Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
The Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics (IJEEEMI) is a peer-reviewed open-access journal. The journal invites scientists and engineers throughout the world to exchange and disseminate theoretical and practice-oriented topics which covers three (3) majors areas of research that includes but not limited to: (1) Electronics, (2) Biomedical Engineering, and (3) Medical Informatics. Submitted papers must be written in English for an initial review stage by editors and further review process by a minimum of two reviewers. We intend to maintain, improve the high standards of excellence, visibility, and further development of the journal. The goal is to be indexed in the Scopus and the Web of Science in the next year, with the continuous increasing the quality of the journal articles and visibility of the journal. A submitted paper should be an original work, and should not have been presented or published previously. Submission for publication in this journal presumes that the paper is not being considered for publication elsewhere and is not under review by any other publication. All articles undergo a thorough evaluation process while following all ethical guidelines to maintain the highest publishing standards. The IJEEEMI team strictly reviews the suitability, originality of manuscripts for publication.
Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia
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Real-Time, Multi-Command Drone Navigation Using a Consumer-Grade EEG-Based SSVEP BCI
Steady-state visual evoked potential (SSVEP) brain-computer interfaces (BCIs) provide a non-invasive method for hands-free device control. However, their practical applications are limited by reliance on costly laboratory-grade electroencephalography (EEG) systems. This study addresses this gap by designing and evaluating a real-time, six-command SSVEP-BCI for drone navigation using a consumer-grade EEG headset. An adaptive processing pipeline was developed to extract spectral and spatial features, which were classified using Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN) models. Analysis of data from 30 participants revealed that the RF classifier achieved an optimal balance between performance and speed, with a high classification accuracy of 87.24% and a low computational latency of 0.09 seconds, resulting in a high information transfer rate (ITR) of 35.0 bits/min. In contrast, the ANN was insufficiently accurate, and SVM performance was marginal. These findings demonstrate the viability of low-cost, multi-command SSVEP-BCIs for applications in assistive technology, teleoperation, and human-computer interaction.
Copyright (c) 2025 Anderias Eko Wijaya, Nurizati Nurizati, Rian Hermawan, Muhammad Agung Suhendra (Author) https://creativecommons.org/licenses/by-sa/4.0
Comparative Study of Filter, Wrapper, and Hybrid Feature Selection Using Tree-Based Classifiers for Software Defect Prediction
Software defect prediction (SDP) is essential for improving software reliability by enabling the early identification of modules that may contain defects before the release stage. SDP commonly exhibits redundant or non-contributory metrics, underscoring the need for feature selection to derive a more informative subset. To address this problem, the present study investigates and compares the effectiveness of three feature-selection strategies: SelectKBest (SKB), Recursive Feature Elimination (RFE), and the hybrid SKB+RFE, in enhancing the performance of tree-based classifiers on the NASA Metrics Data Program (MDP) data collections. The study utilizes three classification algorithms, namely Random Forest (RF), Extra Trees (ET), and Bagging (Decision Tree), with Area Under the Curve (AUC) serving as the primary metric for assessing model performance. Experimental results reveal that the RFE and Extra Trees combination yields the top performance, producing an average AUC of 0.7855. This is subsequently followed by the SKB+RFE+ET configuration, which achieves an AUC of 0.7809, and SKB+ET at 0.7776. These findings demonstrate that iterative wrapper-based approaches such as RFE can identify more relevant and effective feature subsets than filter or hybrid strategies, with the RFE+Extra Trees configuration yielding the strongest overall predictive performance and wrapper-based methods exhibiting higher stability across heterogeneous datasets. Even without hyperparameter tuning and relying solely on class-weighting rather than explicit resampling techniques, the findings offer empirical insight into the isolated influence of feature selection on predictive performance. Overall, the study confirms that RFE combined with Extra Trees offers the strongest predictive performance on NASA MDP data collections and forms a foundation for developing more adaptive and robust models.
Copyright (c) 2025 Rahmayanti Rahmayanti, Rudy Herteno, Setyo Wahyu Saputro, Triando Hamonangan Saragih, Friska Abadi (Author) https://creativecommons.org/licenses/by-sa/4.0
Telemedicine and AI in Remote Prediabetes Monitoring Among Adolescents
The escalating prevalence of prediabetes in Indonesia, particularly among children and adolescents, necessitates the development of lightweight, adaptable, and cost-effective telemedicine solutions for the noninvasive monitoring of blood glucose levels. Existing systems predominantly employ machine learning and deep learning approaches that require substantial computational resources and stable internet connectivity, limiting their applicability in regions with constrained digital infrastructure. The objective of this study is to develop an artificial intelligence (AI)–driven telemedicine system that employs an expert system to determine prediabetes status by utilizing commercially available smartwatches as noninvasive optical sensors. The methodological approach includes an examination of smartwatch capabilities to identify Bluetooth Low Energy (BLE) sensors, service architectures, and the Generic Attribute Profile (GATT); the development of a Rule-Based Reasoning (RBR) expert system to determine prediabetes status using Fasting Plasma Glucose (FPG) and Postprandial Plasma Glucose (PP2) measurements; and the application of Rapid Application Development (RAD) methods in the development of Flutter-based mobile applications and Laravel Inertia Vue–based web applications. The results of this study demonstrate that the telemedicine system operates in both offline and online modes and incorporates AI functionality on mobile devices and servers without requiring extensive computational resources. All system functionalities successfully passed testing, and the expert system achieved 100% accuracy in determining prediabetes status. In conclusion, the integration of telemedicine and AI-based expert systems provides an effective, economical, and flexible solution that can be widely implemented in Indonesia to reduce the increasing incidence of prediabetes through continuous digital health monitoring.
