Low-cost Monitoring Devices Research Articles

Lateral-flow assays for bovine paratuberculosis diagnosis.

Mycobacterium avium subsp. paratuberculosis (MAP) causes bovine paratuberculosis (PTB). PTB is responsible for significant economic losses in dairy herds around the word. PTB control programs that rely on testing and culling of test-positive cows have been developed. Current diagnostics, such as ELISA for detecting MAP antibodies in serum samples and PCR detecting MAP DNA in feces, have inadequate sensitivity for detecting subclinical animals. Innovative "omics" technologies such as next-generation sequencing (NGS) technology-based RNA-sequencing (RNA-Seq), proteomics and metabolomics can be used to find host biomarkers. The discovered biomarkers (RNA, microRNAs, proteins, metabolites) can then be used to develop new and more sensitive approaches for PTB diagnosis. Traditional approaches for measuring host antibodies and biomarkers, such as ELISAs, northern blotting, quantitative reverse-transcriptase polymerase chain reaction (RT-qPCR), cDNA microarrays, and mass spectrometry are time-consuming, expensive, and sometimes exhibit poor sensitivity. With the rapid development of nanotechnology, low-cost monitoring devices for measuring antibodies against MAP proteins in point-of-care (POC) settings have been developed. Lateral flow assays (LFAs), in particular, are thought to be appropriate for the on-site detection of antibodies to MAP antigens and/or host biomarkers. This review aims to summarize LFAs that have recently been developed to accurately detect antibodies against MAP antigens, as well as the benefits that host biomarkers linked with MAP infection give to PTB diagnosis. The identification of these novel biomarkers could be the basis for the development of new LFAs. The dairy industry and producers are likely to benefit from reliable and rapid technologies capable of detecting MAP infection in situ to establish a quick and sensitive PTB diagnosis.

Equine Gait Recognition using Wearable Technology for Endurance Monitoring: A Preliminary Study

To overcome limitation in equine gait monitoring, a low-cost monitoring device is needed especially for long-distance event performance monitoring. Current devices capture horse gait in limited range of distance, limited duration usage and expensive. Mobile application such as Androsensor has shown a great potential in horse gait recognition. However, further study is still warranted in order to determine the reliability of this kind of mobile application for performance monitoring especially for long-distance event such as endurance. In this study, a validation test of Androsensor mobile application for equine gait patterns recognition was conducted before the data can be applied for long-distance performance monitoring. Two horses (Thoroughbred) were used in this preliminary study. A unit of inertia measurement unit sensor (IMU) (Delsys Trigno Avanti Sensor) with 150 Hz sampling rate and a mini smartphone installed with Androsensor apps with 200 Hz sampling rate were attached together on horse forelimb pastern to compare the acceleration of horse gait signal captured by both devices. The data captured by both devices were analyzed. The correlation coefficient between an established device (IMU) and the Androsensor apps have shown a good correlation with higher correlation coefficient (r > 0.5) for walk, trot and canter. This finding indicates that the Androsensor mobile application has a great potential to be used in equine gait research and performance monitoring. It also can offer a wide function in this field as it is cheap and wearable.

A cost-effective IoT-based intelligent indoor air quality monitoring

Air quality monitoring has become a critical issue for human beings as it is related to living quality. Poor air quality harms human health, worsening respiratory diseases, increasing heart risks, impacting brain health, and endangering vulnerable populations. Various countries legislate air quality norms related to air monitoring and measurement standards. Nonetheless, most of the monitoring stations for air pollution are set up outdoors and monitor sources of air pollution and greenhouse gases in larger regions, neglecting the importance of indoor air quality (IAQ). Thus, monitoring the IAQ for better healthcare has become necessary. However, low-cost sensors are often associated with design compromises that hamper data reliability. Although scientists have extensively studied IAQ, the practical application of low-cost devices in indoor environments has yet to be investigated. We explored the effectiveness of a low- cost IAQ experimental monitoring device by studying the quality of the results gathered from the reference monitor unit. We hypothesized that the advanced technology and sensors would provide accurate and valid data to make the low-cost IAQ monitor feasible for widespread usage. Our goal was to provide an intelligent, movable, and cost-effective indoor air quality monitoring unit which has been designed to simulate users' indoor routes and detect air quality in real-time. Since, the main factor affecting the accuracy and validity of the measurements results from its orthogonal design. Our results revealed a low-cost monitoring device can be used with efficient design and by using sensors that are highly accurate which are low-cost for accurate and valid readings.

