Jurnal Teknokes

Fast and Mobile Cataract Detection by Applying Line Laser Eye Illumination

Nicole Fehler, Robin Haag, André Messias, Martin Hessling — Fri, 01 Mar 2024 00:00:00 +0000

Cataract is observed when the eye lens becomes opaque. This condition causes blurred vision and is the main cause of blindness worldwide. Cataract diagnosis is usually performed during ophthalmologist examination using a slit lamp, which requires expertise, is expensive and bulky. In this study, we present a small handheld illumination setup for cataract detection. Ex-vivo porcine eyes are investigated to determine whether colored line lasers, 450 nm (blue), 520 nm (green) and 650 nm (red), which shine obliquely into the eye, are principally suited for detection of the Y shaped suture cataract and of cold cataract, respecting exposure limits of EU guideline 2006/25/EC. Camera images of the cataract exhibited good results under illumination with all line lasers. Observations with the physician’s eye led to an even better diagnosis of cataract. Generally, green laser light illumination was the best choice for cataract detection. With red laser light illumination it was also possible, but least suitable for this purpose. With this method, line lasers are a good choice for cataract identification, as cataract can be detected quickly and without much effort. This type of line laser illumination of the eye is safe and both types of cataract are detectable with all wavelengths. For the human eye, a further development of this system is conceivable.

Automated Environmental Stewardship: A Ribbon-Cutting Robot with Machine Vision for Sustainable Operation

Sun, 17 Mar 2024 00:00:00 +0000

This paper provides a novel way for automating ribbon-cutting rituals that use a specifically constructed robot with superior computer vision capabilities. The system achieves an outstanding 92% accuracy rate when assessing picture data by using a servo motor for ribbon identification, a motor driver for robot movement control, and nichrome wire for precision cutting. The robot's ability to recognize and interact with the ribbon is greatly improved when it uses a Keras and TensorFlow-based red ribbon identification model which obtained accuracy of about 93% on testing set before deployment in system. Implemented within a Raspberry Pi robot, the method exhibits amazing success in automating ceremonial activities, removing the need for human intervention. This multidisciplinary method assures the precision and speed of ribbon-cutting events, representing a significant step forward in the merging of tradition and technology via the seamless integration of robots and computer vision.

Design of Incu Analyzer for IoT-Based Baby Incubator Calibration

Tue, 19 Mar 2024 00:00:00 +0000

An incubator analyzer, serving as a calibration tool, is utilized to measure diverse parameters such as temperature, mattress temperature, humidity, airflow, and noise in infant incubators. The present study focuses on the development of the Design Incubator Analyzer for IoT (Mattress Temperature and Humidity) with LCD and ThingSpeak display, specifically designed for calibrating baby incubators. The primary objective is to design and develop an Incubator Analyzer as a calibration device for assessing various parameters in infant incubators, encompassing temperature, mattress temperature, humidity, airflow, and noise. The design of this calibration device incorporates a Thermocouple Type-K sensor for baby incubator mattress temperature parameters, a DHT22 sensor for humidity parameters, and an ESP32 microcontroller. The ESP32 processes data from the Thermocouple Type-K and DHT22 sensors to generate values for mattress temperature (TM) and humidity (RH), which are then displayed on LCD and ThingSpeak displays. The device underwent rigorous testing against an established measuring device, the INCU II. In the study, the TM parameter or mattress temperature exhibited the smallest error of -0.0140% at 35°C and the largest error of 0.0584% at 36°C. Concerning the humidity parameter, the largest error was 0.0570% at 32°C, while the smallest error was 0.0207% at 35°C. Overall, the Incubator Analyzer Design for IoT-Based Baby Incubator Calibration device, or IoT-based Incubator Analyzer, demonstrates potential usability following the planning and execution phases, including a thorough review of existing literature. To enhance user experience during the calibration process, an IoT system was developed for data transmission over Wi-Fi, presenting results on the ThingSpeak platform in real-time

The Enhancing Accuracy in Mycobacterium Tuberculosis Counting on Acid-Fast Bacteria Sputum through Microscope Camera and Image Processing with Thresholding Method

Achmad Agus Sholichuddin, M. Prastawa Assalim T.P — Tue, 19 Mar 2024 00:00:00 +0000

