INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIII, Issue IX, September 2024
www.ijltemas.in Page 15
Peecheck 2.0: Design and Improvement of Rapid and Low-Cost
Urine Analysis Device for Rural Health Care
Rose Ann C. Estoquia., NiΓ±a Nicole F. Galiza., Jullean Jasper D. Historillo., Teejay Marc C. Musca and Kristian Carlo B.
Victorio
Department of Electrical Engineering, Polytechnic University of the Philippines Sta. Mesa, Manila Philippines
DOI: https://doi.org/10.51583/IJLTEMAS.2024.130902
Received: 31 August 2024; Accepted: 16 September 2024; Published: 26 September 2024
Abstract: The research paper introduced improvements to PeeCheck, a portable urine analyzer, addressing the need for versatile,
rapid, and low-cost urine analysis tools, particularly in resource-constrained rural healthcare settings. The focus was on evaluating
the accuracy and speed of reading the urine strip and the electrical characteristics of the portable urine analyzer device across
various parameters. PeeCheck 2.0 represented enhancements in medical diagnostics using urine as a non-invasive sample. By
incorporating the integration of technologies such as versatility in using variations of urine strips and data cloud storage using the
Raspberry Pi Pico W microcontroller, PeeCheck 2.0 was designed to analyze 14 colorimetric urine characteristics, aiding in the
pre-diagnostic of health issues associated with kidney and metabolic-related diseases. The study evaluated PeeCheck 2.0's
performance through the analysis of urine samples collected from 3rd- and 4th-year electrical engineering students and compared
the results with those obtained from standard laboratory urinalysis. The findings highlighted PeeCheck 2.0's potential to enhance
health outcomes and reduce healthcare disparities in rural communities by providing healthcare providers with a low-cost and
reliable solution for rapid detection and addressing health concerns in areas with limited resources. The study findings demonstrated
that PeeCheck 2.0 achieved significant accuracy and speed in analyzing urine samples across 10 colorimetric parameters compared
to standard laboratory methods. PeeCheck 2.0 obtained an overall accuracy of 92.470 percent in its analyses. Results from the
evaluation with urine samples collected from electrical engineering students showed a high correlation with laboratory urinalysis,
validating PeeCheck 2.0's efficacy in detecting key indicators of kidney and metabolic-related diseases. However, continuous
programming-based calibration optimized PeeCheck 2.0 by adjusting parameters in real-time with data and sensor feedback,
ensuring consistent accuracy and reliability in urine parameter testing. This research underscored PeeCheck 2.0's role in advancing
medical diagnostics through innovation in urine analysis technology, contributing to improved healthcare delivery and outcomes in
rural and underserved communities.
Keywords: urine reagent strip, color sensor, portable urine analyzer, urinalysis, cloud storage
I. Introduction
Access to quality healthcare remains a challenge for many Filipinos, particularly in rural areas where only 25 percent have access to
essential health services compared to 46 percent in urban areas [1]. Despite recent improvements, such as telemedicine and mobile
health units reaching remote communities [2], there is still a critical need for accessible, rapid, and cost-effective diagnostic tools.
PeeCheck 2.0 aims to address this gap by providing a versatile and low-cost urine analysis device capable of detecting various
health conditions, including kidney diseases and diabetes, using urine as a non-invasive biomarker [3], [4].This improved version of
PeeCheck enhances diagnostic capabilities in resource-constrained environments, supporting the Philippines' efforts to bridge
healthcare disparities and achieve Sustainable Development Goals related to health, reduced inequalities, and sustainable
communities. By offering a reliable and efficient solution for early health screening, PeeCheck 2.0 has the potential to significantly
improve health outcomes and access to care in rural communities, aligning technical innovation with practical healthcare needs.
Significance of the Study
The rationale for developing PeeCheck 2.0 is primarily practical and focuses on addressing pressing healthcare challenges in rural
and underserved communities in the Philippines. Despite progress in healthcare delivery, significant disparities remain, particularly
in rural areas where access to medical services is limited. PeeCheck 2.0 aims to bridge this gap by providing a low-cost, rapid, and
portable urine analysis tool that leverages telemedicine and AI technologies for health monitoring and early detection of kidney and
metabolic-related diseases.
Practical Significance
ο‚· Health and Safety: PeeCheck 2.0 offers a crucial solution for monitoring the general health of individuals in rural areas,
where regular access to healthcare professionals is often scarce. By enabling the early detection of health issues through
urine analysis, it helps healthcare providers in these regions address potential concerns promptly and maintain the
well-being of their patients over time.
ο‚· Economic Benefits: The device is designed to be affordable and accessible, significantly reducing healthcare expenses by
preventing the need for costly treatments and hospitalizations through early detection. It also supports the economy by
improving productivity; healthier individuals are less likely to miss work due to illness. Additionally, rural residents save
on travel costs for healthcare consultations, and healthcare facilities can cut expenses by using PeeCheck 2.0 instead of
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIII, Issue V, May 2024
www.ijltemas.in Page 16
more expensive urinalysis equipment.
ο‚· Social Impact: By enabling telemedicine and remote consultations, PeeCheck 2.0 fosters connections and provides easier
access to healthcare services for people in remote areas. This reduces the need for long-distance travel, thereby alleviating
transportation burdens and enhancing overall health outcomes. The device also empowers rural healthcare facilities to
extend their reach and deliver comprehensive care, ensuring essential services are accessible to all, regardless of location.
ο‚· Sustainability: Integrating cloud storage for data management ensures the secure and sustainable handling of health
information, minimizing data loss and facilitating informed decision-making. AI-driven analysis of urine test results offers
timely insights, helping to create personalized treatment plans and improving the quality of care provided by healthcare
practitioners. PeeCheck 2.0 also supports government health agencies by providing real-time data collection and analysis,
aiding in public health surveillance and policy formulation.
ο‚· Ethical Considerations: PeeCheck 2.0 enhances access to quality healthcare, potentially saving lives by simplifying
pre-diagnostic procedures and preventing disease progression. It promotes health equity by empowering rural health units,
practitioners, and patients, thus contributing to the overall quality of life and well-being in resource-constrained settings.
By addressing these practical needs, PeeCheck 2.0 not only improves healthcare delivery in rural areas but also supports broader
goals of health equity, economic sustainability, and social well-being, making a significant contribution to the healthcare landscape
in the Philippines.
