Sensor Kit Research Articles

18β-glycyrrhetinic Acid Modulated Autophagy is Cytotoxic to Breast Cancer Cells.

The development of endocrine therapy resistance in the luminal A subtype of breast cancer is related to the appearance of protective autophagy. The bioactive component from the root of licorice, 18β-glycyrrhetinic acid (18β-GA), has many antitumor properties. Whether 18β-GA can modulate autophagy to inhibit proliferation of the luminal A subtype is still unclear. The proportion of apoptosis caused by 18β-GA in MCF-7 and T-47D cells was determined using flow cytometry. The autophagy marker, LC3-II conversion, was investigated using Western blotting, and a PremoTM Tandem Autophagy Sensor Kit. We found that the concentration (150-μM) of 18β-GA caused caspase-dependent apoptosis and LC3-II accumulation or blocked autophagic flux. Moreover, 18β-GA-mediated apoptosis was improved using rapamycin but reversed by 3-methyladenine (3-MA) addition. The phosphorylation level of Jun-amino-terminal kinase (JNK) was increased significantly in the 18β-GA treatment and combined incubation using rapamycin. A JNK inhibitor (SP600125) significantly inhibited 18β-GA-mediated apoptosis, LC3-II accumulation and rescued the numbers of MCF-7 and T-47D colony formation. Especially, 18β-GA can inhibit xenograft tumor growth in BALB/c nude mice. These data indicate the combination of 18β-GA with rapamycin or 3-MA can sensitize or decrease MCF-7 and T-47D cells to 18β-GA-induced apoptosis, respectively. 18β-GA modulated autophagy is cytotoxic to luminal A subtype breast cancer cells through apoptosis promotion and JNK activation.

Civil air quality monitoring as an alternative and supplement to the National Air Quality Monitoring Network

The European initiatives for citizens? inclusion in the air quality monitoring process have impacted the participants? knowledge, and attitudes towards air pollution and have led to a higher sense of community toward activities and measures against air pollution. In the Republic of Serbia, an experiment with self-made sensor kits "Klimerko - Air to the Citizens" started in 2018. In this paper, we have presented the level of agreement of the PM readings from selected Klimerko devices to the PM readings of the reference equivalent PM monitors from the National Air Quality Monitoring Network in the cities of Bor and Belgrade. The Klimerko devices that we tested showed excellent stability and reliability during the comparison period. The conclusion of our investigation is that the properly calibrated Klimerko devices could be applied for indicative measurements of PM mass concentrations in the ambient air.

Blockchain-Based Cloud-Enabled Security Monitoring Using Internet of Things in Smart Agriculture

The Internet of Things (IoT) has rapidly progressed in recent years and immensely influenced many industries in how they operate. Consequently, IoT technology has improved productivity in many sectors, and smart farming has also hugely benefited from the IoT. Smart farming enables precision agriculture, high crop yield, and the efficient utilization of natural resources to sustain for a longer time. Smart farming includes sensing capabilities, communication technologies to transmit the collected data from the sensors, and data analytics to extract meaningful information from the collected data. These modules will enable farmers to make intelligent decisions and gain profits. However, incorporating new technologies includes inheriting security and privacy consequences if they are not implemented in a secure manner, and smart farming is not an exception. Therefore, security monitoring is an essential component to be implemented for smart farming. In this paper, we propose a cloud-enabled smart-farm security monitoring framework to monitor device status and sensor anomalies effectively and mitigate security attacks using behavioral patterns. Additionally, a blockchain-based smart-contract application was implemented to securely store security-anomaly information and proactively mitigate similar attacks targeting other farms in the community. We implemented the security-monitoring-framework prototype for smart farms using Arduino Sensor Kit, ESP32, AWS cloud, and the smart contract on the Ethereum Rinkeby Test Network and evaluated network latency to monitor and respond to security events. The performance evaluation of the proposed framework showed that our solution could detect security anomalies within real-time processing time and update the other farm nodes to be aware of the situation.