Current advances and future challenges of AIoT applications in particulate matters (PM) monitoring and control

Air pollution is at the center of pollution-control discussion due to the significant adverse health effects on individuals and the environment. Research has shown the association between unsafe environments and different sizes of particulate matter (PM), highlighting the importance of pollutant monitoring to mitigate its detrimental effect. By monitoring air quality with low-cost monitoring devices that collect massive observations, such as Air Box, a comprehensive collection of ground-level PM concentration is plausible due to the simplicity and low-cost, propelling applications in agriculture, aquaculture, and air quality, water resources, and disaster prevention. This paper aims to view IoT-based systems with low-cost microsensors at the sensor, network, and application levels, along with machine learning algorithms that improve sensor networks’ precision, providing better resolution. From the analysis at the three levels, we analyze current PM monitoring methods, including the use of sensors when collecting PM concentrations, demonstrate the use of IoT-based systems in PM monitoring and its challenges, and finally present the integration of AI and IoT (AIoT) in PM monitoring, indoor air quality control, and future directions. In addition, the inclusion of Taiwan as a site analysis was illustrated to show an example of AIoT in PM-control policy-making potential directions.

Occupational health and safety management case study: Establishment of PM10 Contour map

Atmospheric dust is a significant air pollution problem. Particulate matter is small solid particles floating in the air. It is caused by objects that have been smashed, hit, crushed, and crushed until they break down into small parts. When blown by the wind, it will spread in the air. And fell to the ground the fall time is slow or fast depending on the dust particles' weight. Dust is divided into large dust, including small dust called PM10 (dust with a diameter from 10 microns down). The stone mill problem has been a long-time problem. Many related government agencies try to fix the problem of dust pollution and health safety. PM10 disperse from the source to the community nearby the stone mill was measured by the low-cost dust monitoring device. The result found that the 24-hour mean PM10 particulate matter was 0.090 mg/m3, which was also lower than the statutory permissible 24-hour Mean PM10 at no more than 0.12 mg/m3. The PM10 dust contour map can be used for health risk management planning, primarily to mitigate the pollution to the community environment and their health under some usage limitations changes.

Design, characterization and indoor validation of the optical soiling detector “DUSST”

Nowadays, photovoltaic (PV) technology has reached a high level of maturity in terms of module efficiency and cost competitiveness in comparison with other energy technologies. As PV has achieved high levels of deployment, the development of devices that can help to reduce PV operation and maintenance costs has become a priority. Soiling can be cause of significant losses in certain PV plants and its detection has become essential to ensure a correct mitigation. For this reason, accurate and low-cost monitoring devices are needed. While soiling stations have been traditionally employed to measure the impact of soiling, their high cost and maintenance have led to the development of innovative low-cost optical sensors, such as the device presented in this work and named “DUSST” (Detector Unit for Soiling Spectral Transmittance). The thermal characterization of DUSST’s components and the methodology used to predict soiling transmittance losses are presented in this study. The results show that the losses can be predicted with an error lower than 1.4%. The method has been verified with an experimental campaign with naturally soiled coupons exposed outdoors in Jaén, Spain.

Formaldehyde and Total VOC (TVOC) Commercial Low-Cost Monitoring Devices: From an Evaluation in Controlled Conditions to a Use Case Application in a Real Building

Formaldehyde and volatile organic compounds (VOCs) are major indoor pollutants with multiple origins. Standard methods exist to measure them that require analytical expertise and provide, at best, an average value of their concentrations. There is a need to monitor them continuously during periods of several days, weeks, or even months. Recently, portable devices have become available. Two categories of portable devices are considered in this research paper: connected objects for the general public (price <500 €) and monitoring portable devices for professional users (price in the range >500 to 5000 €). The ISO method (ISO 16000-29) describes the standard for VOC detector qualification. It is quite complex and is not well adapted for a first qualitative evaluation of these low-cost devices. In this paper, we present an experimental methodology used to evaluate commercial devices that monitor formaldehyde and/or total volatile organic compounds (TVOC) under controlled conditions (23 °C, 50–65% relative humidity (RH)). We conclude that none of the connected objects dedicated to the general public can provide reliable data in the conditions tested, not even for a qualitative evaluation. For formaldehyde monitoring, we obtained some promising results with a portable device dedicated to professional users. In this paper, we illustrate, with a real test case in an office building, how this device was used for a comparative analysis.

