Low-cost Sensors Research Articles

Detecting airport luggage dimensions through low-cost depth sensors

A factor that impacts airlines' resources is the verification of luggage’s dimensions during the boarding process. Companies often rely on a human operator to perform this check using a manual template, which can cause delays. As an alternative, companies are investing in self bag drop systems. This process introduces new technological challenges since, in this scenario, checking the conformity of luggage dimensions is delegated to the passenger, which can lead to errors. In addition, current solutions use specific computational devices, such as laser scanners, that are expressive in size and cost, which may require interventions in the airport infrastructure. To overcome this, isolated initiatives are observed with alternative technologies, such as low-cost depth sensors, but they usually come without a scientific investigation. In this sense, this work investigates the technical viability of using such low-cost devices to obtain the dimensions of airport baggage. To do so, we developed a model that obtains a 3D point cloud of the luggage surface through a Microsoft Kinect V2 sensor. This cloud is treated and processed to extract the dimensions of the luggage. In order to validate this approach, a full-scale physical prototype was built and tested. The results indicate that the mean absolute error of the obtained dimension by the proposed model is 2.86 cm. Such data suggest that this technology has the potential to become an alternative to detect the dimensions of airport luggage.

Design and development of a feedback system for automatic treadmill speed adaptation

Treadmills with automatic speed adjustment offer a unique advantage: they can mimic outdoor conditions for specific training or testing protocols. This versatility makes them highly applicable in both sports and rehabilitation settings. This study presents a novel framework based on a feedback control loop system, conceived to control a treadmill belt speed using a non-invasive and low-cost sensor (Microsoft Kinect) to detect the users’ position and avoid object obstruction issues. The speed of the treadmill belt is regulated according to the user’s speed, by means of a proportional-integrative-derivative (PID) controller and a parabolic gain function. By tuning the gain function parameters, the user can customize the response of the treadmill. Position data collected during exercise using the Microsoft Kinect sensor was compared with that collected with a stereophotogrammetric motion capture system, showing promising results in terms of accuracy in position assessment. The comparison highlighted a 0.9 degree of correlation between the two systems during the running and cross-country indoor skiing tests performed. In addition, upon considering the relationship between the differences and averages of the two measures, no systematic bias was identified. The system proved to be functional for running and cross-country skiing, and it can therefore re-create similar typical characteristics of outdoor environments (e.g., speed and slope) thanks to the non-invasive user’s position detection and the customizable gain function.

Metrology for sensor networks: metrological traceability and measurement uncertainties for air quality monitoring

Abstract In situ calibration of sensors delivering SI traceable measurement results still provides an open question to the design and operation of sensor networks. Particularly when addressing low-cost sensors, currently, the use of sensor networks for air quality monitoring is limited by the low or unknown accuracy of measurements that they can achieve, while the data quality of individual sensor networks is mainly derived by algorithms. Standardization bodies like DIN and CEN therefore announced the need for investigations of validation methods on gas phase species and particulate matter on the one hand side, and for the development of fully digitized quality assurance/quality control and calibration techniques for sensor networks on the other (CEN/CENELEC, Opportunity for Standardisation to Contribute to the European Partnership on Metrology EPM under Horizon Europe). This contribution concentrates on the metrological traceability of sensor networks for air quality monitoring to the international system of units (SI) based on FAIRified intra-network communications (M. Wilkinson, et al., “The FAIR guiding principles for scientific data management and stewardship,” Sci. Data, vol. 3, 2016, Art. no. 160018) and including delocalized Optical Gas Standards operated according to the digital TILSAM method (O. Werhahn, et al., The TILSAM Method Adapted into Optical Gas Standards – Complementing Gaseous Reference Materials, PTB Open Access Repository, 2021). Informed by related activities in EURAMET (Partnership project FunSNM, EMNs COO & POLMO, TC-IM 1551) (European Metrology Network Climate and Ocean Observation (COO), European Metrology Network Pollution Monitoring (POLMO), EURAMET Project TC-IM 1551, Project Database) this contribution discusses the importance of measurement uncertainties in the context of sensor networks, comprising different sensor principles and promoting an efficient uptake of state-of-the-art methods. We discuss how the sensor network case can be addressed with sensors individually using the GUM principles (Joint Committee for Guides in Metrology, Guide to the Expression of Uncertainty in Measurement (GUM), JCGM 100: 2008 (E)). For sensor network measurements becoming metrologically traceable to the SI, documented and unbroken chains of calibrations need to be implemented each contributing to the measurement uncertainty. This applies to each individual sensor of the network including the potential gold standard among them, but also to the network’s output viewed as a single entity. The contribution provides first approaches to be tested and validated that are underpinned by fundamental design strategies for sensor networks. It follows on practical applications in real world scenarios aside from model uncertainties discussed in artificial intelligence prospects.

