchronise sonar sensors (Deeper Smart Sonars CHIRP+ and Pro+ 2) with an external GNSS receiver (SimpleRTK2B) via smartphone. The bathymetric data collection performances of the G > S > P Sys and the Deeper Smart Sonars were studied through specific tests. Finally, a data-driven method based on a machine learning approach to mapping was developed for the 3D modelling of the bathymetric data produced by the G > S > P Sys. The developed 3D modelling method proved to be flexible, easily implementable and capable of producing models of natural surfaces and submerged artificial structures with centimetre accuracy and precision.

A Smart Tactile Sensing System Based on Carbon Nanotube/Polypropylene Composites for Wearable Applications

Recent advances in nanomaterials render the possibility of fabricating tactile sensors suitable for electronic skin, haptic interfaces, and biomedical applications. Furthermore, the problem of performing complex inertial measurements generated during the tactile process of polymer composites can be tackled using artificial intelligence (AI). However, the implementation of AI-based signal processing on embedded devices still represents an open area of opportunity for the design of the next generation of smart wearable sensor systems. A novel nanostructured smart tactile sensing system for wearable applications is proposed, using a 3-D-printed structure with embedded electronic devices. The highly sensitive piezoresistive tactile sensor is based on multiwall carbon nanotube/polypropylene (MWCNT/PP) composites. The integration of electronic circuits for signal processing of an artificial neural network (NN) on a digital controller unit improves the tactile interpretation in the wearable embedded device. Experiments show that the pressure classification results on MWCNT/PP composites with 98% accuracy.

Versatile Electronic Textile Enabled by a Mixed-Dimensional Assembly Strategy.

Electronic textiles (e-textiles) hold great promise for serving as next-generation wearable electronics owing to their inherent flexible, air-permeable, and lightweight characteristics. However, these e-textiles are of limited performance mainly because of lacking powerful materials combination. Herein, a versatile e-textile through a simple, high-efficiency mixed-dimensional assembly of 2D MXene nanosheets and 1D silver nanowires (AgNWs) are presented. The effective complementary actions of MXene and AgNWs endow the e-textiles with superior integrated performances including self-powered pressure sensing, ultrafast joule heating, and highly efficient electromagnetic interference (EMI) shielding. The textile-based self-powered smart sensor systems obtained through the screen-printed assembly of MXene-based supercapacitor and pressure sensor are flexible and lightweight, showing ultrahigh specific capacitance (2390 mF cm-2 ), robust areal energy density (119.5 µWh cm-2 ), excellent sensitivity (474.8 kPa-1 ), and low detection limit (1Pa). Furthermore, the interconnected conductive MXene/AgNWs network enables the e-textile with ultrafast temperature response (10.4°C s-1 ) and outstanding EMI shielding effectiveness of ≈66.4dB. Therefore, the proposed mixed-dimensional assembly design creates a multifunctional e-textile that offers a practical paradigm for next-generation smart flexible electronics.

Integrated wearable smart sensor system for real-time multi-parameter respiration health monitoring

Integrated wearable smart sensor system for real-time multi-parameter respiration health monitoring

Wearable Smart Sensor System for Medical Monitoring with an Assessment of the Level of Blood Loss and Pain Shock Because of Trauma or Injury

Wearable Smart Sensor System for Medical Monitoring with an Assessment of the Level of Blood Loss and Pain Shock Because of Trauma or Injury

Digital Twin of a photoacoustic trace gas sensor for monitoring methane in complex gas compositions

The digitalization of industrial processes requires smart sensor systems. Photoacoustic spectroscopy is well suited for this purpose as it allows for small-sized and low-cost trace gas analysis. However, the method is susceptible to changes in measurement conditions and standard calibration routines often fail to correct for all changes. We therefore created a Digital Twin (DT) of a photoacoustic trace gas sensor for methane and evaluated it regarding variations in gas composition (CH4, N2, O2, CO2, H2O), temperature and pressure. With a mean absolute percentage error of 0.8 % the accuracy of the sensor after DT compensation significantly exceeds the 24 % achieved based on standard calibration in nitrogen. For the first time, we can fully analytically compute the photoacoustic signal under moderate ambient conditions with an error in the ppbV range by taking a holistic approach. Assuming knowledge of the underlying energy transfer processes, the model of this Digital Twin can be adapted to any microphone based photoacoustic sensor for monitoring any analyte species.

