A human interruption-free robotic platform for autonomous analysis of urinary samples

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Humayet Islam, M. Robiul Islam, G. Rabbi, M. Jalal Uddin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5747031/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Urine, a bodily by-product which conveys a number of physiological insights is a potential candidate for ongoing, regular health assessment. Despite their remarkable advancements, automated urine analyzers remain laboratory-based instruments that need labor-intensive sample processing and analysis, rendering them unsuitable for routine health screening. To expedite routine health screening, this work reports on a human interruption-free robotic platform that bypasses the manual operation for biological sample assay including handling of assay steps and colorimetric analysis of target markers in the sample. The robotic arm and the customized Android app; the key components of the platform automate the assay inside an imaging chamber eluding manual operation under similar assay protocols for multiple measurements and processes post-assayed image to quantify the target urine markers. Detailed characterization of the robotic operation, light distribution, and image analysis of the proposed platform was applied in the detection of glucose, protein and p H level in artificial urine sample that reveals a set of essential performance parameters that are comparable to, or even better than, that of conventional urine assay. As a result, it is believed that the proposed device may suitably be applied for autonomous analysis of other biological samples as a routine point-of-care (PoC) device. Urine marker Robotic arm Hue PoC device Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Although the automation can offer multiple advantages through improved efficiency, reproducibility, reliability, clinical translation, and safety [ 1 , 2 ]; experimental procedures of life science in the academic and clinical laboratories are extremely reliant on the manual operation of reagents and equipments by the research students and research staffs, respectively [ 3 – 5 ]. The usage of automation to these biological processes is restricted in the light of inaccessible and affordable technologies, insufficient biology skills of the system developer, narrow funding structures to accommodate high-tech robotic solutions, high levels of protocol variability and poor trust in automated frameworks. Other inhibitory factors include the absence of cheaper designs utilizing locally available raw materials, flexible and innovative modulars to be adaptive with respective experimental platforms [ 6 – 11 ]. Thereupon, to exploit the potential of automation in life science research at the academic and clinical laboratories there is an increasing need to design and develop the automation solutions that (i) would merge the engineering and biological skills together to answer novel research questions, and (ii) would not conflict with the constrained financial and spatial resources of the respective institutes. The ‘freely accessible’ bodily fluids such as blood, urine, saliva, sweat are the markers that can offer an individual’s in-depth physiological information [ 12 – 15 ]. Mass spectrometry is one of the popular methods for screening and profiling untargeted and targeted biomarkers in samples such as blood, urine, cerebrospinal fluid, sweat and saliva [ 16 – 19 ]. Some more commonly used techniques to profile these markers also include nuclear magnetic resonance, sample culture using automated urine sediment analyzers, absorption spectroscopy which vary in terms of sample preparation and sample numbers, operating principle, and results analysis [ 20 ]. To address those issues, a number of automated systems including artificial intelligence (AI) involving various subfields such as machine learning (ML), deep learning (DL), artificial neural networks (ANNs) and convolutional neural networks (CNNs) have been reported by several groups to supplant the automated sample analyzers [ 21 – 24 ]. These methods work on the basis of different algorithms and techniques to process post-analyzed images generating corresponding big clinical data to assist healthcare professionals during decision-making process and identifying the most suitable treatment. Also, another trend of smartphone-based automatic analysis techniques has also been reported in recent years [ 25 – 28 ] which are predominantly profound on the change of non-uniform red, green, blue (RGB) color scheme recorded by the phone camera to other uniform color schemes like hue, saturation, and intensity (HSI) to extract necessary color features [ 14 , 29 – 31 ]. Thus, these AI-based strategies and smartphone-based techniques target only on the different detection modalities processing clinical data for enhanced healthcare, but detailed automation in the entire experimental procedures in biological sample analysis is hardly reported to date. In this paper, we propose a robotic arm-assisted platform along with a wooden imaging chamber to automate bodily fluidics’ assay. The robotic arm supports sample handling for the necessary assay steps without the interruption of hands and facilitates maintaining similar assay protocols for multiple measurements avoiding possible contamination. The imaging box equipped with light source favors the imaging of the post-assayed sample under uniform optical environment. Finally, the platform has been successfully applied in the detection of urinary glucose and p H . To analyze post-assayed image, we also have developed ‘AutomateUrineAnalysis’ Android app. 2. Working Principles behind the Developed Platform The proposed platform has been conceptually sketched as in Scheme 1 . The automated platform includes (i) a 5-DOF (degree-of-freedom) robotic arm; (ii) an echo-friendly wooden imaging box equipped with LED strip; and (iii) a smartphone with ‘AutomatedUrineAnalysis’ app. The 5-DOF robotic arm is basically supposed to handle the assay steps. The smartphone is placed on the imaging box maintaining proper focal distance with the sample loading place. In our case, the 5-DOF robotic arm picks up a urine strip from the strip box and dips into urine sample-filled tube for a certain reaction time. Immediately after the dipping time, the robotic arm takes the strip out of the urine sample and removes the left-over solution from the strip wiping gently with tissue paper and put inside an imaging box via a sample slider. The detailed working mechanism of the robotic arm is explained with a flow-chart in Fig. 1 . Consequently, after a specific reaction time the smartphone snaps the colorimetric image of the urine reacted sensor strip and analyze it to quantify the level of the target marker in the urine sample using the ‘AutomatedUrineAnalysis’ app which has been explicated in Fig. 2 . The proposed platform enables the autonomous assay with a number of unique advantages including (i) the structure of the robotic arm can be modified based on the type of target sample and sensor; (ii) since the arm can handle assay steps without on-hand interruption, the assay time can be precisely maintained avoiding the possible contamination issues; and (iii) the imaging chamber was made of ecofriendly wood and it ensures uniform optical distribution on the urine sensor which is major concern for smartphone-based colorimetric analysis [ 32 ]. 