Calibrating Low-Cost Smart Insole Sensors with Recurrent Neural Networks for Accurate Prediction of Center of Pressure.

This paper proposes a scheme for predicting ground reaction force (GRF) and center of pressure (CoP) using low-cost FSR sensors. GRF and CoP data are commonly collected from smart insoles to analyze the wearer's gait and diagnose balance issues. This approach can be utilized to improve a user's rehabilitation process and enable customized treatment plans for patients with specific diseases, making it a useful technology in many fields. However, the conventional measuring equipment for directly monitoring GRF and CoP values, such as F-Scan, is expensive, posing a challenge to commercialization in the industry. To solve this problem, this paper proposes a technology to predict relevant indicators using only low-cost Force Sensing Resistor (FSR) sensors instead of expensive equipment. In this study, data were collected from subjects simultaneously wearing a low-cost FSR Sensor and an F-Scan device, and the relationship between the collected data sets was analyzed using supervised learning techniques. Using the proposed technique, an artificial neural network was constructed that can derive a predicted value close to the actual F-Scan values using only the data from the FSR Sensor. In this process, GRF and CoP were calculated using six virtual forces instead of the pressure value of the entire sole. It was verified through various simulations that it is possible to achieve an improved prediction accuracy of more than 30% when using the proposed technique compared to conventional prediction techniques.

Balloon-shaped fiber surface nanoscale axial photonic microresonator for micro-displacement measurement.

In this Letter, we demonstrate a micro-displacement sensor based on a balloon-shaped fiber surface nanoscale axial photonic (SNAP) microresonator. The SNAP microresonator is fabricated by fiber bending to introduce nanoscale effective radius variations (ERVs) on the fiber surface. Displacement measurement based on the balloon-shaped SNAP microresonator is realized based on the ERV modulation resulting from the change in the bending radius of the balloon-shaped structure. An advantage of this approach is that the displacement measurement range is not limited to the axial length of the SNAP region. The experimental results show that the displacement measurement range of the balloon-shaped fiber SNAP microresonator can reach 2500 µm and that the minimum measurement resolution is 0.1 µm. This large-range, high-resolution, and low-cost micro-displacement sensor has the potential to be a promising candidate in high-precision displacement measurement applications.

Assessing low-cost sensor for characterizing temporal variation of PM2.5 in Bandung, Indonesia

Fine particulate matter (PM2.5) is a concern due to its health effects, necessitating critical monitoring for both quantity and variability. Utilizing low-cost sensors to track PM2.5 is essential to augment other monitoring instruments, which are effective in generating temporal and spatial data. Therefore, in this study, we employed the low-cost PurpleAir (low-cost PA-II) to characterize the seasonal and diurnal variation of PM2.5 in Bandung, Indonesia, representing the densely populated metropolitan area of Bandung. During the sampling period from June 2022 to May 2023, co-location sampling with the filter-based Super Speciation Air Sampling System (SuperSASS) was employed to assess the low-cost PA-II. The PM2.5 data collected by the low-cost PA-II were compared to the SuperSASS data. The annual average mass concentration of PM2.5 measured by SuperSASS and the low-cost sensor was 31.51 ± 15.53 μg/m3 and 39.04 ± 15.16 μg/m3, respectively, surpassing the Indonesian government's regulation limit for an annual average of 15 μg/m3. The comparative results of the two methods were obtained with R2 = 0.96, and low-cost PA-II data was 1.24 higher than SuperSASS. The difference may be attributed to several factors, including differences in sensor technology, calibration, location, and data processing. The seasonal variation indicated an increase in concentration during the dry season and a decrease during the wet season. The diurnal pattern indicates the morning peak between 06:00 and 08:00 during the rush hour, as well as the evening peak between 18:00 and 23:00, attributed to low temperatures and stagnant conditions. The diurnal pattern of PM2.5, which often exhibits the lowest peak at midday, is influenced by a combination of meteorological, atmospheric, and human activity factors. The findings suggested that the utilization of low-cost PA-II for a broader spatial scope is promising for real-time monitoring of PM2.5, to increase air pollution awareness in society.