Sustainable Triboelectric Materials for Smart Active Sensing Systems

AbstractWith the vigorous development of the Internet of Things and artificial intelligence, the active sensing system based on triboelectric nanogenerators plays an excellent performance potential and application value as a pioneering technology for smart manufacturing. Nevertheless, achieving material innovation to strike a good balance between active sensing systems and environmental friendliness remains a difficult task. As the most abundant biopolymer on earth, the sustainability potential and excellent performance of cellulose are of great importance for the development of smart sensing systems. This review intends to provide a new perspective on the sustainability of smart active sensing systems to design and fabricate cellulosic triboelectric materials for self‐powered sensing systems. Herein, the structure and advantageous properties of cellulosic triboelectric materials are briefly described. Furthermore, the structure–property–application relationship of the materials is addressed from the perspective of material design and structure optimization. Next, the latest applications of cellulose triboelectric materials are comprehensively described in smart sensing fields such as environmental monitoring, smart home, smart medical, human–machine interaction, and the Internet of everything. Lastly, the current challenges and future developments of cellulose triboelectric materials for smart sensor systems are presented.

(Invited) Nanostructured Electrochemical Devices and Self-Powered Systems for Biosensing

Self-powered systems for biosensing have attracted tremendous research interest in recent years, mainly due to the rapidly expanding market of wearable and portable devices for applications in clinical diagnosis and physiological monitoring. In our work, novel and unique hierarchical nanostructures were designed and synthesized to realize electrochemical devices with high performance, especially the sensor stability and energy storage capability. Meanwhile, scalable and printable approach was developed to integrate these electrochemical devices into monolithically integrated self-powered systems. The as-developed nanostructured electrochemical devices in conjunction with printable approach show great potency in fabrication of various wearable integrated self-powered devices for personalized healthcare monitoring applications. Our research highlights are as follow: Development of nanoporous membranes for electrochemical sensor applications. It eliminates enzymes escaping and provides sufficient surface area for molecular/ion diffusion and interactions, so as to ensure the sustainable catalytic activities of the sensors and generate reliable measurable signals during noninvasive monitoring. The highly improved stability of sensors is extremely desirable for investigation of metabolic activities in physiological systems.A fully integrated and self-powered system in a smartwatch fashion for continuous monitoring of sweat glucose levels during both equilibrium status and dynamic activities. The smartwatch can be self-powered by flexible photovoltaic cells, without external charging, the harvested energy can also be stored in the flexible Zn-MnO2 batteries as backup power source. It is also capable for real-time and in situ data analysis/display with integrated circuit board and E-ink screen.A monolithically integrated self-powered smart sensor system with energy supplied by fully printable planar supercapacitors and embedded solar cells, was fabricated on plastic substrate with inkjet printing technique as a proof-of-concept. The as-developed printable nanostructured electrochemical devices in conjunction with printable approach for system integration show great potency in fabrication of various wearable integrated self-powered devices for personalized healthcare monitoring applications.

KneTex – Improvements to classification methods for a sensor system for rehabilitation after ACL surgery

Abstract Injuries and the associated surgery to the anterior cruciate ligament (ACL) can often trigger unpredictable effects, such as the so-called giving way effect, which is an uncontrolled buckling of the knee joint. For this purpose, the KneTex project has developed a smart textile-integrated sensor and actuator bandage system to record the movement of such patients and to monitor and support the rehabilitation process. Long-term monitoring and analysis of the movement data will identify patterns or gait types that can lead to a giving way effect. This paper describes the recent developments of the random forest model-based motion classification system developed within the project. Improvements have been achieved by reducing the number of features needed by 25% using feature importance analysis, speeding up the computation time by 14%, and increasing the classification efficiency. Feature elimination is a useful tool to improve classification systems in settings where feature count is high and feature importance analysis contributed by improving our understanding which sensor of our system are important for the motion classification task.

Emerging Strategies Based on Sensors for Chronic Wound Monitoring and Management

Pressure ulcers (PUs) are a serious global health challenge, affecting a large section of the population and putting immense pressure on healthcare systems. Sensor-based diagnostic tools and monitoring systems have emerged as a potential non-invasive solution to reduce the occurrence of new cases of PUs and promise a significant reduction in treatment expenditure and time. In this endeavour, the present manuscript reviews the advancements made in the last decade in the development and commercial adoption of different sensor systems for PU-associated chronic wound management. Different types of smart sensor systems have been developed in which pressure, chemical, and optical sensors have witnessed a lot of interest and significant advancement among research communities and industries alike. These sensors utilize a host of nanomaterial-based sensing materials, flexible support, diverse transducing modes, and different device designs to achieve high sensitivity and selectivity for skin pressure, temperature, humidity, and biomarkers released from the wound. Some of these sensor’s array-based electronic skin (e-skin) has reached the stage of commercialization and is being used in commercial products, such as smart bandages, shoes, watches, and mattress among others. Nonetheless, further innovations are necessary in the direction of associating multiple types of sensor arrays, particularly pressure and chemical sensor-based e-skins in a microsystem for performing real-time assessment of all the critical wound parameters.