Chromoionophore integrated hexagonal mesopore silica monolith as reusable two-in-one solid-state sensor for the sequential optical recognition of ultra-trace Co2+ and Fe3+ ions

The study envisages a novel, two-in-one, pH-based, discriminative solid-state dual colorimetric sensor for the selective and sequential sensing and recovery of industrially/environmentally relevant Co2+ and Fe3+ ions. The proposed solid-state optical sensor embraces a direct and uniform dispersion of 4′-phenyl-2,2′,6′,2″-terpyridine (PTPy) - a heterocyclic chromoionophore, onto the hexagonal array of honeycomb structured mesoporous silica monolith. The surface morphology, microstructural pattern, phase transition properties, elemental composition and pore features of the sensor material are examined using FE-SEM-EDAX, HR-TEM-SAED, p-XRD, XPS, FT-IR, TGA and BET/BJH analysis. The silica monolith reveals a high surface area and voluminous porosity that ensures a homogeneous dispersion of the chromoionophoric molecules, thus enabling ultra-fast sensing kinetics through easy ion diffusion to the chelating sites. The solid-state colorimetric sensor exhibits a linear response range in the concentration range of 0–100 μg/L, with 0.5 and 0.3 μg/L as detection limits for Co2+ and Fe3+, respectively. The proposed optical sensor is loaded with intelligent pH-based selective ion-sensing, easy operation, excellent aqueous compatibility, high chemical/thermal stability, brilliant ion-selectivity and sensitivity. The solid-state sensor exhibits good reusability for several cycles. It can serve as an efficient, low-cost sensor kit for continuous real-time environmental monitoring and an ion-concentrator for industrial applications' resource recovery.

Automatic quantification of living cells via a non-invasive achromatic colorimetric sensor through machine learning-assisted image analysis using a smartphone

Several cell quantification techniques including optical and electrochemical methods have been developed. However, these techniques have certain limitations such as the requirement of considerable effort and time or insufficient feasibility and accessibility. Moreover, cell quantification is generally performed in an invasive manner, which makes performing continuous cell experiments difficult and leads to the requirement of large number of samples. In this study, a simple, rapid, and accurate in-situ cell quantification method using an achromatic colorimetric sensor system is proposed. A non-invasive achromatic colorimetric cell quantitation sensor (Ach-CQ sensor) is developed based on the subtractive color model. The color of the sensor changes from black to orange owing to the pH change occurring due to the release of acid molecules such as proton donors and lactate during cellular respiration. The Ach-CQ sensor exhibits a higher color contrast difference and effectively determines the number of cells through color changes within 20 s compared with that of a conventional pH indicator such as bromothymol blue. Moreover, machine learning-assisted, saturation-based image analysis is applied for the facile quantification of living cells to achieve automatic quantification of living cells in a non-invasive manner with a limit of detection of 51 × 104 cells in the case of HeLa cells using a smartphone. Additionally, a 3D-printed Ach-CQ sensor kit is fabricated for on-site cell quantification.

Compositional measurement of gas emissions in the Eastern Carpathians (Romania) using the Multi-GAS instrument: Approach for in situ data gathering at non-volcanic areas

The Multi-GAS, a robust and low-cost instrument for real-time in-situ gas measurements, has previously been used mainly for compositional measurements of active volcanic plumes. Here we demonstrate novel use of a specially designed Multi-GAS instrument adapted to low temperature degassing areas. We performed compositional measurements in the Eastern Carpathians on dry and bubbling gas emissions using a sensor kit that allows measurement of CO2, CH4 and H2S (three major components of low-temperature hydrothermal/volcanic manifestations). Our results demonstrate good agreement between Multi-GAS measurements and independently obtained CO2 concentrations from gas chromatography. We also provide some novel H2S information for some anomalous sites, which we relate to possible alteration processes of sulphide minerals. The use of Multi-GAS in such environments could open new possibilities for data collection at non-volcanic areas and exploration of mineral resources. Moreover, it can also be a useful tool in exploration surveys to select the best sampling sites for more detailed laboratory measurements.