Assessing the spatial transferability of calibration models across a low-cost sensors network

Low-cost sensor networks (LCSNs) are expanding worldwide to gather high spatiotemporal resolution data due to their economic feasibility and compact size. The reliability of LCS-recorded data is limited due to their calibration dependencies in the field. Previous studies have focused on the development of LCS calibration models by co-location with the regulatory monitoring stations. However, it is challenging to calibrate LCS in the field for countries with limited infrastructure for air quality monitoring, pointing towards the need for transferable calibration models. Only a few studies have addressed this challenge and provide no information on the factors that may affect the performance of transferable calibration models. Here, we examined the spatial transferability of the calibration models developed using machine learning (ML) algorithms for an LCSN with twenty-two (22) sites in NCT-Delhi. The site-specific calibration models performed well at each site with high R2 and significantly low RMSE values. These models were transferred to the other sites, and the effect of distance between the sites (D), source composition, PM ratios, and particle size distribution (PSD) on the transferability of calibration models was investigated. The models developed at the Mundka (S10) and Punjabi Bagh (S16) sites complied with the evaluation criterion (R2 ≥ 0.70) for each site, irrespective of the distance between the sites. Furthermore, PM ratios reported by the LCSs did not significantly differ across sites, suggesting that the PMS algorithm provides a proxy of the size-resolved mass fractions. Evaluation of the PSD at different sites supported our findings. We also introduced the concept of selecting representative locations for LCS co-location by computing transferability scores using k-means clustering and presented a reference map for NCT-Delhi for developing scalable calibration models.

An Efficient Bio-Receptor Layer Combined with a Plasmonic Plastic Optical Fiber Probe for Cortisol Detection in Saliva.

Cortisol is a clinically validated stress biomarker that takes part in many physiological and psychological functions related to the body's response to stress factors. In particular, it has emerged as a pivotal tool for understanding stress levels and overall well-being. Usually, in clinics, cortisol levels are monitored in blood or urine, but significant changes are also registered in sweat and saliva. In this work, a surface plasmon resonance probe based on a D-shaped plastic optical fiber was functionalized with a glucocorticoid receptor exploited as a highly efficient bioreceptor specific to cortisol. The developed plastic optical fiber biosensor was tested for cortisol detection in buffer and artificial saliva. The biosensor response showed very good selectivity towards other hormones and a detection limit of about 59 fM and 96 fM in phosphate saline buffer and artificial saliva, respectively. The obtained detection limit, with a rapid detection time (about 5 min) and a low-cost sensor system, paved the way for determining the cortisol concentration in saliva samples without any extraction process or sample pretreatment via a point-of-care test.

Using metal oxide gas sensors to estimate the emission rates and locations of methane leaks in an industrial site: assessment with controlled methane releases