2.1. Working of the Robotic Arm Recently, robots are widely used in many sectors to enhance accuracy, productivity, and to shorten work-weeks for labor by automation [ 40 , 41 ]. Since accuracy, repeatability and productivity are the biggest concerns in medical technology; application of robots in various medical operations, especially in diagnostics is an emerging issue for automatizing the diagnosis tools. Many tasks in clinical works are simple but important, such as collecting blood samples, monitoring body temperature, or improving contamination free sample management [ 42 ]. Moreover, the skilled hands are significantly necessary for reliable diagnosis outcomes. Therefore, if robotic approaches can be integrated in clinical diagnosis procedures, it will provide trustworthy diagnosis results without requiring trained personnels. Three separate elements comprise the fully automated 5-DOF robotic arm: (i) a mechanical unit, (ii) a control unit, and (iii) a memory unit. The mechanical unit includes five servo (MG996R) motors, two toggle switches, and a 20-by-4 LCD display. The control and memory units are made of five potentiometers, and a microcontroller (ATmega2560), respectively. Without involving any handheld operation, the 5-DOF robotic arm retrieves a strip from the strip box and dips it into a test tube filled with urine. The strip is then placed inside an image box after being wiped with a tissue. The detailed flow chart for the 5-DOF robotic arm is illustrated in Fig. 1 . In the working of the robotic arm, the Power button is pressed to start the system, and the system is actively marked with idle mode. Next, the recording switch is pressed to initialize the system, and the microcontroller receives commands from the potentiometers. Accurate angle movements of the robotic arm can be achieved by positioning it by the microcontroller initiated analog signals through the potentiometer where the potentiometers are used to calibrate the robotic arms initially. The corresponding post-calibrated signal are saved in the microcontroller. For multiple measurement, the robotic arm works repeatedly when the playing switch is turn on. An LCD screen (20 x 4) displays the respective mechanical activities of the robotic arm to execute the measurement. 2.2. AutomatedUrineAnalysis Android App To determine the hue value of the post-assayed colorimetric reagent pads of the urine strip inside the imaging box the ‘AutomatedUrineAnalysis’ App enables the camera to snap the pad’s image, determines the raw rgb values of the images, and extracts the corresponding colorimetric information such as hue value using those rgb values. The extraction procedure of the hue value of images has been shown in Fig. 2 . The Android app first focuses on the images automatically and selects the region of interest (RoI). Then the RGB value of the RoI and the corresponding hue values are calculated based on the following algorithms reported earlier [ 38 , 39 ]. $$\:R=\frac{r}{r+g+b}\times\:100,\:G=\frac{g}{r+g+b}\times\:100,\:B=\frac{b}{r+g+b}\times\:100$$ 1 ……………..…. \(\:H={\text{cos}}^{-1}\left[\frac{0.5\{\left(R-G\right)+(R-B)}{\sqrt{\{{\left(R-G\right)}^{2}+(R-B\left)\right(G-B)}}\right]\:\) for \(\:\pi\:\ge\:0\:(G>B)\) ………….…….…..(2) ‘AutomatedUrineAnalysis’ app can auto detect different shapes (square, circle, and pixel) of RoI on the reagent strips. 3. Materials and Methods 3.1. Fabrication of Experimental Platform The experimental platform includes two sections; (i) wooden box with, (ii) the supportive stand for holding the robotic arm, and (iii) electrical controller. The wooden platform is composed of a sliding tray with the dimension of 13.5cm x 6cm x 0.6cm to hold the sensor strip, and an imaging box (16.5cm x 8.5cm x 7.5cm) equipped with LED array beneath the top layer, as shown in Fig. 3 (a) . With its elegant carvings, the LED-fitted imaging box comfortably accommodate phone onto its upper part for image and image analyzing, as in Fig. 3 (b) . The height between the smartphone camera and the strip sensor are tunable to ensure the appropriate focal length. The LED array along with the built-in camera flash diffuse the light homogeneously onto the strip sensor. The wooden wall of the box minimizes further light reflection to avoid image burning issues. The slider also includes two round holes of 12.57 cm 2 , and 2.84 cm 2 , respectively to accommodate the strip box and sample tube, accordingly. Furthermore, the potentiometers and display unit are fixed with the slider using a glue gone (GG-5, 100W), as shown in the Fig. 3 (c). Five servo-motor were used in the robotic arm for 5-DOF (Degree of Freedom) using very thin and smooth wood. The tray with the strip can automatically inserted inside the imaging box with the help of robotic arm. For placing the strip box in front of the robotic arm a 12.57 cm 2 round hole has been made on the wooden housing. Another 2.84 cm 2 sized round hole has been created to keep the test tube which is placed beside the strip box. Both the strip box and the test tube adjust nicely with the prototype housing. 3.2. Sample Preparation The urine strips sensors (Uric 3V, China) were procured through a local supplier, providing the foundational tool for the subsequent analyses. The albumin solution was sourced from Medistorebd, Bangladesh, a p H buffer solution spanning the range of 4–9 was purchased from China, and dextrose powder was obtained from GlaxoseD in Bangladesh. To create the artificial urine samples, the protein concentrations spanning from 0 to 600 mg/dL, and the glucose concentrations ranging from 0 to 300 mg/dL were prepared. Distilled (DI) water was employed throughout the preparation process of different concentrations of glucose and protein to ensure the purity of the urinary solutions. 