The Problem
Despite significant progress in healthcare access for rural Filipinos, there remains a critical need for versatile, rapid, and
cost-effective urine analysis tools to diagnose and monitor health conditions accurately in resource-constrained environments. The
current PeeCheck system is limited in its diagnostic capabilities and requires enhancements to address these gaps and provide
reliable, non-invasive, and accessible healthcare solutions for underserved rural communities in the Philippines.
Scope and Limitations
In this study, we addressed the limitations of the existing PeeCheck urine analysis system by enhancing its diagnostic capabilities
and technological components. Our goal was to expand its detection range from four specific health parameters (protein, pH level,
glucose, and specific gravity) to include bilirubin, urobilinogen, ketone, blood, creatinine, nitrite, leukocytes, ascorbate,
microalbumin, and calcium, thereby facilitating broader health evaluations and aiding in the diagnosis of metabolic, systemic,
endocrine, and urinary tract disorders. We upgraded key hardware elements, such as replacing the color sensor and touchscreen
display, and transitioning from the Arduino Nano to the more powerful Raspberry Pi Pico W for improved processing, memory, and
connectivity. Additionally, we incorporated cloud storage, Telecare AI, a medication recommendation system, and an SMS
generator to enhance data handling and communication. However, our study did not cover systemic or confirmatory diagnoses,
complex medical diagnostics, the development of advanced AI algorithms, large-scale clinical trials, or long-term reliability testing.
These exclusions ensured a focused approach on improving PeeCheck's functionality and impact in resource-constrained healthcare
settings.
II. Methodology
This research utilized a quantitative research approach to design and improve the rapid and low-cost urine analysis device,
PeeCheck 2.0, for rural healthcare. The quantitative approach was applied to achieve the study's objectives, providing a
comprehensive understanding of the research scope. This method involved using data and mathematical computations based on
observations and findings to evaluate the variables tested in the study.
The researchers employed the quantitative approach to assess the system's performance. They evaluated the sensor’s accuracy by
comparing its readings with those from conventional laboratory-grade equipment. This comparison allowed the researchers to
determine the precision of the sensor and identify any potential discrepancies between its output and the results obtained from the
laboratory-grade apparatus. These findings were crucial for making informed decisions regarding the design, enhancement, and
future implementation of the PeeCheck 2.0 system.
Project Construction
The researchers designed and built an improved functional prototype of a rapid and low-cost urine analysis device for rural health
care. They visited physical and online electrical stores to purchase and obtain the materials required for construction of the
prototype. Tests will be conducted on each material to assess functionality. Once these components are assembled into a system,
Arduino IDE software will used C++ programming language to program the entire setup.
ο‚· Acquisition of Required Materials for the Prototype
The researchers acquired materials for the PeeCheck 2.0 urine analysis device, including the central hub, Raspberry Pi Pico W.
Local physical retailers were canvassed first, and the remaining parts were obtained online. Components were examined for defects
to ensure the prototype's integrity.
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
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Once all materials were ready, an initial design was created using Solid Edge software for system component arrangement. After
confirming functionality, the researchers programmed the PeeCheck in Arduino IDE.
ο‚· Programing of codes and Assembly of the device
The researchers made the prototype by following the initial design and schematic diagram. After creating the prototype, the
researchers moved the programmed code from Arduino IDE to the Raspberry Pi Pico W and uploaded it to the prototype. Data was
sent to the prototype for this. Then, the researchers checked if the system works well and provided accurate results. After they got
the right consistency and precision in the sensor output, they finished up and completed the prototype.
Testing and Evaluation
The accuracy and performance of the device was validated through comprehensive testing. The researchers focused on assessing the
sensor's measurement readings and data reading speed to ensure the proper functioning of the prototype.
ο‚· Prototype Testing
A series of tests were conducted to gauge the color sensor's functionality. One specific test involved using a URS-14 with a urine
sample to showcase the sensor's capability to detect changes in the color and intensity of the strip. The sensor's response to these
strip alterations was meticulously monitored, data was recorded, and the results were analyzed to verify the sensor's proper
functionality.
For the study, the researchers gathered 22 males and 8 females with a total of thirty (30) participants of similar ages from
fourth-year electrical engineering students from PUP (Sta. Mesa). Each participant provided two 60ml urine samples, stored in a
sterile vial. A trial of 14-parameterurinalysis was conducted using URS-14 urine strips to read protein, pH level, glucose, specific
gravity, bilirubin, urobilinogen, ketones, blood, creatinine, nitrite, leukocytes, ascorbate, microalbumin, and calcium. The
researchers performed one trial on each urine sample to evaluate the PeeCheck 2.0 prototype, with two trials conducted in case of a
numerical value result, taking the second result as the result, ensuring a reliable assessment of the device's accuracy.
ο‚· Comparison and Evaluation with Urinalysis Results
To assess the accuracy of the prototype, standard urinalysis tests were immediately conducted by a third-party laboratory using the
same urine samples from the participants. The results from both tests underwent a Percentage Difference analysis using Microsoft
Excel to determine any significant disparities between the prototype and standard urinalysis. Additionally, the mean percentage
difference between the prototype's sensor readings and conventional urinalysis values was computed, providing an accurate
assessment of the device's clinical utility in detecting urinary biomarkers.