PROTOTYPING SMART HOME FOR IMMOBILIZED PEOPLE: EEG/MQTT-BASED BRAIN-TO-THING COMMUNICATION

Context. Immobilized people face additional barriers in almost all areas of life, including simple operations like turning the light on/off and controlling the air conditioner. The object of the study was to develop the brain-to-thin communication of affordable priceto control the smart home appliances by immobilized people from neck to toes. Objective. The goal of the work is to manage smart home appliances via brain-to-thing communication with EEG non-invasive electrodes, edge IoT devices, and MQTT protocol if the brain and eye control of the disabled work normally. Method. A non-invasive Sichiray TGAM brainwave EEG sensor kit captures signals and then transmit them via Bluetooth to the HC-05 module connected to the Arduino Mega microcontroller. Information about edge IoT devices is presented to the disabled on the LCD 1602 display wired to the same Arduino Mega. The disabled person chooses the option shown on display via the double blink that is detected if the quality of signal equals zero and low/mid gamma waves are less than ten in three consecutive Bluetooth packets. Control commands are sent from Arduino Mega (MQTT publisher) to the edge IoT devices (MQTT subscribers) that analyze them and start a specific operation like opening a door and turning the alarm on/off. Results. Five females and five males of different ages from 8 to 59 years old examined the control of smart home appliances with the Sichiray TGAM brainwave sensor kit. Everyone successfully handled the Sichiray headset and showed satisfaction with the brain-to-thing system. Conclusions. In this work, a smart home concept for immobilized people was developed using the brain-to-thing approach and the MQTT communication between the MQTT publisher, Sichiray TGAM brainwave EEG sensor kit connected via Bluetooth to the Arduino Mega microcontroller, and edge IoT devices total priced at USD 150. The most likely prospect of the presented work is to produce the sample that is ready to market.

EVALUATING A RELIABLE MICROCONTROLLER SYSTEM FOR SITTING BALANCE TREATMENT

Rationale: As time spent seated increases, balanced sitting posture is more important now than ever before. Unbalanced sitting posture deteriorates spinal curvature, which can predispose the pathological discs in the back, leading to irreversible damage. An IoT platform was connected to a chair with pressure sensors attached for this microcontroller device that continuously collected the user's sitting data.
 Procedures: An Arduino Yun microcontroller board was used to monitor sitting posture with the balance feedback system (BFS), which consisted of reliable pressure sensor kits and an IoT platform. Four pressure sensors were coordinated to acquire sitting pressure using the software algorithm, and data was sent to the Thingspeak server and plotted in real-time. The testing session was divided into three stages to verify the system's performance: pre-BFS stage, BFS-Treatment stage, and post-BFS stage. Each stage consisted of the volunteer sitting in the chair for an hour daily for five consecutive days. During the pre-BFS stage, the volunteer sat in the chair to measure their natural seated pressure distribution without using the application. During the BFS-Treatment stage, the volunteer sat in the chair using the application to review pressure data. For the post-BFS stage, the volunteer did not have access to the application in hopes that they could apply their learnings from the previous stage to sit with corrected posture.
 Conclusions: The study concluded that the BFS could monitor the user’s sitting posture and help improve posture to maintain a balanced state. This study can contribute to the prevention of various sitting diseases.