4. Results and Discussion 4.1. Time Dependent Comparison between the Regular and Proposed Methods The regular urinalysis method using urine strip sensor follows inserting the strip in urinary sample for some seconds and then wiping of left-over solution from the reagent pads of the strip. Subsequently, the color generated in the sample reacted pad is compared with the reference color chart to quantify the target analyte. Since this visual dipstick inspection procedure is manually executed, it may suffer from the individuals’ subjective interpretation for multiple assaying [ 40 ] and the dipping time may also vary hands to hands. To study this issue, the hue value of the post-reacted reagent pads has been measured varying the dipping time of urine sensor into the glucose sample up to 20 seconds using both the regular and proposed method to conduct a comparative study. Figure 4 shows the comparative standard deviation of the hue value for 100 mg/dL glucose while measured for different dipping time of urine sensor into the urine sample up to 20 seconds. As seen in the figure, the regular dipstick method suffers from high standard deviation while measuring the hue value of an individual level of glucose varying the sample reaction time, whereas our proposed platform measures the same level of glucose under similar measurement protocol with lesser standard deviation. This study proofs that the proposed method can be reliably applied for colorimetric analysis of biological samples since it is dipping time independent. 4.2. Algorithm Precision Evaluation In medical image analysis, selecting regions of interest (RoI) is a standard procedure that applies to all imaging modalities. By selecting a certain RoI, systems can maximize resources shortening processing times, and improving accuracy, which allows for the omission of unnecessary data. This is particularly important when dealing with large or high-resolution images where processing the entire image might be unnecessary or impractical. For example, reducing the RoI can significantly increase the analysis's efficacy in image recognition. In this work, an ‘AutomatedUrineAnalysis’ app has been developed for image processing and different RoIs have been demonstrated to assess the accuracy of the ‘AutomatedUrineAnalysis’ app to quantify the hue value the assayed image. Figure 5 (a) shows the screen view of different RoIs (square, circle, and pixel shapes) with their corresponding hue values for 100 mg/dL glucose measured based on the different RoIs by the ‘AutomatedUrineAnalysis’ app shown in F igure (b-d) . As seen in the figure, the measured hue values for the RoIs are more or less similar with almost equal coefficient of determination (R 2 ), which confirms the algorithm precision used in the developed image processing app. 4.3. Measurement of Various Issues in Built-in Flash and External Light Sources When a smartphone captures a photo in low-light conditions or when the ambient light is insufficient, it activates the built-in flash to illuminate the target image. Sometimes image burning happens when the image appears overexposed or washed out if the built-in flash is too bright or too close to the subject [ 41 – 43 ]. To avoid such issue, studies both on using built-in flash light and external light source keeping the built-in flash light off by ‘AutomatedUrineAnalysis’ app have been carried out. When the ‘AutomatedUrineAnalysis’ app imaged using in smartphones build-in flash light burning of image happened, as shown in Fig. 6 (a) . But the usage of external lighting source addressed that image burning issue minimizing the overexposure, as shown in Fig. 6 (b) . Figure 6 (c) show the comparative hue values for varying concentrations of glucose measured using the built-in smartphone flash and external light source. As seen in the Fig. 6 (c) , the hue value measured using external light source shows linearly decreasing trend against increasing concentration of glucose with reasonable coefficient of determination (R 2 = 0.9638). Whereas the hue value quantified using built-in smartphone flash shows very poor coefficient of determination (R 2 = 0.0011), which means that the over exposed images by the built-in smartphone flash results in similar hue values for all the different concentrations of glucose. 4.4. Measurement of Various Urine Markers Based on the proof-of-concept for minimizing the time-dependent inaccuracy in the manual dipstick method and the benefits of the robotic platform, hue values for glucose (0-350 mg/dL), protein (0-500 mg/dL), and p H (5.0-7.5) of the artificial urinary solution have been measured. As shown in Fig. 7 (a-c) , the hue value for glucose, protein, and p H increases linearly. The data and the error bars in the figures represent the relevant mean and relative standard deviation. The measurement covers a wide range of values that are within the clinical detection range for glucose, protein, and p H indicating diseased and healthy body [ 44 , 45 ]. Consequently, throughout a larger dynamic range, the suggested platform allows for more quantitative and sensitive monitoring of p H , protein, and glucose. 4.5. Device Compatibility Since different devices have varying hardware capabilities and OS versions, device compatibility is crucial for the usability of smartphone-based research to ensure a consistent user experience across different smartphones [ 46 , 47 ]. This is particularly important in diverse markets where different devices are prevalent. Resolving these discrepancies is necessary to ensure compatibility. In this study, post-assayed images were taken using three separate smartphones, and the ‘AutomatedUrineAnalysis’ android app was used to analyze the images. These were low-cost smartphones: the Sony Xperia L2, Xiaomi Redmi Note 7, and Realme C3. As seen in Figs. 8 (a-c) and 8(d) , respectively, the hue value and accompanying co-efficient of variation (CV) for the same concentration range of glucose (0-350 mg/dL) marker detected using the same android app on these different smartphones are almost comparable. Therefore, the suggested human interruption-free platform concept is encouraging since it may be deployed effectively and affordably any smartphone for communities with limited resources. 5. Conclusions This research presents a unique low-cost and human interruption-free robotic platform for urine biomarker detection and analysis in association with a custom-made smartphone-based imaging module. The platform successfully improved the assay outcome reducing the time-dependent measurement inaccuracy experienced in conventional dipstick method. The utilization of the wooden imaging module and smartphone app as an optical reader confirmed the uniform distribution of imaging light on the sample avoiding the image burning and enhanced the assay signals. In the end, the proposed platform was successfully applied for quantifying a number of urinary markers in artificial urine samples. As a result, the developed platform could significantly influence PoC clinical applications in environments with limited resources. Declarations Author Contribution M. Humayet Islam wrote the main manuscript, conception, design of the work, data analysis and interpretation.M. Robiul Islam and G. Rabbi collected the data.M. Jalal Uddin supervised the whole research. References Solmi M, Seitidis G, Mavridis D, Correll CU, Dragioti E, Guimond S, et al. Incidence, prevalence, and global burden of schizophrenia - data, with critical appraisal, from the Global Burden of Disease (GBD) 2019. Mol Psychiatry. 