Table 1: Percentage Difference Analysis Equation
Equation 1
%π·π‘–π‘“π‘“π‘’π‘Ÿπ‘’π‘›π‘π‘’ =
π‘ƒπ‘’π‘’πΆβ„Žπ‘’π‘π‘˜β€ˆπ‘†π‘…π‘‰ βˆ’ π‘ˆπ‘Ÿπ‘–π‘›π‘Žπ‘™π‘¦π‘ π‘–π‘ β€ˆπ‘†π‘…π‘‰
π‘ˆπ‘Ÿπ‘–π‘›π‘Žπ‘™π‘¦π‘ π‘–π‘ β€ˆπ‘†π‘…π‘‰
(
100
)
%
Equation 2
𝑀𝐷 =
Ξ£%β€ˆπ‘‘π‘–π‘“π‘“π‘’π‘Ÿπ‘’π‘›π‘π‘’β€ˆπ‘π‘’π‘Ÿβ€ˆπ‘ π‘Žπ‘šπ‘π‘™π‘’
π‘‡π‘œπ‘‘π‘Žπ‘™β€ˆπ‘›π‘œ. β€ˆπ‘œπ‘“β€ˆπ‘ π‘Žπ‘šπ‘π‘™π‘’π‘ β€ˆπ‘π‘’π‘Ÿβ€ˆπ‘π‘Žπ‘Ÿπ‘Žπ‘šπ‘’π‘‘π‘’π‘Ÿ
Equation 3
%π΄π‘π‘π‘’π‘Ÿπ‘Žπ‘π‘¦β€ˆπ‘π‘’π‘Ÿβ€ˆπ‘π‘Žπ‘Ÿπ‘Žπ‘šπ‘’π‘‘π‘’π‘Ÿ = 100% βˆ’ 𝑀𝐷 π‘π‘’π‘Ÿβ€ˆπ‘π‘Žπ‘Ÿπ‘Žπ‘šπ‘’π‘‘π‘’π‘Ÿ
Equation 4
%π·π‘’π‘£π‘–π‘π‘’β€ˆπ‘‚π‘£π‘’π‘Ÿπ‘Žπ‘™π‘™β€ˆπ΄π‘π‘π‘’π‘Ÿπ‘Žπ‘π‘¦ =
Ξ£%π΄π‘π‘π‘’π‘Ÿπ‘Žπ‘π‘¦β€ˆπ‘π‘’π‘Ÿβ€ˆπ‘π‘Žπ‘Ÿπ‘Žπ‘šπ‘’π‘‘π‘’π‘Ÿ
π‘π‘’π‘šπ‘π‘’π‘Ÿβ€ˆπ‘œπ‘“β€ˆπ‘π‘Žπ‘Ÿπ‘Žπ‘šπ‘’π‘‘π‘’π‘Ÿπ‘ 
As shown in Equation 1, the Percentage Difference (% Difference) in the device was calculated by subtracting the PeeCheck sample
result value (PeeCheck SVR) from the Urinalysis sample result value (Urinalysis SVR), dividing by the Urinalysis SVR, and then
multiplying the result by 100. This formula provided a measure of the deviation between the PeeCheck and Urinalysis results,
expressed as a percentage.
The mean difference (MD) was obtained by dividing the total number of samples per parameter (total no. of samples per parameter)
by the cumulative percentage difference per sample (Ξ£ % difference per sample), as shown in Equation 2.
The accuracy percentage for each parameter was obtained by subtracting the mean difference for each parameter from 100 percent,
as indicated by Equation 3.
The device's overall accuracy percentage was determined by taking the average of the accuracy percentages from all parameters, as
indicated by Equation 4.
ο‚· Speed of Analyze Mode in Different Urine Strip Variations
To evaluate the speed (time per strip) of the prototype device, the researchers tested urine samples with 14-parameter strip using
mobile timer to monitor the analyzing mode time from the stepper motor running until the result was shown. The urine sample was
tested with 14-parameter urine strips. Through this testing, the researchers determined the speed of testing using 14 parameter strips.
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIII, Issue V, May 2024
www.ijltemas.in Page 18
To evaluate the accuracy of the speed or the precision of the time duration for analyzing the strips, the researchers collected five
testing time durations for each strip variation and calculated the average time for each strip variation.
ο‚· Testing Electrical Characteristics
The electrical characteristics, including input voltage, current draws, and power consumption of the prototype, were assessed during
various modesβ€”Standby with Wi-Fi (before stepper motor usage), Analyze with Wi-Fi, Standby without Wi-Fi (before stepper
motor usage), Analyze with Wi-Fi, Standby with Wi-Fi (after stepper motor usage), and Standby with Wi-Fi (after stepper motor
usage). This assessment aimed to evaluate device power efficiency, identify potential energy-saving opportunities, and estimate
long term operating costs. Actual electrical parameters were measured using the RPI CORE INA 219 power monitor module for
Raspberry Pi devices, with an OLED displaying voltage input and current draw readings.
To evaluate the data accuracy of the electrical characteristics, the researchers accumulated three testing results from different modes
of usage. These modes included Standby with Wi-Fi (before stepper motor usage), Analyze with Wi-Fi, Standby without Wi-Fi
(before stepper motor usage), Analyze with Wi-Fi, Standby with Wi-Fi (after stepper motor usage), and Standby with Wi-Fi (after
stepper motor usage). Data was recorded three trials of minute and compute for the average of that minute during the standby
modes, while average of electrical characteristics was noted during the analyze modes.
III. Analysis & Discussion
This chapter presents an overview of the findings obtained from the PeeCheck 2.0 evaluation and provides a comprehensive
analysis and interpretation of these results.
PeeCheck 2.0 Design
Fig. 24 Front View of Actual PeeCheck 2.0
The PeeCheck 2.0 is a prototype designed for pre-diagnostic urine sample testing, aiming to detect early urinary and metabolic
health conditions. It features a touchscreen display and battery percentage indicator on the front for user-friendly control and battery
indicator.
Fig. 25 Isometric View of Actual PeeCheck 2.0
The device contains a charging port, an SD card mounting port, a rocker switch for system power, tray opening and tray slot on the
side for the urine strip tray.
IV. Comparison of PeeCheck 2.0 and Laboratory Urinalysis Results
ο‚· Urobilinogen in Urine Test
Table 2: Comparison of Urobilinogen-In-Urine Test Results
Sample No.