Abstract. Fugitive methane (CH4) emissions occur in the whole chain of oil and gas production, including from extraction, transportation, storage, and distribution. Such emissions are usually detected and quantified by conducting surveys as close as possible to the source location. However, these surveys are labour-intensive, are costly, and fail to not provide continuous emissions monitoring. The deployment of permanent sensor networks in the vicinity of industrial CH4 emitting facilities would overcome the limitations of surveys by providing accurate emission estimates, thanks to continuous sampling of emission plumes. Yet high-precision instruments are too costly to deploy in such networks. Low-cost sensors using a metal oxide semiconductor (MOS) are presented as a cheap alternative for such deployments due to their compact dimensions and to their sensitivity to CH4. In this study, we demonstrate the ability of two types of MOS sensors (TGS 2611-C00 and TGS 2611-E00) manufactured by Figaro® to reconstruct a CH4 signal, as measured by a high-precision reference gas analyser, during a 7 d controlled release campaign conducted by TotalEnergies® in autumn 2019 near Pau, France. We propose a baseline voltage correction linked to atmospheric CH4 background variations per instrument based on an iterative comparison of neighbouring observations, i.e. data points. Two CH4 mole fraction reconstruction models were compared: multilayer perceptron (MLP) and second-degree polynomial. Emission estimates were then computed using an inversion approach based on the adjoint of a Gaussian dispersion model. Despite obtaining emission estimates comparable with those obtained using high-precision instruments (average emission rate error of 25 % and average location error of 9.5 m), the application of these emission estimates is limited to adequate environmental conditions. Emission estimates are also influenced by model errors in the inversion process.

Abnormal Pavement Condition Detection with Vehicle Posture Data Considering Speed Variations.

Pavement condition monitoring is an important task in road asset management and efficient abnormal pavement condition detection is critical for timely conservation management decisions. The present work introduces a mobile pavement condition monitoring approach utilizing low-cost sensor technology and machine-learning-based methodologies. Specifically, an on-board unit (OBU) embedded with an inertial measurement unit (IMU) and global positioning system (GPS) is applied to collect vehicle posture data in real time. Through a comprehensive analysis of both time domain and frequency domain data features for both normal and abnormal pavement conditions, feature engineering is conducted to identify how the most important features affect abnormal pavement condition recognition. Six machine learning models are then developed to identify different types of pavement conditions. The performance of different algorithms and the significance of different features are then analyzed. Moreover, the influence of vehicle speed on pavement condition assessment is further examined and classification models for different speed intervals are developed. The results indicate that the random forest (RF) model that considers vehicle speed achieves the best performance in pavement condition monitoring. The outcomes of the present work would contribute to cost-effective pavement condition monitoring and provide an important reference for pavement maintenance sectors.

Enhancing hygrothermal monitoring of wet construction with digital twins

The development of digital building twins in the Architecture, Engineering, Construction and Operation (AECO) sector requires the adoption of adequate monitoring strategies for building components. This article presents a proposal and validation of a digital twin-enabled hygrothermal monitoring procedure applied to lightweight concrete using an IoT Arduino-based prototype. This study conducted tests to measure moisture in three types of lightweight concrete for over a year. Temperature and relative humidity sensors were embedded in the lightweight concrete samples. The recorded data were compared to four in-situ surface moisture meters and gravimetric analysis. Although surface moisture meters have different reference scales, the qualitative assessment is similar. The embedded sensors preserved their performance levels during the entire testing period, even those exposed to relative humidity levels near 100 %. The continuous monitoring of temperature and relative humidity in non-structural construction elements, using low-cost sensors embedded during construction, is proven to be viable.

Copper Micro-Flowers for Electrocatalytic Sensing of Nitrate Ions in Water.

The progressive increase in nitrate's (NO3-) presence in surface and groundwater enhances environmental and human health risks. The aim of this work is the fabrication and characterization of sensitive, real-time, low-cost, and portable amperometric sensors for low NO3- concentration detection in water. Copper (Cu) micro-flowers were electrodeposited on top of carbon screen-printed electrodes (SPCEs) via cyclic voltammetry (with voltage ranging from -1.0 V to 0.0 V at a scan rate of 0.1 V s-1). The obtained sensors exhibited a high catalytic activity toward the electro-reduction in NO3-, with a sensitivity of 44.71 μA/mM. They had a limit of detection of 0.87 µM and a good dynamic linear concentration range from 0.05 to 3 mM. The results were compared to spectrophotometric analysis. In addition, the devices exhibited good stability and a maximum standard deviation (RSD) of 5% after ten measurements; reproducibility, with a maximum RSD of 4%; and repeatability after 10 measurements with the RSD at only 5.63%.