Monitoring Approaches for New-Generation Energy Performance Certificates in Residential Buildings

In 2002, the Energy Performance of Building Directive (EPBD) introduced energy certification schemes to classify and compare building performances to support reaching energy efficiency targets by informing the different actors of the building sectors. However, since its implementation, the Energy Performance Certifications (EPCs) remained unexploited with limited impact on the energy savings targets. In this context, the EPC RECAST project aims at studying a new generation of EPCs with a focus on the residential sector. More in detail, the paper presents and frames a monitoring approach based on low-cost and non-invasive technology for real data collection in existing residential apartments/houses. The method is based on different levels of monitoring selected according to the typology of the building (e.g., detached house, apartment), services (e.g., centralized or local energy generation), and energy vectors (e.g., natural gas or electricity). Three different levels have been identified (named as: basic, medium, and advanced) and for each one, different plug and play monitoring sensor kits have been selected. Six representative pilot buildings have been identified and selected to verify the approach in general and, in particular, the sensors’ applicability and communication, the data reliability, and the monitoring platform. The presented work highlights, on the one hand, the general feasibility of the proposed monitoring approach; on the other, it highlights the difficulty of fully standardizing the sensors kits considering that each building/apartment has specific characteristics and constraints that have to be carefully analyzed. The use of the ultrasonic flow meters represents a good technical option for reducing the cost and the impact on the existing plant system; however, their installation must be verified considering that the logger needs to be powered and the sensors calibrated for collecting reliable data.

An Efficient Smart Streamlet Management System Using Internet of Thing

Waste management is generally handled by the local municipal authority through traditional approaches, but there exist many limitations of these approaches. Considerable number of research has been performed on solid waste management system. However, smart system especially for streamlet waste (liquid) management is unavailable. In this study, we developed an efficient management system for streamlet using Internet of Things. This system informs the concerned municipal authority in real time about the current waste levels of streamlets in a city. For this purpose, sensor kits are deployed over the streamlets that transmit data to the cloud server for storage, processing and analysing via the Internet. The municipal authority can visualise the data in graph-form entries along with a sensor location from the cloud. When the waste level reaches a predefined threshold value, the waste collector is automatically notified through a smart phone in real time. The performance and accuracy of the proposed system is tested based on the conducted case studies. Experimental results show that the proposed system efficiently delivers its desired operations. The implementation of such system in the society will enhance the quality of life and will make opportunities for government and businessmen to invest money on such a project.

Facilitating privacy-preserving activity recognition in age-friendly environments through low-power devices

Advances in the Internet of Things (IoT) technologies are being applied in various industries, but lately, they are also finding applications in home-based healthcare systems. Such pervasive healthcare systems aim to enable older adults to receive better and more cost-effective care in their preferred home environment. Battery-powered IoT devices are essential for low-cost deployment, especially in rural areas. However, one of the main challenges for any battery-powered device is energy management so that the period between battery changes is prolonged. This paper proposes an energy-saving approach for a non-invasive passive infrared (PIR) sensor. The proposed method can put the device into a deep sleep to minimize the energy consumption and use hardware wake-up interrupts to make it functional again. The analyzed sensor kit allows the detection of persons, including recognition of their actions, while preserving the privacy of the person. This is very important for age-friendly environments where privacy-preserving is essential.

Research OF Complex Processes Based on Step-By-Step Modeling

Purpose. When solving practical problems that require the creation and further analysis of the model, an important criterion is the labour intensity of modeling. In this regard, the article is aimed at formalizing the modeling process and using the method of step-by-step modeling for the design of technological processes. This approach allows you to design processes and tasks according to the following stages: physical modeling, mathematical modeling, discrete computer modeling and simulation. Methodology. To solve the problem, a methodology of step-by-step modeling is used. The simulation involves 3 stages and uses the decomposition algorithm, i. e. considers the problem from global to detailed. At the first stage of this implementation, the necessary information is collected for the experiment. This information is presented in the form of statistics. In the second stage, further processing takes place, which is performed by checking the compliance of the input data and the process with the question of how this process should be performed. The last stage is the simulation of passages of this fragment, which is represented by a chain of transitions, obtaining statistics of time efficiency of this process, weaknesses of the process and the ability to compare the results obtained during modeling and in the real process, as well as the ability to predict future results and actions. Findings. The technique can be used to study complex technological processes in the enterprise. It allows modeling of complex processes to obtain information about the time efficiency of the technological operation, finding weaknesses in it and patterns in the occurrence of random events that may affect the operation. Using this approach can be very effective in the systems that require constant real-time monitoring, as this tool can be modified by adding sensor kits that will constantly send information to the system or equip an additional system that will provide ready-made information packets. Originality. The method of step-by-step modeling of representation has been improved, which consists in the simultaneous use of physical, mathematical and simulation modeling of complex processes with a set of stages of their implementation. Practical value. The proposed technique is designed for step-by-step modeling of the technological process with the subsequent construction of simulation programming.