Cassava leaf NDVI - Artificial Neural Network (CaNDVI-ANN): A Low Cost, Portable and Non-Invasive Cassava Plant Health Monitoring Device

Cassava leaf NDVI - Artificial Neural Network (CaNDVI-ANN): A Low Cost, Portable and Non-Invasive Cassava Plant Health Monitoring Device

Use of Low-Cost Air Quality Monitoring Devices for Assessment of Road Transport Related Emissions

Comprehensive information on air quality is very important for development and assessment of air pollution reduction measures, especially for urban areas facing these problems. Such information is useful not only for monitoring of air quality levels but also for validation of air quality modelling tools. These tools are used, among many application fields, to assess road transport related air pollution as well as to investigate impacts of traffic management measures. Today, in addition to high precision monitoring stations in the cities, there are several low-cost monitoring devices available which can provide additional information on a larger area with less costs. This paper investigates the utilization of such devices as an additional data source for air quality assessment through a case study in the city of Munich and focuses on PM10 measurements. In this context, hourly PM10 values are measured with ten low-cost devices in the study area and it is checked if the measured values from devices capture general trends in air quality levels. Firstly, a statistical analysis is conducted which analyses officially reported historical PM10 concentrations, weather conditions and traffic data for the use case area from the year 2016. The analysis showed strong correlations between PM10 concentrations and background pollutant levels, wind conditions as well as traffic volumes. The evaluation focuses on investigation of whether the values from low-cost devices also showed similar trends as expected by the statistical correlations. The results represented some of the anticipated relationships, however, others could not be easily concluded. The experiment implies the importance of extensive measurement periods and frequent calibration of devices. In addition, the study demonstrates the difficulties of interpreting point measurement data in terms of causalities. Use of dispersion models can additionally help to understand the reasons of unexpected high or low concentrations at certain areas, as they consider complex relationships between factors affecting air quality such as meteorological conditions and the built structure.

Telerehabilitation in individuals with severe acquired brain injury: Rationale, study design, and methodology.

Background:Severe acquired brain injury (SABI) rehabilitation should be as intensive and long as to allow the patients get the best independence and quality of life (QoL), but facing with the rehabilitation socioeconomic burden. Telerehabilitation (TR) could supply frail subjects requiring long-term rehabilitation.Methods:A multicenter, prospective, parallel design, single-blind trial will be conducted at the IRCCS Neurolesi Bonino Pulejo (Messina, Italy) and IRCCS Hospital San Camillo (Venice, Italy) involving patients suffering from SABI and requiring home motor and cognitive rehabilitation. We will investigate the use of TR, based on advanced Information and Communication Technology (ICT) solutions, taking into account that the supervision of rehabilitation at home will be enriched with the counseling and vital parameters monitoring. The enrolled patients will be balanced for pathology, and randomized in 2 groups, performing TR (G1) or standard rehabilitation training (G2), respectively, according to a pc-generated random assignment. TR will be delivered by means of an advanced video-conferencing system, whereas the patient will be provided with low-cost monitoring devices, able to collect data about his/her health status and QoL. In both the groups each treatment (either cognitive or motor, or both as per patient functional status) will last about 1 hour a day, 5 days/week, for 12 weeks. Two structured telephone interviews will be administered to the patients (when possible) and/or their caregivers, and to all the healthcare professionals involved in the patient management, 1 week after the beginning and at the end of the TR. All the patients will undergo a complete neurological and cognitive examination performed by skilled physicians and psychologists, blindly. Clinical evaluations will be administered blindly, before and after the treatments.Results:the data of this study should demonstrate that TR is at least non-inferior in comparison with the same amount of usual territorial rehabilitative physical treatments, taking into account patients’ functional recovery, psychological well-being, caregiver burden, and healthcare costs.Conclusion:data coming from this study could demonstrate the usefulness of TR in facing the rehabilitation socioeconomic burden of managing patients with SABI, so to allow the patients get the best independence and quality of life (QoL).