2023 Dec;28(12):5319–27. 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How the brain tissue shapes the electric field induced by transcranial magnetic stimulation. NeuroImage. 2011 Oct 1;58(3):849–59. Cole EJ, Stimpson KH, Bentzley BS, Gulser M, Cherian K, Tischler C, et al. Stanford Accelerated Intelligent Neuromodulation Therapy for Treatment-Resistant Depression. Am J Psychiatry. 2020 Aug;177(8):716–26. Caulfield KA, Brown JC. The Problem and Potential of TMS’ Infinite Parameter Space: A Targeted Review and Road Map Forward. Front Psychiatry [Internet]. 2022 May 10 [cited 2024 Nov 6];13. Available from: https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2022.867091/full Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5747031","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":398431428,"identity":"9bd21ac5-5bf7-47c7-948d-0f36956169f4","order_by":0,"name":"M. Humayet Islam","email":"","orcid":"","institution":"Islamic University","correspondingAuthor":false,"prefix":"","firstName":"M.","middleName":"Humayet","lastName":"Islam","suffix":""},{"id":398431429,"identity":"14cdd475-8be0-400f-bdb8-b3f66150e86a","order_by":1,"name":"M. Robiul Islam","email":"","orcid":"","institution":"KwangWoon University","correspondingAuthor":false,"prefix":"","firstName":"M.","middleName":"Robiul","lastName":"Islam","suffix":""},{"id":398431430,"identity":"9185b7e0-be82-4e1e-acd7-79f0060ea246","order_by":2,"name":"G. Rabbi","email":"","orcid":"","institution":"International University of Business Agriculture and Technology (IUBAT)","correspondingAuthor":false,"prefix":"","firstName":"G.","middleName":"","lastName":"Rabbi","suffix":""},{"id":398431431,"identity":"17baee2d-d5ba-4252-b72b-c0958173aea6","order_by":3,"name":"M. Jalal Uddin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYFCCAxCKH0QkFJCiRbIBpMWAFMsMwFqJ0aLbeMaA4UeFnZzx+dWJHx4YMMjzix3Ar8XswBkDxp4zycZmN95ulgA6zHDm7ATCWhh42w4kbrtxdgNIS4LBbSK0MP79d6B+84yzm38QrYWZt+FAggF/7zZibTlWcFjmWLLhjBu82ywSDCSI8MuNwxsfvqmxk+fvP7v55o8KG3l+aQJaGCQOQCNTAqxSgoByEOBvgDEOEKF6FIyCUTAKRiQAAASDSWrw8xDcAAAAAElFTkSuQmCC","orcid":"","institution":"Islamic University","correspondingAuthor":true,"prefix":"","firstName":"M.","middleName":"Jalal","lastName":"Uddin","suffix":""}],"badges":[],"createdAt":"2025-01-01 16:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5747031/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5747031/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73315573,"identity":"4a30daa7-5de4-4f5e-a205-ad4a199d83df","added_by":"auto","created_at":"2025-01-08 19:55:54","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":16790,"visible":true,"origin":"","legend":"\u003cp\u003eFlow-chart to demonstrate the step-wise working of the robotic arm.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5747031/v1/0586a48b1d314d88abcf85de.jpg"},{"id":73315574,"identity":"426ecb2f-fdeb-49f6-ab4d-0e7567dfd46e","added_by":"auto","created_at":"2025-01-08 19:55:54","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":24617,"visible":true,"origin":"","legend":"\u003cp\u003eFlow-chart explaining the imaging and analysis of post-assayed colorimetric images of the strips.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5747031/v1/5dc7ad621e980ea5e4ba7608.jpg"},{"id":73315575,"identity":"ef9ce09d-2b14-4ba0-8380-c525ea4fe7f3","added_by":"auto","created_at":"2025-01-08 19:55:54","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":72297,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a). \u003c/strong\u003eInternal view, \u003cstrong\u003e(b). \u003c/strong\u003etop view of the eco-friendly optical platform placing smartphone on the top, and \u003cstrong\u003e(c).\u003c/strong\u003e details of the developed experimental platform.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5747031/v1/1d8fd849c87fa3211bf9a483.jpg"},{"id":73316158,"identity":"d91810e6-6eb7-4bb5-9b8f-25718ea67057","added_by":"auto","created_at":"2025-01-08 20:03:54","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":15320,"visible":true,"origin":"","legend":"\u003cp\u003eComparative standard deviation between the regular dipstick method and proposed autonomous method for the hue value of 100 mg/dL glucose. The measurement was carried out for different dipping time up to 20 seconds of urine sensor into the glucose sample.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5747031/v1/f991611c18e12f506ba1dc3a.jpg"},{"id":73315577,"identity":"1a9812bc-fc0e-4c70-840d-cf92350065dc","added_by":"auto","created_at":"2025-01-08 19:55:54","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":81534,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a).\u003c/strong\u003e Screen view of different RoIs (square, circle, and pixel shapes), and \u003cstrong\u003e(b-d)\u003c/strong\u003e. hue values for 100 mg/dL glucose measured based on the different RoIs by the \"AutomatedUrineAnalysis\" app.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5747031/v1/b31dae92818892a90d5b411f.jpg"},{"id":73315594,"identity":"8c9e5852-b4f1-4640-bfd9-04dfcd64eefa","added_by":"auto","created_at":"2025-01-08 19:55:55","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":32301,"visible":true,"origin":"","legend":"\u003cp\u003eImages of RoI on the post-assayed strip sensor with \u003cstrong\u003e(a).\u003c/strong\u003e built-in smartphone flash, and \u003cstrong\u003e(b).\u003c/strong\u003e external light source. \u003cstrong\u003e(c).\u003c/strong\u003e Comparative hue values for different concentrations of glucose measured using external light source and built-in smartphone flash.\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5747031/v1/bc3e3ed9f01451a266ef27aa.jpg"},{"id":73317274,"identity":"8d473811-a86c-4ef6-9336-47ae4fe7dc0f","added_by":"auto","created_at":"2025-01-08 20:11:54","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":46398,"visible":true,"origin":"","legend":"\u003cp\u003eMeasurement of the hue value for different concentrations\u003cstrong\u003e \u003c/strong\u003eof \u003cstrong\u003e(a)\u003c/strong\u003e. glucose, \u003cstrong\u003e(b).\u003c/strong\u003e protein, and \u003cstrong\u003e(c).