Environmental quality characterization of public schools transformed into climate shelters in Barcelona: A pilot innovative study

BACKGROUND AND AIM: The Barcelona’s Climate Shelters pilot project has converted eleven primary schools, considered vulnerable to heat and air pollution, into urban climate shelters by implementing a diverse package of blue (water), green (vegetation) and grey measures (shade and building ventilation). An evaluation strategy to assess the potential environmental benefits achieved on temperature, humidity and air quality has been implemented. METHODS: Each of the eleven pilot schools was equipped with six low-cost Smart Citizen Kits and one multisensor AQMesh pod for climatological (temperature, relative humidity and atmospheric pressure), and air quality monitoring (PM2.5, PM10, NO2, NO, O3, CO and SO2). According to the transformations applied, low-cost kits were deployed at outdoor and indoor locations in intervention and reference areas (control stations). Occasionally, intensive measurement campaigns using additional sensor kits and NO2 passive diffusion tubes were also performed. Data collection began in August 2019 and will last until October 2021. We followed a pre (before school interventions) and post (after summer 2020) evaluation strategy to compare environmental measures trends over time. RESULTS:To date, the pre-intervention results showed that on average schoolchildren were exposed 22% of the days to indoor temperatures higher than 26ºC. Regarding PM2.5 and PM10, outdoor daily mean limits of the World Health Organization (WHO) were exceeded up to 2.4% ( 25μg/m3) and 2.9% ( 50μg/m3) of the days, respectively. Finally, until early 2020 most of the schoolyards were exposed to NO2 levels above the WHO annual mean limit of 40μg/m3. These levels significantly decreased due to the impact of the strict COVID-19 pandemic lockdown. The first post-intervention results will be available at the end of July 2021. CONCLUSIONS:This innovative intervention schools project will help us to identify the most effective solutions, which could be reproduced and further scaled up in the future, both locally and across other cities. KEYWORDS: Air pollution, Climate, Children’s environmental health, Methodological study design, Exposure Assessment.

Multi-modal data collection for measuring health, behavior, and living environment of large-scale participant cohorts.

BackgroundAs mobile technologies become ever more sensor-rich, portable, and ubiquitous, data captured by smart devices are lending rich insights into users’ daily lives with unprecedented comprehensiveness and ecological validity. A number of human-subject studies have been conducted to examine the use of mobile sensing to uncover individual behavioral patterns and health outcomes, yet minimal attention has been placed on measuring living environments together with other human-centered sensing data. Moreover, the participant sample size in most existing studies falls well below a few hundred, leaving questions open about the reliability of findings on the relations between mobile sensing signals and human outcomes.ResultsTo address these limitations, we developed a home environment sensor kit for continuous indoor air quality tracking and deployed it in conjunction with smartphones, Fitbits, and ecological momentary assessments in a cohort study of up to 1,584 college student participants per data type for 3 weeks. We propose a conceptual framework that systematically organizes human-centric data modalities by their temporal coverage and spatial freedom. Then we report our study procedure, technologies and methods deployed, and descriptive statistics of the collected data that reflect the participants’ mood, sleep, behavior, and living environment.ConclusionsWe were able to collect from a large participant cohort satisfactorily complete multi-modal sensing and survey data in terms of both data continuity and participant adherence. Our novel data and conceptual development provide important guidance for data collection and hypothesis generation in future human-centered sensing studies.