PeeCheck 2.0
Urinalysis
Remarks
1
16 (Negative)
Negative
Similar
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2
16 (Negative)
Negative
Similar
3
16 (Negative)
Negative
Similar
4
16 (Negative)
Negative
Similar
5
3.3 (Negative)
Negative
Similar
6
16 (Negative)
Negative
Similar
7
16 (Negative)
Negative
Similar
8
3.3 (Negative)
++
Dissimilar
9
16 (Negative)
Negative
Similar
10
3.3 (Negative)
Negative
Similar
11
16 (Negative)
Negative
Similar
12
16 (Negative)
Negative
Similar
13
3.3 (Negative)
Negative
Similar
14
3.3 (Negative)
Negative
Similar
15
3.3 (Negative)
Negative
Similar
16
3.3 (Negative)
Negative
Similar
17
3.3 (Negative)
Negative
Similar
18
3.3 (Negative)
Negative
Similar
19
3.3 (Negative)
Negative
Similar
20
3.3 (Negative)
Negative
Similar
21
3.3 (Negative)
Negative
Similar
22
3.3 (Negative)
Negative
Similar
23
3.3 (Negative)
Negative
Similar
24
3.3 (Negative)
Negative
Similar
25
3.3 (Negative)
Negative
Similar
26
3.3 (Negative)
Negative
Similar
27
3.3 (Negative)
Negative
Similar
28
3.3 (Negative)
Negative
Similar
29
3.3 (Negative)
Negative
Similar
30
3.3 (Negative)
+
Dissimilar
Percentage Accuracy
93.333%
Table 2 provides a comparative analysis of urobilinogen detection results between PeeCheck 2.0 and standard urinalysis. A negative
result indicates absence of urobilinogen, while β€œ+” and β€œ++” denote trace and moderate amounts respectively. PeeCheck 2.0
demonstrated strong agreement with standard urinalysis, matching results in 28 out of 30 samples, with only two discrepancies. This
performance yields a high percentage accuracy of 93.333 percent, highlighting PeeCheck 2.0’s reliability in urobilinogen detection.
ο‚· Bilirubin in Urine Test
Table 3: Comparison of Bilirubin-In-Urine Test Results
PeeCheck 2.0
Urinalysis
Remarks
Negative
Negative
Similar
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Negative
Negative
Similar
Negative
Negative
Similar
Negative
Negative
Similar
Negative
Negative
Similar
Negative
Negative
Similar
Negative
Negative
Similar
Negative
Negative
Similar
Negative
Negative
Similar
Negative
Negative
Similar
Negative
Negative
Similar
Negative
Negative
Similar
Negative
Negative
Similar
Negative
Negative
Similar
Negative
Negative
Similar
Negative
Negative
Similar
Negative
Negative
Similar
Negative
Negative
Similar
Negative
Negative
Similar
Negative
Negative
Similar
Moderate (50)
Negative
Dissimilar
Negative
Negative
Similar
Negative
Negative
Similar
Negative
Negative
Similar
Negative
Negative
Similar
Negative
Negative
Similar
Negative
Negative
Similar
Moderate (50)
Negative
Dissimilar
Negative
Negative
Similar
Negative
Negative
Similar
Percentage Accuracy
93.333%
Table 3 compares the detection of bilirubin between PeeCheck 2.0 and standard urinalysis. Results include negative findings for
absence of bilirubin, and varying levels denoted as small and moderate. PeeCheck 2.0 exhibited strong agreement with standard
urinalysis, showing 28 out of 30 matching results with only two discrepancies. This performance translates to a high accuracy rate
of 93.333 percent for bilirubin detection in urine, underscoring PeeCheck 2.0's reliability in this parameter.
ο‚· Ketone in Urine Test
Table 4: Comparison of Ketone-In-Urine Test Results
Sample No.
PeeCheck 2.0
Urinalysis
Remarks
1
Negative
Negative
Similar
2
Negative
Negative
Similar
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3
Negative
Negative
Similar
4
Negative
Negative
Similar
5
Negative
Negative
Similar
6
Negative
Negative
Similar
7
Negative
Negative
Similar
8
Negative
Negative
Similar
9
Trace (0.5)
Negative
Dissimilar
10
Negative
Negative
Similar
11
Negative
Negative
Similar
12
Negative
Negative
Similar
13
Negative
Negative
Similar
14
Negative
Negative
Similar
15
Negative
Negative
Similar
16
Negative
Negative
Similar
17
Negative
Negative
Similar
18
Trace (0.5)
Negative
Dissimilar
19
Trace (0.5)
Negative
Dissimilar
20
Negative
Negative
Similar
21
Negative
Negative
Similar
22
Negative
Negative
Similar
23
Negative
Negative
Similar
24
Negative
Negative
Similar
25
Negative
Negative
Similar
26
Negative
Negative
Similar
27
Negative
Negative
Similar
28
Negative
Negative
Similar
29
Negative
Negative
Similar
30
Negative
Negative
Similar
Percentage Accuracy
90.000%
Table 4 compares ketone detection results between PeeCheck 2.0 and standard urinalysis, where a negative result indicates absence
of ketones in the urine sample and trace indicates minimal amounts. PeeCheck 2.0 and standard urinalysis exhibit 27 matching
results and 3 discrepancies. This indicates that PeeCheck 2.0 achieves a high accuracy of 90.000 percent in testing for ketones in
urine samples, demonstrating its reliability in this parameter compared to standard laboratory analysis.
ο‚· Blood in Urine Test
Table 5: Comparison of Blood-In-Urine Test Results
Sample No.
PeeCheck 2.0
Urinalysis
Remarks
1
Negative
Negative
Similar
2
Negative
Negative
Similar
3
Negative
Negative
Similar
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4
Negative
Negative
Similar
5
Negative
Negative
Similar
6
Negative
Negative
Similar
7
Negative
Negative
Similar
8
Negative
Negative
Similar
9
Negative
Negative
Similar
10
Negative
Negative
Similar
11
Moderate (80)
Negative
Dissimilar
12
Negative
Negative
Similar
13
Negative
Negative
Similar
14
Negative
Negative
Similar
15
Negative
Negative
Similar
16
Negative
Negative
Similar
17
Negative
Negative
Similar
18
Moderate (80)
Negative
Dissimilar
19
Moderate (80)
Negative
Dissimilar
20
Negative
Negative
Similar
21
Negative
Negative
Similar
22
Negative
Negative
Similar
23
Negative
Negative
Similar
24
Moderate (80)
Negative
Dissimilar
25
Negative
Negative
Similar
26
Negative
Negative
Similar
27
Negative
Negative
Similar
28
Negative
Negative
Similar
29
Moderate (80)
Negative
Dissimilar
30
Small (25)
Negative
Dissimilar
Percentage Accuracy
80.000%
Table 5 compares blood detection results between PeeCheck 2.0 and standard urinalysis, categorizing results as negative (no Red
Blood Cells - RBCs detected) and moderate (significant presence of RBCs). PeeCheck 2.0 and standard urinalysis demonstrate 24
matching results and 6 differing results. This indicates that PeeCheck 2.0 achieves an accuracy rate of 80.000 percent in detecting
blood in urine samples, reflecting moderate reliability in this parameter compared to standard laboratory analysis.