Data assimilation and multisource decision-making in systems biology based on unobtrusive Internet-of-Things devices

Biological and medical diagnoses depend on high-quality measurements. A wearable device based on Internet of Things (IoT) must be unobtrusive to the human body to encourage users to accept continuous monitoring. However, unobtrusive IoT devices are usually of low quality and unreliable because of the limitation of technology progress that has slowed down at high peak. Therefore, advanced inference techniques must be developed to address the limitations of IoT devices. This review proposes that IoT technology in biological and medical applications should be based on a new data assimilation process that fuses multiple data scales from several sources to provide diagnoses. Moreover, the required technologies are ready to support the desired disease diagnosis levels, such as hypothesis test, multiple evidence fusion, machine learning, data assimilation, and systems biology. Furthermore, cross-disciplinary integration has emerged with advancements in IoT. For example, the multiscale modeling of systems biology from proteins and cells to organs integrates current developments in biology, medicine, mathematics, engineering, artificial intelligence, and semiconductor technologies. Based on the monitoring objectives of IoT devices, researchers have gradually developed ambulant, wearable, noninvasive, unobtrusive, low-cost, and pervasive monitoring devices with data assimilation methods that can overcome the limitations of devices in terms of quality measurement. In the future, the novel features of data assimilation in systems biology and ubiquitous sensory development can describe patients’ physical conditions based on few but long-term measurements.

PerKApp: A general purpose persuasion architecture for healthy lifestyles

Automatically monitoring and supporting healthy lifestyle is a recent research trend, fostered by the availability of low-cost monitoring devices, and it can significantly contribute to the prevention of chronic diseases deriving from incorrect diet and lack of physical activity. In this paper, we present a general purpose architecture for persuasion scenarios and behavioral change. The architecture is designed to be easily portable across languages and domains and has been implemented and evaluated in a specific system for Workplace Health Promotion (WHP) called PerKApp. PerKApp provides a fully fledged platform supporting the remote monitoring of workers lifestyle by providing real-time feedback through persuasive context-based messages when necessary.

Calibration of low-cost sound level meters using machine learning techniques

The assessment of environmental noise pollution from complex sources that are not represented in traditional noise maps requires a long term monitoring network to manage citizen complaints, a network of low-cost sound level meters is a practical option to analyze real-world cases, however the accuracy of the measurements is a concern when this information is used to evaluate regulation accomplishment. Previous work on low-cost monitoring devices has been carried out in microphone comparison, processing board selection and recently in MEMS microphone use. In the present work the use of reinforcement learning techniques is explored to calibrate the sound pressure levels generated by a low-cost monitoring device using a class one sound level meter as reference in a continuous measurement setup. Machine learning based models are known to take into account strong non-linearities which are effective to get an alternative calibration method for low-cost sensors.

An IOT Enabled Air Quality Measurement

Objectives: To develop a portable, low cost and efficient pollution monitoring device. Embedded module can be placed on rooftop and display will show the ambient quality values. Methods/Statistical Analysis: We have adopted the method of data acquisition in real time using semiconductor sensors of MQ series. The sensor data is sampled with help of AVR microcontroller, processed with the program and the values are displayed on LCD. Power consumption is tried to keep low by using sleep mode of microcontroller. The processed data is uploaded on cloud platform and analysed for critical parameters. Findings: The data could be accessed by the user and even government authorities to monitor air quality in real time and take precautionary steps. This low cost & portable solution has semiconductor sensors interfaced with AVR microcontroller. It needs very low power & to make it work independently we can include solar energy to power it. The data taken is validated at different location and with the data of standard equipment setup at locations. Application/ Improvements: Device finds application in industries, educational institutes and public places. Adding more sensors for detection of other gases would improve monitoring of various gases in environment. Keywords: Air Quality, Arduino, Gas Sensors, IOT, Low Power

Validate personal air-pollution sensors.

Alastair Lewis and Peter Edwards call on researchers to test the accuracy of low-cost monitoring devices before regulators are flooded with questionable air-quality data.