Azo dye-based optical probe for the detection toward mimic molecule of date rape drug

The azo dye-based visual responsive optical probe 5-(diheptylamino)-2-((3,5-dinitrothiophen-2-yl)diazenyl)phenol (DP), for the purpose of detection toward the mimic molecule of date rape drug was designed, synthesized and characterized. The recognizing abilities toward the mimic molecule (sodium butyrate) of date rape drug (gamma-hydroxybutyric acid) were examined in water. The prepared molecule was visually, analytically, and theoretically confirmed with the sensing ability of the probe. Developing an easy-to-use sensor kit will work spontaneously with a detectable colorimetric response in daylight. Obtained color change from light turquoise color to bluish-purple toward sodium butyrate containing alcoholic beverages was successfully recognized and confirmed. Even though the color change from light turquoise to bluish-purple obtained, which is not easily recognized clearly with the eye under normal light, UV–Visible spectra indicate the changes, thus the probe can be utilized productively to protect the victim from sexual assault.

Population data and genetic characteristics of 12 X-STR loci using the Investigator® Argus X-12 Quality Sensor kit for the Kedayan population of Borneo in Malaysia.

DNA profiling of X-chromosomal short tandem repeats (X-STR) has exceptional value in criminal investigations, especially for complex kinship and incest cases. In this study, Investigator® Argus X-12 Quality Sensor (QS) kits were successfully used to characterize 12 X-STR loci in 199 unrelated healthy Kedayan individuals living in Sabah and Sarawak, Malaysia. The LG1 haplogroup (DXS8378 - DXS10135 - DXS10148) has the largest HD (0.9799) as compared with all other closely linked haplotype groups examined (LG2; DXS7132-DXS10074-DXS10079, LG3; DXS10103-DXS10101-HPRTB and LG4; DXS10134-DXS7423-DXS10146). Data from statistical analysis showed that high combined of PDM, PDF, MEC_Krüger, MEC_Kishida, MEC_Desmarais, and MEC_Desmarais_duo values (0.999999994405922, 0.99999999999999, 0.999990463834938, 0.999999975914808, 0.999999975985006, and 0.999996491927194, respectively) in the Kedayan. In a two-dimensional scaling (MDS) plot and dendrogram constructed using allele frequencies at the 12 X-STR loci, Kedayan appear to be most closely related to their other Austronesian populations including the Malays and Filipinos as compared with other reference population groups. Findings from the present study thus demonstrate high genetic variability across the 12 tested X-STR loci and can be used for population studies and forensic applications.

Probeware for the Modern Era: IoT Dataflow System Design for Secondary Classrooms

Sensor systems have the potential to make abstract science phenomena concrete for K-12 students. Internet of Things (IoT) sensor systems provide a variety of benefits for modern classrooms, creating the opportunity for global data production, orienting learners to the opportunities and drawbacks of distributed sensor and control systems, and reducing classroom hardware burden by allowing many students to “listen” to the same data stream. To date, few robust IoT classroom systems have emerged, partially due to lack of appropriate curriculum and student-accessible interfaces, and partially due to lack of classroom-compliant server technology. In this article, we present an architecture and sensor kit system that addresses issues of sensor ubiquity, acquisition clarity, data transparency, reliability, and security. The system has a dataflow programming interface to support both science practices and computational data practices, exposing the movement of data through programs and data files. The IoT Dataflow System supports authentic uses of computational tools for data production through this distributed cloud-based system, overcoming a variety of implementation challenges specific to making programs run for arbitrary duration on a variety of sensors. In practice, this system provides a number of unique yet unexplored educational opportunities. Early results show promise for Dataflow as a valuable learning technology from research conducted in a high school classroom.