ο‚· Protein in Urine Test
Table 6: Comparison of Protein-In-Urine Test Results
Sample No.
PeeCheck 2.0
Urinalysis
Remarks
1
Negative
Negative
Similar
2
Negative
Negative
Similar
3
Negative
Negative
Similar
4
Negative
Negative
Similar
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5
Negative
Negative
Similar
6
Negative
Negative
Similar
7
Negative
Negative
Similar
8
Negative
Negative
Similar
9
Negative
Negative
Similar
10
Negative
Negative
Similar
11
Negative
Negative
Similar
12
Negative
Negative
Similar
13
Negative
Negative
Similar
14
Negative
Negative
Similar
15
Negative
Negative
Similar
16
Negative
Negative
Similar
17
Negative
Negative
Similar
18
Negative
Negative
Similar
19
Negative
Negative
Similar
20
Negative
Negative
Similar
21
Negative
Negative
Similar
22
Negative
Negative
Similar
23
Negative
Negative
Similar
24
Negative
Negative
Similar
25
Negative
Negative
Similar
26
Negative
Negative
Similar
27
Negative
Negative
Similar
28
Negative
Negative
Similar
29
Negative
Negative
Similar
30
Negative
Negative
Similar
Percentage Accuracy
100%
Table 6 compares protein detection results between PeeCheck 2.0 and standard urinalysis, where a negative result indicates absence
of protein in the urine sample. PeeCheck 2.0 and standard urinalysis exhibit 30 identical results, indicating perfect agreement. This
suggests that PeeCheck 2.0 is highly reliable for detecting protein in urine, achieving an accuracy rate of 100 percent in this
parameter compared to standard laboratory analysis.
ο‚· Nitrite in Urine Test
Table 7: Comparison of Nitrite-In-Urine Test Results
Sample No.
PeeCheck 2.0
Urinalysis
Remarks
1
Negative
Negative
Similar
2
Negative
Negative
Similar
3
Negative
Negative
Similar
4
Negative
Negative
Similar
5
Negative
Negative
Similar
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6
Negative
Negative
Similar
7
Negative
Negative
Similar
8
Negative
Negative
Similar
9
Negative
Negative
Similar
10
Negative
Negative
Similar
11
Negative
Negative
Similar
12
Negative
Negative
Similar
13
Negative
Negative
Similar
14
Negative
Negative
Similar
15
Negative
Negative
Similar
16
Negative
Negative
Similar
17
Negative
Negative
Similar
18
Negative
Negative
Similar
19
Negative
Negative
Similar
20
Negative
Negative
Similar
21
Negative
Negative
Similar
22
Negative
Negative
Similar
23
Negative
Negative
Similar
24
Negative
Negative
Similar
25
Negative
Negative
Similar
26
Negative
Negative
Similar
27
Negative
Negative
Similar
28
Negative
Negative
Similar
29
Negative
Negative
Similar
30
Positive
Negative
Dissimilar
Percentage Accuracy
96.667%
Table 7 compares nitrate (nitrite) detection results between PeeCheck 2.0 and standard urinalysis, distinguishing between negative
(no nitrites detected) and positive (nitrites detected) results. PeeCheck 2.0 and standard urinalysis demonstrate 29 matching results
and 1 differing result. This indicates that PeeCheck 2.0 achieves a high accuracy of 96.667 percent in testing for nitrites in urine
samples, highlighting its reliability and effectiveness in this parameter compared to standard laboratory analysis.
ο‚· Leukocytes in Urine Test
Table 8: Comparison of Leukocytes-In-Urine Test Results
Sample No.
PeeCheck 2.0
Urinalysis
Remarks
1
Negative
Negative
Similar
2
Negative
Negative
Similar
3
Negative
Negative
Similar
4
Negative
Negative
Similar
5
Negative
Negative
Similar
6
Negative
Negative
Similar
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7
Negative
Negative
Similar
8
Negative
Negative
Similar
9
Negative
Negative
Similar
10
Negative
Negative
Similar
11
Negative
Negative
Similar
12
Negative
Negative
Similar
13
Negative
Negative
Similar
14
Negative
Negative
Similar
15
Negative
Negative
Similar
16
Negative
Negative
Similar
17
Negative
Negative
Similar
18
Negative
Negative
Similar
19
Negative
Negative
Similar
20
Small (70)
++ (Small)
Similar
21
Negative
Negative
Similar
22
Small (70)
Negative
Dissimilar
23
Negative
Negative
Similar
24
Negative
Negative
Similar
25
Negative
Negative
Similar
26
Negative
Negative
Similar
27
Small (70)
+++ (Moderate)
Dissimilar
28
Negative
Negative
Similar
29
Small (70)
Negative
Dissimilar
30
Negative
Negative
Similar
Percentage Accuracy
90.000%
Table 8 compares leukocyte-in-urine results between PeeCheck 2.0 and standard urinalysis, categorizing results as negative (no
leukocytes detected), small (few leukocytes), and moderate (moderate leukocytes). PeeCheck 2.0 and standard urinalysis exhibit 27
matching results and 3 differing results. This indicates that PeeCheck 2.0 achieves a high accuracy of 90 percent in detecting
leukocytes in urine samples, underscoring its reliability in this parameter compared to standard laboratory analysis.
ο‚· Glucose in Urine Test
Table 9: Comparison of Glucose-In-Urine Test Results
Sample No.
PeeCheck 2.0
Urinalysis
Remarks
1
Negative
Negative
Similar
2
Negative
Negative
Similar
3
Negative
Negative
Similar
4
Negative
Negative
Similar
5
Negative
Negative
Similar
6
Negative
Negative
Similar
7
Negative
Negative
Similar
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8
Negative
Negative
Similar
9
Negative
Negative
Similar
10
Negative
Negative
Similar
11
Negative
Negative
Similar
12
Negative
Negative
Similar
13
Negative
Negative
Similar
14
Negative
Negative
Similar
15
Negative
Negative
Similar
16
Negative
Negative
Similar
17
Negative
Negative
Similar
18
Negative
Negative
Similar
19
Negative
Negative
Similar
20
Negative
Negative
Similar
21
Negative
Negative
Similar
22
Negative
Negative
Similar
23
Negative
Negative
Similar
24
Negative
Negative
Similar
25
Negative
Negative
Similar
26
Negative
Negative
Similar
27
Negative
Negative
Similar
28
Negative
Negative
Similar
29
Negative
Negative
Similar
30
Negative
Negative
Similar
Percentage Accuracy
100%
Table 9 compares glucose detection results between PeeCheck 2.0 and standard urinalysis, where negative results indicate absence
of glucose in urine. Both PeeCheck 2.0 and standard urinalysis show identical negative results for glucose detection, indicating
perfect agreement. PeeCheck 2.0 achieves a high accuracy rate of 100 percent in this parameter, demonstrating its reliability and
consistency in detecting the absence of glucose in urine samples.