Tuberculosis (TB) is a chronic infectious illness. Pulmonary TB is still a global threat and concern. Currently, TB is among the top 10 causes of death in the world in 2015 and it is estimated that there are around 10.4 million new cases of TB in the world. 6 countries account for around 60% of new cases in the world: India, Indonesia, China, Nigeria, Pakistan, and South Africa. Globally, the TB death rate was around 22% of total world deaths from 2000 to 2015. This study introduces an innovative tool, the "Microscope Camera for Enhanced Mycobacterium Tuberculosis Counting in Acid-Fast Bacteria Sputum through Image Processing with Thresholding Method," designed to revolutionize the examination of tuberculosis (TB) samples. In response to the global threat of Pulmonary Tuberculosis, the tool employs a state-of-the-art CMOS-HD (Complementary metal-oxide-semiconductor High Definition) camera module and leverages the Python application for efficient image processing. The primary objective is to address the limitations faced by conventional diagnostic methods, particularly the challenges associated with the Rapid Molecular Test Method due to its dependence on expensive equipment, thereby making it less feasible for implementation in resource-constrained public health centers. The study underscores the persistent threat of TB as a global health concern, emphasizing its prevalence, with approximately 10.4 million new cases worldwide. Focusing on Ziehl-Neelsen stain sputum samples, the research seeks to elevate the accuracy of TB diagnosis by proposing a streamlined approach. This involves integrating a digital microscope system with a CMOS-HD camera module and utilizing advanced image processing techniques, specifically the Python Thresholding method. In the methodology, the research outlines a comprehensive data collection and analysis process. Mechanical and block diagrams illustrate the design of the module, while the system flow diagram elucidates the sequence of steps involved in TB sample analysis. The data analysis section employs metrics such as average, percentage error, and success percentage, providing a quantitative assessment of the system's performance. The results reveal an encouraging average system accuracy of 85.30%, demonstrating the potential of the developed tool to enhance the diagnostic process. However, the study transparently acknowledges areas for improvement, particularly in addressing discrepancies between manual and system calculations. The conclusion emphasizes the tool's promising role in assisting healthcare workers in detecting and counting TB bacteria in sputum, underscoring the need for ongoing refinements to achieve a definitive 100% accuracy and eliminate the possibility of misdiagnoses.

Fuzzy Logic Temperature Control on Baby Incubator Transport Battery Efficiency

Yohanes Cristomus Co'o, I.D.G. Hari Wisana, Abd Kholiq — Tue, 19 Mar 2024 00:00:00 +0000

Baby incubator transport is a life support tool used to maintain the body temperature of newborn babies during transportation from one place to another, such as from a hospital to an intensive care center with more complete facilities. The problem that often occurs in transport incubators is limitations in the power system. Baby incubator transport uses a battery as the main power source. However, the limited battery power can cause risks to the baby if there is a problem with the power system or the battery runs out. This study aims to monitor the remaining battery voltage in a transport baby incubator that uses fuzzy logic to control the temperature inside and will compare with the performance of PID control. This research uses a fuzzy logic method to control temperature and maximize battery power. In this study, researchers only looked at the efficiency of the fuzzy logic method in temperature control and the battery that will be used. The research uses a display that will display the battery voltage and current values, battery power percentage, skin temperature, chamber temperature, humidity and the selected temperature control. The module that has been made is then compared with the Digital Multimeter measuring instrument. From the results of data collection, the measurement of the remaining battery voltage between the sensor reading and the measuring instrument has a difference where at a temperature of 34 ºC it is 2.1%, at a temperature of 35 ºC it is 2% and at a temperature of 36 ºC it is 3.9%. When compared to research using PID control, fuzzy logic takes longer to reach the desired temperature and demands more battery power when compared to PID control.

Design of Ambulatory Blood Pressure Monitoring for IOT-Based Hypertension Patients