\u003c/strong\u003e p\u003csup\u003eH\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5747031/v1/dfe5c2ac36aa3ed82f468108.jpg"},{"id":73315590,"identity":"92a936fe-c8d8-460d-8056-023be926b316","added_by":"auto","created_at":"2025-01-08 19:55:55","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":66830,"visible":true,"origin":"","legend":"\u003cp\u003eMeasurement of hue value of varying concentrations of glucose using three different smartphones.\u003c/p\u003e","description":"","filename":"Picture8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5747031/v1/f667d1289feac92cf7df77f7.jpg"},{"id":73382606,"identity":"12e6efa2-b3ed-41d9-b1de-54eb4b923e45","added_by":"auto","created_at":"2025-01-09 11:32:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1000040,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5747031/v1/9d52cbfe-afbc-4cae-9518-098595ad6dc3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A human interruption-free robotic platform for autonomous analysis of urinary samples","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAlthough the automation can offer multiple advantages through improved efficiency, reproducibility, reliability, clinical translation, and safety [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]; experimental procedures of life science in the academic and clinical laboratories are extremely reliant on the manual operation of reagents and equipments by the research students and research staffs, respectively [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The usage of automation to these biological processes is restricted in the light of inaccessible and affordable technologies, insufficient biology skills of the system developer, narrow funding structures to accommodate high-tech robotic solutions, high levels of protocol variability and poor trust in automated frameworks. Other inhibitory factors include the absence of cheaper designs utilizing locally available raw materials, flexible and innovative modulars to be adaptive with respective experimental platforms [\u003cspan additionalcitationids=\"CR7 CR8 CR9 CR10\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Thereupon, to exploit the potential of automation in life science research at the academic and clinical laboratories there is an increasing need to design and develop the automation solutions that (i) would merge the engineering and biological skills together to answer novel research questions, and (ii) would not conflict with the constrained financial and spatial resources of the respective institutes.\u003c/p\u003e \u003cp\u003eThe \u0026lsquo;freely accessible\u0026rsquo; bodily fluids such as blood, urine, saliva, sweat are the markers that can offer an individual\u0026rsquo;s in-depth physiological information [\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Mass spectrometry is one of the popular methods for screening and profiling untargeted and targeted biomarkers in samples such as blood, urine, cerebrospinal fluid, sweat and saliva [\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Some more commonly used techniques to profile these markers also include nuclear magnetic resonance, sample culture using automated urine sediment analyzers, absorption spectroscopy which vary in terms of sample preparation and sample numbers, operating principle, and results analysis [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. To address those issues, a number of automated systems including artificial intelligence (AI) involving various subfields such as machine learning (ML), deep learning (DL), artificial neural networks (ANNs) and convolutional neural networks (CNNs) have been reported by several groups to supplant the automated sample analyzers [\u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. These methods work on the basis of different algorithms and techniques to process post-analyzed images generating corresponding big clinical data to assist healthcare professionals during decision-making process and identifying the most suitable treatment. Also, another trend of smartphone-based automatic analysis techniques has also been reported in recent years [\u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] which are predominantly profound on the change of non-uniform red, green, blue (RGB) color scheme recorded by the phone camera to other uniform color schemes like hue, saturation, and intensity (HSI) to extract necessary color features [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Thus, these AI-based strategies and smartphone-based techniques target only on the different detection modalities processing clinical data for enhanced healthcare, but detailed automation in the entire experimental procedures in biological sample analysis is hardly reported to date.\u003c/p\u003e \u003cp\u003eIn this paper, we propose a robotic arm-assisted platform along with a wooden imaging chamber to automate bodily fluidics\u0026rsquo; assay. The robotic arm supports sample handling for the necessary assay steps without the interruption of hands and facilitates maintaining similar assay protocols for multiple measurements avoiding possible contamination. The imaging box equipped with light source favors the imaging of the post-assayed sample under uniform optical environment. Finally, the platform has been successfully applied in the detection of urinary glucose and p\u003csup\u003eH\u003c/sup\u003e. To analyze post-assayed image, we also have developed \u0026lsquo;AutomateUrineAnalysis\u0026rsquo; Android app.\u003c/p\u003e"},{"header":"2. Working Principles behind the Developed Platform","content":"\u003cp\u003eThe proposed platform has been conceptually sketched as \u003cb\u003ein\u003c/b\u003e Scheme \u003cspan refid=\"Sch1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The automated platform includes (i) a 5-DOF (degree-of-freedom) robotic arm; (ii) an echo-friendly wooden imaging box equipped with LED strip; and (iii) a smartphone with \u0026lsquo;AutomatedUrineAnalysis\u0026rsquo; app. The 5-DOF robotic arm is basically supposed to handle the assay steps. The smartphone is placed on the imaging box maintaining proper focal distance with the sample loading place. In our case, the 5-DOF robotic arm picks up a urine strip from the strip box and dips into urine sample-filled tube for a certain reaction time. Immediately after the dipping time, the robotic arm takes the strip out of the urine sample and removes the left-over solution from the strip wiping gently with tissue paper and put inside an imaging box via a sample slider. The detailed working mechanism of the robotic arm is explained with a flow-chart in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Consequently, after a specific reaction time