Testing the accuracy of smartphones and sound level meter applications for measuring environmental noise

This paper reports on experimental tests undertaken to assess the capability of noise monitoring applications to be utilized as an alternative low cost solution to traditional noise monitoring using a sound level meter. The methodology consisted of testing 100 smartphones in a reverberation room. Broadband white noise was utilized to test the ability of smartphones to measure noise at background, 50, 70 and 90dB(A) and these measurements were compared with true noise levels acquired via a calibrated sound level meter. Tests were conducted on phones using the Android and iOS platforms. For each smartphone, tests were completed separately for leading noise monitoring apps culminating in 1472 tests. The results suggest that apps written for the iOS platform are superior to those running on the Android platform. They show that one of the apps tested – SLA Lite – is within ±1dB of true noise levels across four different reference conditions. The results also show that there is a significant relationship between phone age and its ability to measure noise accurately. The research has implications for the future use of smartphones as low cost monitoring and assessment devices for environmental noise.

An automated system for quantitative analysis of newborns' oral-motor behavior and coordination during bottle feeding.

In this work a novel unobtrusive technology-aided system is presented and tested for the assessment of newborns' oral-motor behavior and coordination during bottle feeding. A low-cost monitoring device was designed and developed in order to record Suction (S) and Expression (E) pressures from a typical feeding bottle. A software system was developed to automatically treat the data and analyze them. A set of measures of motor control and coordination has been implemented for the specific application to the analysis of sucking behavior. Experimental data were collected with the developed system on two groups of newborns (Healthy vs. Low Birth Weight) in a clinical setting. We identified the most sensitive S features to group differences, and analyzed their correlation with S/E coordination measures. Then, Principal Component Analysis (PCA) was used to explore the system suitability to automatically identify peculiar oral behaviors. Results suggest the suitability of the proposed system to perform an objective technology-aided assessment of the newborn's oral-motor behavior and coordination during the first days of life.

Low cost and batteryless sensor-enabled radio frequency identification tag based approaches to identify patient bed entry and exit posture transitions

IntroductionFalls in hospitals and residential care facilities commonly occur near the bed. The aim of this study was to investigate the accuracy of a continuously wearable, batteryless, low power and low cost monitoring device (Wearable Wireless Identification and Sensing Platform) with a single kinematic sensor capable of real-time monitoring to automatically detect bed entry and exit events. Materials and methodsThree dimensional acceleration readings and the strength of the transmitted signal from the WISP was interpreted to identify bed exit events and sensitivity, specificity and Receiving Operator Curves (ROC) were determined. ResultsThe sensor located over sternum method performed best with sensitivity and specificity values of 92.8% and 97.5% respectively for detecting bed entry and values of 90.4% and 93.80% respectively for bed exit. On the other hand, the sensor-on-mattress algorithm achieved sensitivity and specificity values of 84.2% and 97.4% respectively for bed entry and 79% and 97.4% for bed exit detection. ConclusionThe WISP located over the sternum method is the preferred method to detect bed entry and exit. However, further work in frail older people is required to confirm the performance of this method.

Digital health investment opportunities abound, but standouts deliver disruptive change

The convergence of ubiquitous smartphones, wireless Internet, and low-cost monitoring devices, is driving the emergence of a new world of digital health. At the same time, cost pressures on healthcare are creating demand for new ways to not just improve the way patients receive information and care and the way doctors provide it, but fundamentally change the way they interact with each other. The article discusses a venture capitalist's approach to investing in the sector.Â

Municipal Environmental-Monitoring System

Nowadays protection of our environment is playing more and more significant role and within this topic the protection of the human aspects is even more important. The continuous monitoring of the municipal environment came into foreground in Hungary also through the past years. Within the Faculty of Engineering of the University of Pannonia a unique organizational unit was established under the name “Sustainable Development Environmental an Informatics Cooperative Research Centre”. This centre started an R+D project on the main topic of environmental protection and its technologies. The goal of this paper is to summarize a measuring method of municipal-environmental parameters (e.g.: CO, O3, NOx, NO2, C6H6) using low-cost monitoring devices and open source visualization software. The system integrates the measured data in a GIS for further processing and publishing. The sample-area was given by the location of the University. Hence, the test-measurements were carried out in Veszprem, Hungary. The developed system gives the power of data integration so the user can manage one uniform user interface for processing the data gained from the environmental measurements. On the whole it can be stated that the system is suitable for collecting environmental data with specific low costs at minimal infrastructural build-up. Further measurements and modeling will be managed in the future using the developed system in order to compare the present results of air quality of Veszprem to the collected environmental parameters in other cities and counties.