Tue, 19 Mar 2024 00:00:00 +0000

Ambulatory blood pressure monitoring or ABPM is a non-invasive method to determine the average blood pressure for at least 24 hours, not only when medical checkup. ABPM is often found in cardiac examinations and monitoring of catlab preoperative patients. This study aims to analyze the performance of the ABPM tool that can measure blood pressure continuously with a specified time interval connected to IoT so that can make it easier to get test results. The contribution of this research is a 24-hour monitoring system with delivery via IoT. The experiment was conducted 10 times with Prosim comparison at each point to assess the level of reading accuracy and effectiveness of IoT viewers. At 120/80 mmHg systole accuracy 98.42%, diastole 97.25%. While at 150/100 mmHg systole accuracy is 99.67%, Diastole is 98.1%. At 200/160 mmHg point Systole accuracy 98.35%, Diastole 98.25%. The SPSS test states that the reading data collection is acceptable and has an average commensurate with the test. The difference in viewer time on the TFT and IoT layers is 3.8 seconds and the test data value is 0% loss. The results from making this module, concluding by utilizing the sensor MPX5050 obtained sufficient accuracy, the use of ESP32 as a microcontroller processes the sensor readings which will be converted into systole-diastole values and displays on IoT so that it can slightly help analyze the patient's condition, and this module can read the simulator tool well at pressures of 120/80 mmHg, 150/100 mmHg, and 200/160 mmHg. The device showed good accuracy and reliability in measuring blood pressure at different levels compared to a vital signs simulator. The device can be used for 24-hour monitoring of hypertension patients and provide useful information for diagnosis and treatment.

PID Temperature Control of Baby Incubator Transport Battery Efficiency

Angga Vidaryanto, I.D.G Hari Wisana, Abd Kholiq, Riqqah Dwi Dewiningrum — Tue, 19 Mar 2024 00:00:00 +0000

Transport baby incubators are used to keep babies warm and safe while in transport using battery voltage sources or DC electricity, which are portable and can be used without getting a supply of electrical energy. The problem that often occurs with this tool is the limited battery power system. causes a risk to the infant in the event of power failure or battery exhaustion. We aim to evaluate the battery efficiency of Baby Incubator Transport using a PID temperature controller. The evaluation is done by comparing and analyzing the battery voltage of the device to the standard device, as well as considering the setting temperature and duration of use of the device so that it can provide convenience in evacuating babies in an emergency. The tool uses the PID method to control temperature and maximize battery power. In this design, researchers only look at the efficiency of the PID method on temperature control and the battery to be used. This module will have a display that will display the battery voltage value, battery voltage percentage, skin temperature, chamber temperature, humidity, and temperature control that has been selected in the form of a graph. Compared with the digital multimeter measuring instrument. From the results of data collection, it can be concluded that the PID method has a faster rise time to reach the setting temperature, while the fuzzy method has a longer rise time to reach the setting temperature. However, the PID method requires more battery power than the Fuzzy method. The measurement results between the display and the measuring device have a difference of 3.1% at 34°C, at 35°C it is 3.9%, and at 36°C it is 4.7%. The biggest error is at a temperature of 36ºC, the smallest is at a temperature of 34ºC. Based on the results of the observation analysis of battery power consumption, it is found that the smaller the battery energy, the smaller the current issued, as well as the voltage issued. But if the load is large, the current is inversely proportional to the center, the battery voltage decreases while the current increases.

Fuzzy Logic Method to Control Evenly Distributed and Stable Waterbath Temperature with Four Heaters

Ripqi Kurniawan, Syaifudin Syaifudin, Lamidi Lamidi, Shubhrojit Misra — Tue, 19 Mar 2024 00:00:00 +0000

Water baths are commonly used in scientific fields to incubate samples at specific temperatures. The temperature of the water bath must be controlled precisely, because even the slightest temperature variation can affect the results of the experiment. It can handle imprecise, uncertain and incomplete information, making it suitable for temperature control in water baths. This research aims to determine the distribution and stability of fuzzy logic control to control the temperature of a water bath with four heaters. Even heat distribution from the four heaters will ensure consistent water bath temperature throughout the bath. This research uses an Arduino microcontroller to process the temperature sensor output from the DS18B20, then the processed temperature value will be displayed on the TFT LCD. The independent variable in this research is the temperature value, while the dependent variable is the DS18B20 temperature sensor. The largest error value from the module measurements is at a temperature setting of 30 ºC on the 2nd temperature sensor with an error value of 1.43%. Meanwhile, the smallest error value is found at a temperature setting of 35 ºC on the 1st and 4th temperature sensors with an error value of 0.01%. Data collection used a digital thermometer comparison tool with 10 repetitions as a temperature sensor reference tool. The results obtained using this sensor are more stable and have a high accuracy value. The results of the research show that the temperature difference between points 1 to 4 when viewed from the error percentage is very small, or it can be said that the temperature distribution is even. The conclusion from these results is that the module has a stable temperature value and the error value is low and is still within the tolerance limit. permitted is ±5%.