Utilization of Neural Network for Local Processing on the Internet of Things (IoT)

This research is intended to produce a smart sensor system using a Neural Network which is implemented through the design of an Occupation monitoring tool and application (presence). Sensor equipment uses Arduino UNO, EST8266 Wifi, DHT22 and LDR. The readings of these sensors are then sent remotely via the internet and read by the application. Based on the research training conducted, a prediction model will be born where this model will translate the sensor data received and then generate data on the presence in an environment where the sensor is placed. IoT sensor tools and applications that are placed in several locations are very useful in carrying out daily human activities such as monitoring security in the home environment, schools, offices and warehouses for storing goods.

스마트센서시스템의 기능안전을 위한 Lora 통신 필드 적합성 테스트 연구

Purpose: Three types of LPWA LoRa module were investigated as candidate smart sensor systems in a smart factory. A long-distance communication success test was conducted to determine which module type best satisfied functional safety requirements.BRMethods: The three types of LoRa communication module considered in this paper were 1. sLory, 2. RYLR890 using UART interface and 3. P2P UART communication module RAK811. A communication success field demonstration test was performed at 3.24 km in the city center and in a flat area (Ansan-si).BRResults: The RYLR890 module type achieved communication success up to 3 km. However, the communication success rate from departure to arrival was unstable. The RAK811 achieved communication up to 2 km and the field test demonstrated that the transmission/reception performance was stable from departure to arrival.BRConclusion: Three types of LoRa module were selected for LPWA and the world

Selectivity enhancement for metal oxide (MOX) based gas sensor using thermally modulated datasets coupled with golden section optimization and chemometric techniques.

The ever-increasing demand for smart sensors for internet of things applications drove the change in outlook toward smart sensor system design. This paper focuses on using low-cost gas sensors [Metal Oxide (MOX)] for detection of more than one gas, which is otherwise complex due to poor selectivity of MOX sensors. In this work, detection of two gases, namely, ammonia (NH3) and carbon monoxide (CO), using a single metal oxide (pristine tin oxide) sensor is demonstrated. Furthermore, chemometric based algorithms have been used to classify and quantify both gases. The present investigation uses the temperature modulated gas sensor response obtained at different concentrations for the mentioned gases. The golden section based optimization technique has been employed to obtain two different ranges of temperatures for both gases. After applying certain pre-processing techniques, the acquired data from the sensors were fed to various classification techniques, such as partial least squares (PLS) discriminant analysis, k-means, and soft independent modeling by class analogy, and 100% classification results were obtained. Furthermore, PLS regression (PLS-R) was used to perform quantitative analysis on the data using the optimized temperature ranges for both gases, and R2 regression coefficients, 0.999 25 for NH3 and 0.9399 for CO, were obtained. The results obtained from both the qualitative and quantitative analyses make our approach low-cost and smart to mitigate the cross-selectivity of metal oxide semiconductor based smart sensor design.

A Combined Semi-Supervised Deep Learning Method for Oil Leak Detection in Pipelines Using IIoT at the Edge

Pipelines are integral components for storing and transporting liquid and gaseous petroleum products. Despite being durable structures, ruptures can still occur, resulting not only in financial losses and energy waste but, most importantly, in immeasurable environmental disasters and possibly in human casualties. The objective of the ESTHISIS project is the development of a low-cost and efficient wireless sensor system for the instantaneous detection of leaks in metallic pipeline networks transporting liquid and gaseous petroleum products in a noisy industrial environment. The implemented methodology is based on processing the spectrum of vibration signals appearing in the pipeline walls due to a leakage effect and aims to minimize interference in the piping system. It is intended to use low frequencies to detect and characterize leakage to increase the range of sensors and thus reduce cost. In the current work, the smart sensor system developed for signal acquisition and data analysis is briefly described. For this matter, two leakage detection methodologies are implemented. A 2D-Convolutional Neural Network (CNN) model undertakes supervised classification in spectrograms extracted by the signals acquired by the accelerometers mounted on the pipeline wall. This approach allows us to supplant large-signal datasets with a more memory-efficient alternative to storing static images. Second, Long Short-Term Memory Autoencoders (LSTM AE) are employed, receiving signals from the accelerometers, and providing an unsupervised leakage detection solution.