ο‚· Specific Gravity in Urine Test
Table 10: Comparison of Specific Gravity-In-Urine Test Results
Sample No.
PeeCheck 2.0
Urinalysis
Remarks
Percentage Difference (%)
1
1.015
1.015
Similar
0.000
2
1.015
1.015
Similar
0.000
3
1.015
1.015
Similar
0.000
4
1.025
1.020
Dissimilar
0.490
5
1.010
1.005
Dissimilar
0.498
6
1.015
1.010
Dissimilar
0.495
7
1.015
1.025
Dissimilar
0.976
8
1.015
1.020
Dissimilar
0.490
9
1.015
1.015
Similar
0.000
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10
1.015
1.015
Similar
0.000
11
1.015
1.020
Dissimilar
0.490
12
1.015
1.020
Dissimilar
0.490
13
1.015
1.020
Dissimilar
0.490
14
1.015
1.005
Dissimilar
0.995
15
1.015
1.015
Similar
0.000
16
1.000
1.005
Dissimilar
0.498
17
1.015
1.020
Dissimilar
0.490
18
1.015
1.010
Dissimilar
0.495
19
1.015
1.025
Dissimilar
0.976
20
1.015
1.010
Dissimilar
0.495
21
1.015
1.015
Similar
0.000
22
1.015
1.005
Dissimilar
0.995
23
1.015
1.005
Dissimilar
0.995
24
1.015
1.020
Dissimilar
0.490
25
1.015
1.010
Dissimilar
0.495
26
1.015
1.020
Dissimilar
0.490
27
1.015
1.020
Dissimilar
0.490
28
1.015
1.020
Dissimilar
0.490
29
1.015
1.020
Dissimilar
0.490
30
1.015
1.020
Dissimilar
0.490
Mean Difference
0.460
Percentage Accuracy
99.540%
Table 10 compares specific gravity results between PeeCheck 2.0 and standard urinalysis, where specific gravity values ranging
from 1.005 to 1.030 indicate normal kidney function and fluid balance. The mean difference analysis across 30 samples shows a
minimal mean difference of 0.460 percent between PeeCheck 2.0 and standard urinalysis, with seven samples demonstrating perfect
agreement (0.000 percent difference). PeeCheck 2.0 achieves a high percentage accuracy of 99.540 percent in measuring specific
gravity compared to standard urinalysis. This high accuracy is reflected in 23 out of 30 samples having percentage differences close
to zero (0.000 percent), indicating PeeCheck 2.0's measurements closely align with those of the standard method. Minor deviations
are observed in the remaining samples, with percentage differences ranging from 0.490 percent to 0.995 percent.
Fig. 26 Comparison of the Urine Specific Gravity Percentage
In comparing standard urinalysis and PeeCheck for specific gravity, most of the results from PeeCheck 2.0 testing showed a
percentage difference greater than zero. Out of 30 samples, only seven demonstrated an exact zero percent difference in specific
gravity.
-0.175
0.025
0.225
0.425
0.625
0.825
1.025
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
% Difference
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ο‚· pH Level in Urine Test
Table 11: Comparison Ph Level-In-Urine Test Results
Sample No.
PeeCheck 2.0
Urinalysis
Remarks
Percentage Difference (%)
1
8.000
6.000
Dissimilar
33.333
2
5.000
8.000
Dissimilar
37.500
3
5.000
6.000
Dissimilar
16.667
4
8.000
6.000
Dissimilar
33.333
5
5.000
6.500
Dissimilar
23.077
6
8.000
8.000
Similar
0.000
7
5.000
5.000
Similar
0.000
8
5.000
6.000
Dissimilar
16.667
9
5.000
6.500
Dissimilar
23.077
10
5.000
5.000
Similar
0.000
11
5.000
6.000
Dissimilar
16.667
12
5.000
5.000
Similar
0.000
13
8.000
5.000
Dissimilar
60.000
14
5.000
5.000
Similar
0.000
15
5.000
5.000
Similar
0.000
16
5.000
6.000
Dissimilar
16.667
17
7.500
5.000
Dissimilar
50.000
18
5.000
5.000
Similar
0.000
19
5.000
5.000
Similar
0.000
20
8.000
6.000
Dissimilar
33.333
21
7.500
5.000
Dissimilar
50.000
22
8.000
6.500
Dissimilar
23.077
23
5.000
7.000
Dissimilar
28.571
24
5.000
5.000
Similar
0.000
25
5.000
6.000
Dissimilar
16.667
26
5.000
5.000
Similar
0.000
27
7.500
5.000
Dissimilar
50.000
28
5.000
5.000
Similar
0.000
29
5.000
6.000
Dissimilar
16.667
30
5.000
5.000
Similar
0.000
Mean Difference
18.177
Percentage Accuracy
81.822%
Table 11 compares the pH level results between PeeCheck 2.0 and standard urinalysis across 30 samples. The pH values range from
highly acidic (pH 5) to alkaline (pH 8). The mean difference between PeeCheck 2.0 and standard urinalysis is minimal, calculated at
just 0.460 percent, with seven samples showing perfect agreement (0.000 percent difference). However, the overall percentage
accuracy of 81.822 percent indicates that PeeCheck 2.0 measures pH levels with approximately 81.822 percent accuracy compared
to the standard method. This variability suggests some deviation between the two methods across the samples, emphasizing the
need for further calibration or adjustments to enhance accuracy in pH level measurements.
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Fig. 27 Comparison of Urine pH Level Percentage Difference
In comparing standard urinalysis and PeeCheck for pH level, most of the results from PeeCheck 2.0 testing showed a percentage
difference greater than zero. Out of 30 samples, only 12 demonstrated an exact zero percent difference in specific gravity.