Non-contact Respiration Monitoring Using Bio- Radar Sensor Based on Linear Regression Classifier

Muhamad Fahrudin Y., Syaifudin Syaifudin, Bambang Guruh Irianto, Phuoc-Hai Huynh — Tue, 19 Mar 2024 00:00:00 +0000

Tuberculosis (TB) is an infectious disease that mainly attacks the lungs, caused by the bacterium Mycobacterium tuberculosis. To reduce its spread, hospitals use special rooms for TB patients and health workers follow strict Standard Operating Procedures (SOP). Recent advances in medical technology have led to the development of contactless respiratory monitoring techniques, such as bio-radar sensors that utilize the Doppler principle to detect lung movement. This research aims to explore the application of bio-radar sensors for contactless respiratory rate monitoring and then combine it with machine learning methods, specifically using linear regression algorithms, to translate bio-radar output into measurable respiratory rate values. By training a regression model using a processed raw data set to identify inspiration and expiration, where 1 is inspiration and 0 is expiration. To test the performance of the contactless breathing module, it was compared to a patient monitor. The module and comparison tool were run simultaneously with 10 measurement distance points for 10 patients or respondents with each distance point taken three times. The data that has been obtained from the results of comparisons between modules and comparison tools is entered into machine learning data analysis techniques, namely accuracy, precision and recall. The accuracy results were 74.9%, precision 71.4% and recall 83.3%. This research has proven that bio radar can be used to detect lung movement.

Exploration of Digital Filter on Cardiac Monitor through Carotid Pulse and PCG (Phonocardiogram)

Bahrurrizki Ramadhan, Endro Yulianto, Sari Lutfiyah, Wahyu Caesarendra — Tue, 19 Mar 2024 00:00:00 +0000

Heart defect early detection and correct diagnosis have become important healthcare priorities. Tools for monitoring cardiac problems are constantly being developed, with the PCG (Phonocardiogram) and Cardiac Monitor via Carotid Pulse essential for heart evaluation. Although condenser microphones embedded in electric stethoscopes have been used in past research as PCG sensors, more advancements are still required to reduce received noise. This study investigates how well a Chebyshev type-II digital filter works to reduce noise on the cardiac monitor using PCG and carotid pulse. The PCG sensor is the GY-MAX 9814 module, which is interfaced with an Arduino Uno microcontroller. Matlab, Visual Studio (used as a graph viewer), and Doppler Simulator (used as a phantom cardiac signal) are used. SNR (Signal to Noise Ratio) is used in the analysis to assess the effectiveness of two digital filter orders. The average SNR value for the Doppler Simulator is 0.001404 dB at order 2, however, it climbs dramatically to 18.60023 dB at level 4 according to the results of the SNR analysis. The average SNR value in human signals is 11.50718 dB before the filter, 0.001404 dB after the post-order 2 filter, and 12.0009 dB after the post-order 4 filter. According to the results, the digital filter of order 4 is more effective in reducing noise. This study highlights the possibility of an order 4 digital filter to improve the Cardiac Monitor through PCG and Carotid Pulse. Through enhanced signal quality, the creation of this gadget holds the potential for streamlining the identification of cardiac problems. Future developments in this technology could lead to more precise and trustworthy cardiac exams, which would help with early diagnosis and treatment of cardiovascular health.

Improvement of Non-invasive Blood Sugar and Cholesterol Meter with IoT Technology

Ahmad Faisal Islamudin, Triana Rahmawati, Triwiyanto Triwiyanto, Vugar Abudlayev — Tue, 19 Mar 2024 00:00:00 +0000

In checking blood sugar levels, patients often feel uncomfortable because invasive blood sampling must be done and if done to patients who have a history of high glucose, it can cause wounds that are difficult to heal and can be operated on. This study aims to non-invasively monitor cholesterol levels, reducing discomfort and pain for patients by eliminating the need for invasive procedures. The method used in this research is the MAX30102 sensor will detect blood sugar through the patient's finger, the data will be processed in ESP8266 as monitoring will connect to the OLED LCD as a viewer and IoT as data storage with Wi-Fi connected. In this study, the greatest accuracy value was obtained 99.03% with the largest error value of 10.52% and the smallest accuracy value was 89.48% with the smallest error value of 0.97%. From all measurement results, the average accuracy value is 93.974% and the average error is 6.026%. It can be concluded that the development of a non-invasive method for monitoring blood sugar levels by utilizing the MAX30102 sensor with this accuracy value shows that this non-invasive method is reliable for monitoring blood sugar levels. In future studies, researchers are expected to use more accurate sensors and take more data to get a better average value.