Advancements in miniaturized infrared spectroscopic-based volatile organic compound sensors: A systematic review

The global trends of urbanization and industrialization have given rise to critical environmental and air pollution issues that often receive insufficient attention. Among the myriad pollution sources, volatile organic compounds (VOCs) stand out as a primary cluster, posing a significant threat to human society. Addressing VOCs emissions requires an effective mitigation action plan, placing technological development, especially in detection, at the forefront. Photonic sensing technologies rooted in the infrared (IR) light and matter interaction mechanism offer nondestructive, fast-response, sensitive, and selective chemical measurements, making them a promising solution for VOC detection. Recent strides in nanofabrication processes have facilitated the development of miniaturized photonic devices and thus sparked growing interest in the creation of low-cost, highly selective, sensitive, and fast-response IR optical sensors for VOC detection. This review work thus serves a timely need to provide the community a comprehensive understanding of the state of the art in this field and illuminate the path forward in addressing the pressing issue of VOC pollution.

Response of low-cost sensors to high PM 2.5 concentrations during bushfire and haze events

Response of low-cost sensors to high PM _2.5 concentrations during bushfire and haze events

Evaluation of low-cost sensors to integrate in a water quality monitor for real-time measurements.

Low-cost sensors integrated with the Internet of Things can enable real-time environmental monitoring networks and provide valuable water quality information to the public. However, the accuracy and precision of the values measured by the sensors are critical for widespread adoption. In this study, 19 different low-cost sensors, commonly found in the literature, from four different manufacturers are tested for measuring five water quality parameters: pH, dissolved oxygen, oxidation-reduction potential, turbidity, and temperature. The low-cost sensors are evaluated for each parameter by calculating the error and precision compared to a typical multiparameter probe assumed as a reference. The comparison was performed in a controlled environment with simultaneous measurements of real water samples. The relative error ranged from 0.33 to 33.77%, and most of them were 5%. The pH and temperature were the ones with the most accurate results. In conclusion, low-cost sensors are a complementary alternative to quickly detect changes in water quality parameters. Further studies are necessary to establish a guideline for the operation and maintenance of low-cost sensors.

European beech spring phenological phase prediction with UAV-derived multispectral indices and machine learning regression

Acquiring phenological event data is crucial for studying the impacts of climate change on forest dynamics and assessing the risks associated with the early onset of young leaves. Large-scale mapping of forest phenological timing using Earth observation (EO) data could enhance our understanding of these processes through an added spatial component. However, translating traditional ground-based phenological observations into reliable ground truthing for training and validating EO mapping applications remains challenging. This study explored the feasibility of predicting high-resolution phenological phase data for European beech (Fagus sylvatica) using unoccupied aerial vehicle (UAV)-based multispectral indices and machine learning. Employing a comprehensive feature selection process, we identified the most effective sensors, vegetation indices, training data partitions, and machine learning models for phenological phase prediction. The model that performed best and generalized well across various sites utilized Green Chromatic Coordinate (GCC) and Generalized Additive Model (GAM) boosting. The GCC training data, derived from the radiometrically calibrated visual bands of a multispectral sensor, were predicted using uncalibrated RGB sensor data. The final GCC/GAM boosting model demonstrated capability in predicting phenological phases on unseen datasets within a root mean squared error threshold of 0.5. This research highlights the potential interoperability among common UAV-mounted sensors, particularly the utility of readily available, low-cost RGB sensors. However, considerable limitations were observed with indices that implement the near-infrared band due to oversaturation. Future work will focus on adapting models to better align with the ICP Forests phenological flushing stages.

Estimating incident infrared radiation intensity on a horizontal surface

The study aims to characterize external IR heating sources, operating in free convection atmosphere, qualitatively and quantitatively for various applications. It employs non-invasive IR thermography to measure steady-state irradiations from a ceramic IR heater at variable voltages (60–150 V) using the surface energy budget (SEB) method which relies on only two measured parameters (surface and ambient temperature). The free convection heat transfer from the plate is demarcated between the thermal boundary layer (TBL) and buoyant plume-dominated region by temperature histogram tool. Nearly 78 ± 5 % of the plate area is occupied by the hot plume(s) in the studied voltage range. Estimated irradiation agrees fairly well with one another (within ± 10 %). Additionally, the time response of the plate was measured to be ∼ 120 s. This fast response can be made even faster, by reducing the thermal mass, eventually leading to low-cost IR sensors.