the smartphone snaps the colorimetric image of the urine reacted sensor strip and analyze it to quantify the level of the target marker in the urine sample using the \u0026lsquo;AutomatedUrineAnalysis\u0026rsquo; app which has been explicated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The proposed platform enables the autonomous assay with a number of unique advantages including (i) the structure of the robotic arm can be modified based on the type of target sample and sensor; (ii) since the arm can handle assay steps without on-hand interruption, the assay time can be precisely maintained avoiding the possible contamination issues; and (iii) the imaging chamber was made of ecofriendly wood and it ensures uniform optical distribution on the urine sensor which is major concern for smartphone-based colorimetric analysis [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Working of the Robotic Arm\u003c/h2\u003e \u003cp\u003eRecently, robots are widely used in many sectors to enhance accuracy, productivity, and to shorten work-weeks for labor by automation [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Since accuracy, repeatability and productivity are the biggest concerns in medical technology; application of robots in various medical operations, especially in diagnostics is an emerging issue for automatizing the diagnosis tools. Many tasks in clinical works are simple but important, such as collecting blood samples, monitoring body temperature, or improving contamination free sample management [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Moreover, the skilled hands are significantly necessary for reliable diagnosis outcomes. Therefore, if robotic approaches can be integrated in clinical diagnosis procedures, it will provide trustworthy diagnosis results without requiring trained personnels.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThree separate elements comprise the fully automated 5-DOF robotic arm: (i) a mechanical unit, (ii) a control unit, and (iii) a memory unit. The mechanical unit includes five servo (MG996R) motors, two toggle switches, and a 20-by-4 LCD display. The control and memory units are made of five potentiometers, and a microcontroller (ATmega2560), respectively. Without involving any handheld operation, the 5-DOF robotic arm retrieves a strip from the strip box and dips it into a test tube filled with urine. The strip is then placed inside an image box after being wiped with a tissue. The detailed flow chart for the 5-DOF robotic arm is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eIn the working of the robotic arm, the Power button is pressed to start the system, and the system is actively marked with idle mode. Next, the recording switch is pressed to initialize the system, and the microcontroller receives commands from the potentiometers. Accurate angle movements of the robotic arm can be achieved by positioning it by the microcontroller initiated analog signals through the potentiometer where the potentiometers are used to calibrate the robotic arms initially. The corresponding post-calibrated signal are saved in the microcontroller. For multiple measurement, the robotic arm works repeatedly when the playing switch is turn on. An LCD screen (20 x 4) displays the respective mechanical activities of the robotic arm to execute the measurement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. AutomatedUrineAnalysis Android App\u003c/h2\u003e \u003cp\u003eTo determine the hue value of the post-assayed colorimetric reagent pads of the urine strip inside the imaging box the \u0026lsquo;AutomatedUrineAnalysis\u0026rsquo; App enables the camera to snap the pad\u0026rsquo;s image, determines the raw rgb values of the images, and extracts the corresponding colorimetric information such as hue value using those rgb values. The extraction procedure of the hue value of images has been shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe Android app first focuses on the images automatically and selects the region of interest (RoI). Then the RGB value of the RoI and the corresponding hue values are calculated based on the following algorithms reported earlier [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:R=\\frac{r}{r+g+b}\\times\\:100,\\:G=\\frac{g}{r+g+b}\\times\\:100,\\:B=\\frac{b}{r+g+b}\\times\\:100$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;..\u0026hellip;.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:H={\\text{cos}}^{-1}\\left[\\frac{0.5\\{\\left(R-G\\right)+(R-B)}{\\sqrt{\\{{\\left(R-G\\right)}^{2}+(R-B\\left)\\right(G-B)}}\\right]\\:\\)\u003c/span\u003e \u003c/span\u003efor \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pi\\:\\ge\\:0\\:(G\u0026gt;B)\\)\u003c/span\u003e\u003c/span\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.\u0026hellip;\u0026hellip;.\u0026hellip;..(2)\u003c/p\u003e \u003cp\u003e\u0026lsquo;AutomatedUrineAnalysis\u0026rsquo; app can auto detect different shapes (square, circle, and pixel) of RoI on the reagent strips.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Materials and Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Fabrication of Experimental Platform\u003c/h2\u003e \u003cp\u003eThe experimental platform includes two sections; (i) wooden box with, (ii) the supportive stand for holding the robotic arm, and (iii) electrical controller. The wooden platform is composed of a sliding tray with the dimension of 13.5cm x 6cm x 0.6cm to hold the sensor strip, and an imaging box (16.5cm x 8.5cm x 7.5cm) equipped with LED array beneath the top layer, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e(a)\u003c/b\u003e. With its elegant carvings, the LED-fitted imaging box comfortably accommodate phone onto its upper part for image and image analyzing, as in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e(b)\u003c/b\u003e. The height between the smartphone camera and the strip sensor are tunable to ensure the appropriate focal length. The LED array along with the built-in camera flash diffuse the light homogeneously onto the strip sensor. The wooden wall of the box minimizes further light reflection to avoid image burning issues. The slider also includes two round holes of 12.57 cm\u003csup\u003e2\u003c/sup\u003e, and 2.84 cm\u003csup\u003e2\u003c/sup\u003e, respectively to accommodate the strip box and sample tube, accordingly. Furthermore, the potentiometers and display unit are fixed with the slider using a glue gone (GG-5, 100W), as shown in the Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e(c).