Building Energy Performance Assessment Using an Easily Deployable Sensor Kit: Process, Risks, and Lessons Learned

In the building and construction sector, the mismatch between predicted and measured energy consumption is a well-known phenomenon called the performance gap. A promising approach to reduce the performance gap and thus improve the current building energy performance assessments are methods based on in-situ measurements. In this work, we present a building assessment process based on a novel, easily deployable wireless sensor kit. The basic sensor kit for building energy assessment presented in this study consists of a heating energy input node, several indoor temperature nodes, an outdoor temperature node, and a heat flux sensor. Specifically, the study outlines a medium-scale deployment of the sensor kit in eight occupied single-family homes in Switzerland and identifies the benefits of such an approach in the estimation of the overall heat loss coefficient and U-values. The findings of this study suggest that such sensor kits could be effectively used for rapid building performance assessment, and the paper concludes by outlining the potential benefits and implementation challenges of a larger scale study.

VOCkit: A low-cost IoT sensing platform for volatile organic compound classification

Improvements in small sized sensors allow the easy detection of the presence of Volatile Organic Compounds (VOCs) in the air using easy-to-deploy Internet of Things (IoT) devices. However, classifying what VOC exists in the environment still remains as a complex task. Knowing what VOCs are in the air can help us remove the main cause that vents VOC materials as a way to maintain clean air quality. In this work, we present VOCkit, an IoT sensor kit for non-chemical experts to easily detect and classify different types of VOCs. VOCkit combines miniature chemically-designed fluorometric sensors for recognizing VOCs with an embedded imaging system for classification. Exposing the fluorometric sensors with various VOCs, result in the photophysical property change of fluorescent compounds, which composes the sensors, and the synergistic combination of the changes create unique individual fluorescent color patterns respectively to the VOC material. The fluorescent color change pattern is captured using an embedded camera and the images are processed with machine learning algorithms on the embedded platform for VOC classification. Using 500 fluorometric sensor images collected for five different commonly contactable VOCs, we show the feasibility of VOC classification on small-sized IoT devices. For the VOC types of our interest, our results show a classification accuracy of 97%, implying the potential applicability of VOCkit for real-world usage.

Design of a point-of-care device for electrochemical detection of P.vivax infected-malaria using antibody functionalized rGO-gold nanocomposite

A point-of-care electrochemical “Tri-inlet” device integrated with an electrode that acts as a sensor is developed for early-stage diagnosis of Plasmodium vivax (Pv)-infected malaria in a human patient’s whole blood sample. The electrode is prepared in a step-wise manner involving a synthesis of rGO-based gold nanocomposite (Au-rGO) followed by conjugation with P.vivax specific antibody (merozoite surface protein-1, MSP1) and deposition of the antibody functionalized nanocomposite (Au-rGO-MSP1) over carbon strip. The crucial directional conjugation of the antibody to the nanocomposite is achieved through ethylenediamine (EDA) as a spacer for sensitive detection of the Pv-infected RBCs (Pv-iRBCs) in vivax malaria diagnosis. The Au-rGO-MSP1 based electrode (sensor) showed a decrease in peak-current density and an increase in charge-transfer resistance (Rct) as electrical signals in ferro/ferricyanide solution system due to the interaction between MSP1 antibody of the electrode and Pv-iRBCs. The Rct values increased proportionally with the number of iRBCs and varied linearly for cell concentration (R2 = 0.990). The performance analysis with the patient’s whole blood sample showed good sensitivity and selectivity with a detection limit of ∼40 vivax infected RBCs (Pv-iRBCs)/ 10 μL blood sample in 5 minutes which is comparable or even better than the most of the commercially available malaria kit. The present electrochemical approach demonstrated the proof-of-concept for designing a new disposable type sensor kit that could be easily used for early onsite detection of Plasmodium vivax-infected malaria.