Low-cost system application for policy assessment: a case study from Berlin

Local policies are part of the toolbox available to decision makers to improve air quality but their effectiveness is underevaluated and underreported. We evaluate the impact of the pedestrianization of a street in the city centre of Berlin on the local air pollution. Nitrogen dioxide (NO2) was measured on the street where the policy was implemented and on two parallel streets using low-cost sensor systems supported by periodic calibrations against reference-grade instruments and constrained by passive samplers. Further measurements of NO2 were conducted with a reference-grade instrument mounted on a mobile platform. The concentrations were evaluated against the urban background (UB) to isolate the policy-related signal from natural fluctuations, long-term trends and the COVID-19 lockdown. Our analysis shows that the most likely result of the intervention is a reduced NO2 concentrations to the level of the UB on weekdays for the pedestrian zone. Kerbside NO2 concentrations exhibited substantial differences to the concentrations measured at lampposts highlighting the difficulty for such measurements to capture personal exposure. The results have implications for policy, showing that an intervention on the local traffic patterns can possibly be effective in improving local air quality.

Application of Low-Cost IoT Sensors for Smart Public Transportation

Application of Low-Cost IoT Sensors for Smart Public Transportation

Transferability of machine-learning-based global calibration models for NO2 and NO low-cost sensors

Abstract. It is essential to accurately assess and verify the effects of air pollution on human health and the environment in order to develop effective mitigation strategies. More accurate analysis of air pollution can be achieved by utilizing a higher-density sensor network. In recent studies, the implementation of low-cost sensors has demonstrated their capability to quantify air pollution at a high spatial resolution, alleviating the problem of coarse spatial measurements associated with conventional monitoring stations. However, the reliability of such sensors is in question due to concerns about the quality and accuracy of their data. In response to these concerns, active research efforts have focused on leveraging machine learning (ML) techniques in the calibration process of low-cost sensors. These efforts demonstrate promising results for automatic calibration, which would significantly reduce the efforts and costs of traditional calibration methods and boost the low-cost sensors' performance. As a contribution to this promising research field, this study aims to investigate the calibration transferability between identical low-cost sensor units (SUs) for NO2 and NO using ML-based global models. Global models would further reduce calibration efforts and costs by eliminating the need for individual calibrations, especially when utilizing networks of tens or hundreds of low-cost sensors. This study employed a dataset acquired from four SUs that were located across three distinct locations within Switzerland. We also propose utilizing O3 measurements obtained from available nearby reference stations to address the cross-sensitivity effect. This strategy aims to enhance model accuracy as most electrochemical NO2 and NO sensors are extremely cross-sensitive to O3. The results of this study show excellent calibration transferability between SUs located at the same site (Case A), with the average model performance being R2 = 0.90 ± 0.05 and root mean square error (RMSE) = 3.4 ± 0.9 ppb for NO2 and R2 = 0.97 ± 0.02 and RMSE = 3.1 ± 0.8 ppb for NO. There is also relatively good transferability between SUs deployed at different sites (Case B), with the average performance being R2 = 0.65 ± 0.08 and RMSE = 5.5 ± 0.4 ppb for NO2 and R2 = 0.82 ± 0.05 and RMSE = 5.8 ± 0.8 ppb for NO. Interestingly, the results illustrate a substantial improvement in the calibration models when integrating O3 measurements, which is more pronounced when SUs are situated in regions characterized by elevated O3 concentrations. Although the findings of this study are based on a specific type of sensor and sensor model, the methodology is flexible and can be applied to other low-cost sensors with different target pollutants and sensing technologies. Furthermore, this study highlights the significance of leveraging publicly available data sources to promote the reliability of low-cost air quality sensors.