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFive servo-motor were used in the robotic arm for 5-DOF (Degree of Freedom) using very thin and smooth wood. The tray with the strip can automatically inserted inside the imaging box with the help of robotic arm. For placing the strip box in front of the robotic arm a 12.57 cm\u003csup\u003e2\u003c/sup\u003e round hole has been made on the wooden housing. Another 2.84 cm\u003csup\u003e2\u003c/sup\u003e sized round hole has been created to keep the test tube which is placed beside the strip box. Both the strip box and the test tube adjust nicely with the prototype housing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Sample Preparation\u003c/h2\u003e \u003cp\u003eThe urine strips sensors (Uric 3V, China) were procured through a local supplier, providing the foundational tool for the subsequent analyses. The albumin solution was sourced from Medistorebd, Bangladesh, a p\u003csup\u003eH\u003c/sup\u003e buffer solution spanning the range of 4\u0026ndash;9 was purchased from China, and dextrose powder was obtained from GlaxoseD in Bangladesh.\u003c/p\u003e \u003cp\u003eTo create the artificial urine samples, the protein concentrations spanning from 0 to 600 mg/dL, and the glucose concentrations ranging from 0 to 300 mg/dL were prepared. Distilled (DI) water was employed throughout the preparation process of different concentrations of glucose and protein to ensure the purity of the urinary solutions.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results and Discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Time Dependent Comparison between the Regular and Proposed Methods\u003c/h2\u003e \u003cp\u003eThe regular urinalysis method using urine strip sensor follows inserting the strip in urinary sample for some seconds and then wiping of left-over solution from the reagent pads of the strip. Subsequently, the color generated in the sample reacted pad is compared with the reference color chart to quantify the target analyte. Since this visual dipstick inspection procedure is manually executed, it may suffer from the individuals\u0026rsquo; subjective interpretation for multiple assaying [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] and the dipping time may also vary hands to hands. To study this issue, the hue value of the post-reacted reagent pads has been measured varying the dipping time of urine sensor into the glucose sample up to 20 seconds using both the regular and proposed method to conduct a comparative study. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the comparative standard deviation of the hue value for 100 mg/dL glucose while measured for different dipping time of urine sensor into the urine sample up to 20 seconds. As seen in the figure, the regular dipstick method suffers from high standard deviation while measuring the hue value of an individual level of glucose varying the sample reaction time, whereas our proposed platform measures the same level of glucose under similar measurement protocol with lesser standard deviation. This study proofs that the proposed method can be reliably applied for colorimetric analysis of biological samples since it is dipping time independent.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Algorithm Precision Evaluation\u003c/h2\u003e \u003cp\u003eIn medical image analysis, selecting regions of interest (RoI) is a standard procedure that applies to all imaging modalities. By selecting a certain RoI, systems can maximize resources shortening processing times, and improving accuracy, which allows for the omission of unnecessary data. This is particularly important when dealing with large or high-resolution images where processing the entire image might be unnecessary or impractical. For example, reducing the RoI can significantly increase the analysis's efficacy in image recognition. In this work, an \u0026lsquo;AutomatedUrineAnalysis\u0026rsquo; app has been developed for image processing and different RoIs have been demonstrated to assess the accuracy of the \u0026lsquo;AutomatedUrineAnalysis\u0026rsquo; app to quantify the hue value the assayed image. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e(a)\u003c/b\u003e shows the screen view of different RoIs (square, circle, and pixel shapes) with their corresponding hue values for 100 mg/dL glucose measured based on the different RoIs by the \u0026lsquo;AutomatedUrineAnalysis\u0026rsquo; app shown in F\u003cb\u003eigure (b-d)\u003c/b\u003e. As seen in the figure, the measured hue values for the RoIs are more or less similar with almost equal coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e), which confirms the algorithm precision used in the developed image processing app.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Measurement of Various Issues in Built-in Flash and External Light Sources\u003c/h2\u003e \u003cp\u003eWhen a smartphone captures a photo in low-light conditions or when the ambient light is insufficient, it activates the built-in flash to illuminate the target image. Sometimes image burning happens when the image appears overexposed or washed out if the built-in flash is too bright or too close to the subject [\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. To avoid such issue, studies both on using built-in flash light and external light source keeping the built-in flash light off by \u0026lsquo;AutomatedUrineAnalysis\u0026rsquo; app have been carried out. When the \u0026lsquo;AutomatedUrineAnalysis\u0026rsquo; app imaged using in smartphones build-in flash light burning of image happened, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003cb\u003e(a)\u003c/b\u003e. But the usage of external lighting source addressed that image burning issue minimizing the overexposure, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003cb\u003e(b)\u003c/b\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003cb\u003e(c)\u003c/b\u003e show the comparative hue values for varying concentrations of glucose measured using the built-in smartphone flash and external light source. As seen in the Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003cb\u003e(c)\u003c/b\u003e, the hue value measured using external light source shows linearly decreasing trend against increasing concentration of glucose with reasonable coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.9638). Whereas the hue value quantified using built-in smartphone flash shows very poor coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.0011), which means that the over exposed images by the built-in smartphone flash results in similar hue values for all the different concentrations of glucose.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Measurement of Various Urine Markers\u003c/h2\u003e \u003cp\u003eBased on the proof-of-concept for minimizing the time-dependent inaccuracy in the manual dipstick method and the benefits of the robotic platform, hue values for glucose (0-350 mg/dL), protein (0-500 mg/dL), and p\u003csup\u003eH\u003c/sup\u003e (5.0-7.5) of the artificial urinary solution have been measured. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e\u003cb\u003e(a-c)\u003c/b\u003e, the hue value for glucose, protein, and p\u003csup\u003eH\u003c/sup\u003e increases linearly. The data and the error bars in the figures represent the relevant mean and relative standard deviation. The measurement covers a wide range of values that are within the clinical detection range for glucose, protein, and p\u003csup\u003eH\u003c/sup\u003e indicating diseased and healthy body [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Consequently, throughout a larger dynamic range, the suggested platform allows for more quantitative and sensitive monitoring of p\u003csup\u003eH\u003c/sup\u003e, protein, and glucose.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.5. Device Compatibility\u003c/h2\u003e \u003cp\u003eSince different devices have varying hardware capabilities and OS versions, device compatibility is crucial for the usability of smartphone-based research to ensure a consistent user experience across different smartphones [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. This is particularly important in diverse markets where different devices are prevalent. Resolving these discrepancies is necessary to ensure compatibility. In this study, post-assayed images were taken using three separate smartphones, and the \u0026lsquo;AutomatedUrineAnalysis\u0026rsquo; android app was used to analyze the images. These were low-cost smartphones: the Sony Xperia L2, Xiaomi Redmi Note 7, and Realme C3. As seen in Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e\u003cb\u003e(a-c)\u003c/b\u003e and \u003cb\u003e8(d)\u003c/b\u003e, respectively, the hue value and accompanying co-efficient of variation (CV) for the same concentration range of glucose (0-350 mg/dL) marker detected using the same android app on these different smartphones are almost comparable. Therefore, the suggested human interruption-free platform concept is encouraging since it may be deployed effectively and affordably any smartphone for communities with limited resources.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis research presents a unique low-cost and human interruption-free robotic platform for urine biomarker detection and analysis in association with a custom-made smartphone-based imaging module. The platform successfully improved the assay outcome reducing the time-dependent measurement inaccuracy experienced in conventional dipstick method. The utilization of the wooden imaging module and smartphone app as an optical reader confirmed the uniform distribution of imaging light on the sample avoiding the image burning and enhanced the assay signals. In the end, the proposed platform was successfully applied for quantifying a number of urinary markers in artificial urine samples. As a result, the developed platform could significantly influence PoC clinical applications in environments with limited resources.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM. Humayet Islam wrote the main manuscript, conception, design of the work, data analysis and interpretation.M. Robiul Islam and G. Rabbi collected the data.M. Jalal Uddin supervised the whole research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSolmi M, Seitidis G, Mavridis D, Correll CU, Dragioti E, Guimond S, et al. Incidence, prevalence, and global burden of schizophrenia - data, with critical appraisal, from the Global Burden of Disease (GBD) 2019. Mol Psychiatry. 2023 Dec;28(12):5319\u0026ndash;27.\u003c/li\u003e\n\u003cli\u003eKadakia A, Catillon M, Fan Q, Williams GR, Marden JR, Anderson A, et al. The Economic Burden of Schizophrenia in the United States. J Clin Psychiatry. 2022 Oct 10;83(6):43278.\u003c/li\u003e\n\u003cli\u003eHjorth\u0026oslash;j C, St\u0026uuml;rup AE, McGrath JJ, Nordentoft M. Years of potential life lost and life expectancy in schizophrenia: a systematic review and meta-analysis. Lancet Psychiatry. 2017 Apr 1;4(4):295\u0026ndash;301.\u003c/li\u003e\n\u003cli\u003eKalisova L, Michalec J, Dechterenko F, Silhan P, Hyza M, Chlebovcova M, et al. 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Available from: https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2022.867091/full\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Urine marker, Robotic arm, Hue, PoC device","lastPublishedDoi":"10.21203/rs.3.rs-5747031/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5747031/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUrine, a bodily by-product which conveys a number of physiological insights is a potential candidate for ongoing, regular health assessment. Despite their remarkable advancements, automated urine analyzers remain laboratory-based instruments that need labor-intensive sample processing and analysis, rendering them unsuitable for routine health screening. To expedite routine health screening, this work reports on a human interruption-free robotic platform that bypasses the manual operation for biological sample assay including handling of assay steps and colorimetric analysis of target markers in the sample. The robotic arm and the customized Android app; the key components of the platform automate the assay inside an imaging chamber eluding manual operation under similar assay protocols for multiple measurements and processes post-assayed image to quantify the target urine markers. Detailed characterization of the robotic operation, light distribution, and image analysis of the proposed platform was applied in the detection of glucose, protein and p\u003csup\u003eH\u003c/sup\u003e level in artificial urine sample that reveals a set of essential performance parameters that are comparable to, or even better than, that of conventional urine assay. As a result, it is believed that the proposed device may suitably be applied for autonomous analysis of other biological samples as a routine point-of-care (PoC) device.\u003c/p\u003e","manuscriptTitle":"A human interruption-free robotic platform for autonomous analysis of urinary samples","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-08 19:55:50","doi":"10.21203/rs.3.rs-5747031/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d2940cbb-7b6e-4248-b2e4-7609cb66db0f","owner":[],"postedDate":"January 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-09T11:23:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-08 19:55:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5747031","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5747031","identity":"rs-5747031","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}