Overall Accuracy
%β€ˆπ·π‘’π‘£π‘–π‘π‘’β€ˆπ‘‚π‘£π‘’π‘Ÿπ‘Žπ‘™π‘™β€ˆπ΄π‘π‘π‘’π‘Ÿπ‘Žπ‘π‘¦ =
Ξ£β€ˆ%β€ˆπ΄π‘π‘π‘’π‘Ÿπ‘Žπ‘π‘¦β€ˆπ‘ƒπ‘’π‘Ÿπ‘π‘’π‘›π‘‘π‘Žπ‘”π‘’
π‘π‘œ. β€ˆπ‘œπ‘“β€ˆπ‘π‘Žπ‘Ÿπ‘Žπ‘šπ‘’π‘‘π‘’π‘Ÿπ‘ 
%β€ˆπ·π‘’π‘£π‘–π‘π‘’β€ˆπ‘‚π‘£π‘’π‘Ÿπ‘Žπ‘™π‘™β€ˆπ΄π‘π‘π‘’π‘Ÿπ‘Žπ‘π‘¦ =
924.695
10
= 92.470%
The overall accuracy of PeeCheck 2.0 was computed by aggregating the accuracy percentages of all tested parameters and dividing
them by the total number of parameters. This calculation demonstrates that PeeCheck 2.0 exhibits strong overall reliability,
achieving an accuracy rate of 92.470 percent across its comprehensive range of tested parameters.
Fig. 28 Accuracy of PeeCheck 2.0
Figure 28 presents the accuracy performance of PeeCheck 2.0 across 10 parameters. Notably, glucose and protein measurements
achieve perfect accuracy at 100 percent. Specific gravity follows closely with 99.540 percent accuracy, while nitrate detection is
highly accurate at 96.667percent. Bilirubin and urobilinogen both demonstrate accuracy rates of 93.333 percent. Ketone and
leukocyte measurements each achieve 90 percent accuracy. However, blood and pH level measurements exhibit lower accuracies at
80 percent and 81.822 percent, respectively. Overall, PeeCheck 2.0 maintains a robust accuracy of 92.470 percent across all tested
parameters, indicating its efficacy in urine analysis for various health indicators.
Speed of Analyze
Table 12: Measurement of Speed in Analyzing 14 Parameters
Table 12 presents the speed of PeeCheck in analyzing 14-parameter strips. The measurement begins when the URS 14 is selected
and ends when the results are displayed. Based on five trials, the average analysis time was 107.6 s.
0.000
20.000
40.000
60.000
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
% Difference
93.33
93.3
90
80
100
96.67
90
100
99.54
81.82
92.47
0
20
40
60
80
100
Trial 1 Trial 2 Trial 3 Trial 4 Trial 5
Average
time
Time (s)
110 108 108 107 105 107.6
102
104
106
108
110
112
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Determination of Electrical Characteristics
Table 13: Measurement of Electrical Characteristics in Standby Mode (Before Stepper Motor Usage)
Enabling Wi-Fi on the device causes a minor increase in voltage from 4.021V to 4.024V, indicating a slight impact on electrical
parameters. However, the more notable effect is observed in the current draw, which rises to 98.610 mA when Wi-Fi is enabled.
This leads to a significant escalation in power consumption, increasing from 0.249W to 0.397W compared to operation without
Wi-Fi. The continuous energy consumption by the Wi-Fi module to sustain network connectivity, even in standby mode,
contributes significantly to this heightened power usage. Effective management of Wi-Fi usage is therefore essential for optimizing
battery life and enhancing overall power efficiency in electronic devices.
Table 14: Measurement of Electrical Characteristics in Standby Mode (Before Stepper Motor Usage)
Devices operate efficiently without Wi-Fi, maintaining a stable voltage of 4.021V. The current draw is significantly reduced at
61.883 mA compared to when Wi-Fi is enabled, leading to lower power consumption measured at 0.249W. This absence of
continuous energy demand from the Wi-Fi module results in overall reduced power consumption, which is advantageous for
extending battery life and optimizing power efficiency across both standby and operational modes.
Table 15: Measurement of Electrical Characteristics in Standby Mode (After Stepper Motor Usage)
With Wi-Fi enabled, using the stepper motor results in a slight increase in power consumption during standby, with an average
voltage of 3.949V, a current draw of 550.037 mA, and a power consumption of 2.170 W. This suggests that Wi-Fi adds to the power
load, necessitating effective power management to minimize energy use and prolong battery life after mechanical activities.
Table 16: Measurement of Electrical Characteristics in Standby Mode (After Stepper Motor Usage)
Trial 1 Trial 2 Trial 3 Average
Voltage (V)
4.041 4.011 4.019 4.024
Current (mA)
101.15 97.67 97.11 98.61
Power (W)
0.408 0.391 0.391 0.397
0
20
40
60
80
100
120
Trial 1 Trial 2 Trial 3 Average
Voltage (V)
4.032 4.015 4.015 4.021
Current (mA)
62.19 62.29 61.17 61.883
Power (W)
0.251 0.249 0.246 0.249
0
10
20
30
40
50
60
70
Trial 1 Trial 2 Trial 3 Average
Voltage (V)
3.952 3.949 3.947 3.949
Current (mA)
547.22 551.56 551.33 550.037
Power (W)
2.165 2.174 2.171 2.17
0
100
200
300
400
500
600
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Without Wi-Fi, the stepper motor operation results in an average voltage of 3.934V, with a slightly higher current draw averaging
551.100 mA, leading to an average power consumption of 2.168 W.
Despite Wi-Fi being disabled, the power consumption remains comparable to when Wi-Fi is enabled, indicating significant energy
usage to maintain readiness after mechanical activity. This underscores the importance of effective power management strategies to
minimize energy consumption and maximize battery life for the device.
Table 17: Measurement of Electrical Parameters in Analyze Mode
When Wi-Fi is enabled, the voltage remains nearly identical at around 3.98V. However, the current usage is higher at 522.407 mA
compared to when Wi-Fi is disabled. This results in a higher power consumption of 2.081W with Wi-Fi, indicating an increase of
about 0.079W compared to without Wi-Fi. The presence of Wi-Fi increases power consumption in both standby and active analysis
modes due to the workload and the maintenance of Wi-Fi connections.
Table 18: Measurement of Electrical Parameters in Analyze Mode Without Wi-Fi
With Wi-Fi disabled, the voltage remains around 3.960 V, and the current usage drops to 504.777 mA. This results in a lower power
consumption of 2.002 W, highlighting a reduction in power usage by about 0.079W compared to when Wi-Fi is enabled. Managing
Wi-Fi usage effectively can contribute to conserving battery life and improving power efficiency. Minimizing time spent in active
analysis mode is also crucial to reduce overall power consumption.
Trial 1 Trial 2 Trial 3 Average
Voltage (V)
3.934 3.934 3.934 3.934
Current (mA)
552.08 550.54 550.68 550.1
Power (W)
2.171 2.166 2.167 2.168
0
100
200
300
400
500
600
Trial 1 Trial 2 Trial 3 Average
Voltage (V)
3.983 3.983 3.983 3.983
Current (mA)
442.72 561.8 562.7 522.407
Power (W)
1.764 2.238 2.242 2.081
0
100
200
300
400
500
600
Trial 1 Trial 2 Trial 3 Average
Analyze Mode without Wi-Fi
Voltage (V)
3.967 3.961 3.959 3.963
Current (mA)
505.42 503.74 505.17 504.777
Power (W)
2.009 1.997 2 2.002
0
100
200
300
400
500
600
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Charging and Discharging Cycle of PeeCheck 2.0
πΆβ„Žπ‘Žπ‘Ÿπ‘”π‘–π‘›π‘” π‘‡π‘–π‘šπ‘’ =
Battery Capacity (mAh)
Charging Current(mA)
=
59400mAh
3000 mA
= 19.8 β„Ž
Using a three-ampere fast charging pin, it takes approximately twenty hours to fully charge a 59,400-mAh battery. Factors like
battery health, specific charging devices and charging efficiency may influence the actual charging time.
𝑅𝑒𝑛 π‘‘π‘–π‘šπ‘’ =
Battery Capacity
(
mAh
)
Average Current
(
mA
)
=
59400mAh
381.469 mA
= 155.7 = 6.5 π‘‘π‘Žπ‘¦π‘ 
A fully charged 54,900-mAh battery, discharging at an average current of 381.469 mA, is estimated to last approximately 155.7 h as
a power source. Factors like battery age, temperature, and device efficiency can affect the actual runtime. Regular monitoring of
actual runtime under specific usage conditions is advisable for accurate assessments and battery management.
V. Conclusion
The study evaluated the PeeCheck 2.0 prototype, a pre-diagnostic urine analysis tool, against standard laboratory urinalysis across
ten urine parameters, including Protein, pH level, Glucose, Specific Gravity, Bilirubin, Urobilinogen, Ketone, Blood, Nitrite, and
Leukocytes. The device demonstrated high accuracy, particularly excelling in detecting protein and glucose with perfect 100
percent accuracy, making it particularly effective for early screening of kidney function and diabetes. Additionally, it showed over
80 percent accuracy for other parameters such as specific gravity (99.540 percent), nitrites (96.667 percent), bilirubin (93.333
percent), and urobilinogen (93.333 percent), confirming its reliability for early health screening. Although pH level and blood
detection showed slightly lower accuracies of 81.820 percent and 80 percent, respectively, they still reflect good reliability.
PeeCheck 2.0’s rapid analysis time of approximately 107.6 seconds underscores its efficiency, making it a valuable tool for timely
and comprehensive diagnostic results. However, the evaluation of the device's electrical characteristics revealed significant power
consumption, particularly with Wi-Fi enabled, highlighting the need for effective power management.
To further improve the development and testing of PeeCheck 2.0, a more comprehensive study is recommended, including a diverse
sample set from various demographics, health conditions, and geographic locations, to ensure the device’s efficacy across a broader
population. Additionally, potential concerns about the device’s portability and long-term durability should be rigorously addressed
by testing it in various environmental conditions, such as extreme temperatures, humidity, and usage scenarios. These tests will help
identify any practical limitations or areas for improvement in real-world settings.
To enhance PeeCheck 2.0’s functionality, the study advises hardware upgrades, such as integrating additional sensors and
implementing continuous calibration through machine learning algorithms to account for variations in urine samples over time.
Ergonomic design improvements are suggested to enhance portability and user-friendliness, making it more accessible for different
users, especially in remote areas where it could serve as a preliminary screening tool before seeking treatment at tertiary hospitals.
By broadening the scope of testing and focusing on practical aspects of durability, the device’s reliability and applicability in
diverse settings can be more thoroughly demonstrated, ensuring it meets the needs of both everyday users and healthcare
professionals.
Appendix
Fig. 36 URS-14 Color Chart.
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Acknowledgment
The researchers would like to express their heartfelt gratitude and appreciation to all the individuals who have contributed to the
successful completion of this thesis. Their support, guidance, and encouragement have been instrumental throughout this journey.
First and foremost, the researchers extend their deepest gratitude to their esteemed thesis advisor, Kristian Carlo B. Victorio, for his
unwavering support, invaluable mentorship, and insightful feedback, which have been pivotal in shaping the direction of this
research.
The researchers are also immensely grateful to the research panelists: Edison E. Mojica, Daniel P. Durias, and Faustino Rural, for
their thoughtful guidance, constructive criticism, and support throughout the research process.
The researchers would like to extend their sincere gratitude to the previous PeeCheck 1.0 researchers whose groundbreaking work
provided the crucial framework for our research. Especially, to thank John Cedric Olaivar for his continuous availability and
support, which have been essential to this project's success. The creation of PeeCheck 2.0 has greatly benefited from his advice and
experience.
Additionally, the researchers express their heartfelt thanks to the third- and fourth-year participants from the College of Engineering
and Architecture. Their willingness to participate and share their insights was essential to the success of this study. Their
contributions are deeply appreciated.
The researchers would like to extend their profound appreciation to their families for their unwavering support, understanding, and
encouragement throughout this journey. Their love and patience have been a source of strength and motivation.
Finally, the researchers gratefully acknowledge the programmers, John Louie Caluminga and Romeo Escolano, who provided
crucial technical support and assistance. Their expertise and dedication were invaluable in the successful execution and
implementation of our research goals and completion of this project.
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