Copyright (c) 2025 Siti Aisyah Solechah, Setyo Wahyu Saputro, Muhammad Hifdzi Adini, Mohammad Reza Faisal, Erick Kurniawan, Umiatin Umiatin (Author) https://creativecommons.org/licenses/by-sa/4.0
Design of a Clinical CRM Model Integrated with EMR to Support NCD Education
Non-Communicable Diseases (NCDs) have become a major global health issue requiring serious attention, a challenge for the global health system, because of factors such as urbanization, lifestyle changes, and population aging have caused increased cases of diseases such as diabetes, cancer, and cardiovascular disorders worldwide. Clinics play a crucial role in providing health education to the community to increase awareness of NCD prevention. Health service optimization can be achieved through the integration of Customer Relationship Management (CRM) and Electronic Medical Record (EMR) systems, which enable improved patient-provider relationships and more effective access to medical records. This study aims to develop an integrated CRM EMR application model for clinics as a tool for NCD education. The study employed a user-centered design approach with a descriptive research methodology. The research stages consisted of research, requirements, design, and evaluation, conducted through literature reviews, field data collection, and interviews with clinic staff to identify system requirements and user experiences. The design phase produced a database model, relational tables, and a user interface prototype that represent the integration of CRM and EMR systems. The findings indicate that the proposed model has the potential to improve the efficiency of patient data management, facilitate health education, and strengthen interactions between patients and healthcare providers. The main contribution of this research is the provision of technical outputs in the form of a database design and user interface prototype, which can serve as the foundation for further development and implementation of an integrated CRM EMR system in clinics. By embedding the CRM–EMR model within broader e-health strategies and community-based health promotion programs, this research lays the groundwork for a sustainable digital ecosystem that supports preventive healthcare and advances Indonesia’s digital health transformation.
Copyright (c) 2025 Avid Wijaya, Prima Soultoni Akbar, Eiska Rohmania Zein, Fery Fadly, Rizka Ajeng Trikusumah (Author) https://creativecommons.org/licenses/by-sa/4.0
Topic Mining-Based Knowledge Discovery of User Health Information Needs
Understanding the user’s need for health information has become increasingly important as the use of digital health services continues to grow. However, the unstructured data of user-generated questions presents challenges in accurately capturing and analyzing these needs. This study contributes to addressing SDG 3 (Good Health and Well-being) by utilizing topic mining-based knowledge discovery to identify the primary topics emerging from user questions submitted through the “Tanya Dokter” feature on the Alodokter platform. A total of 8,550 questions were obtained through web scraping between July 2024 and June 2025. The collected data were preprocessed and subsequently analyzed using seven topic modeling approaches: Latent Dirichlet Allocation (LDA), Correlated Topic Model (CTM), Latent Semantic Analysis (LSA), Non-negative Matrix Factorization (NMF), BERTopic, Top2Vec, and ProdLDA. To assess model performance, the coherence metric (c_v) was employed to identify the most effective method. Among these techniques, NMF achieved the best results, producing the highest coherence score of 0.67 with six well-defined topics. The findings show six primary areas of concern: pregnancy; menstruation and contraceptive management; general health and minor ailments; infant care; dermatological conditions; and musculoskeletal and other physical complaints. General health-related issues occurred most frequently, particularly during seasonal transitions, while menstruation and contraceptive management received the least attention, despite menstruation contributing to women’s health risks and the use of contraceptives helping to reduce maternal mortality in Indonesia. These findings offer valuable insights for digital health platforms like Alodokter to enhance information delivery and health literacy, ultimately improving online health services and supporting the achievement of SDG 3
Copyright (c) 2025 Dayana Khoiriyah Harahap, Ken Ditha Tania, Putri Eka Sevtiyuni (Author) https://creativecommons.org/licenses/by-sa/4.0
CoAtNet for Chest X-Ray Report Generation with Bi-LSTM and Multi-Head Attention
In clinical environments, Chest X-Ray (CXR) represents the most prevalent diagnostic instrument, particularly facilitating diagnostic procedures through medical report. However, manual report preparation is time-consuming, highly dependent on the expertise of radiologists, and carries the risk of errors due to high workloads and limited expert staff. Therefore, an automated system based on artificial intelligence is needed to ease the workload of radiologists while increasing consistency. This study aims to develop an automated medical report generation system with balanced data distribution, reliable encoder, and bidirectional contextual understanding. The main contributions of this study include the implementation of an undersampling strategy based on majority captions followed by oversampling on minority labels while maintaining a proportion of labels with higher frequencies, the use of Bi-LSTM with Multi Head Attention (MHA) to strengthen text context understanding, and the use of CoAtNet as a visual encoder that combines the strengths of CNN and Transformer. The methodology incorporates image preprocessing via gamma correction for contrast improvement, data selection, balancing through combined undersampling and oversampling, and CoAtNet implementation as encoder paired with Bi-LSTM and MHA as decoder. Experimental execution employed the IU X-ray dataset, with assessment conducted using BLEU and ROUGE-L metrics. Outcomes revealed that the CoAtNet configuration with Bi-LSTM and MHA, coupled with the undersampling-oversampling strategy, delivered superior performance evidenced by a cumulative score of 1.642, with BLEU-1 to BLEU-4 and ROUGE-L achieving 0.480, 0.329, 0.245, 0.183, and 0.405, respectively. These findings prove that the combination of data balancing strategies with CoAtNet and Bi-LSTM is able to produce more accurate automated medical reports and reduce bias towards the majority label.
Copyright (c) 2025 Rafy Aulia Akbar, Ricky Eka Putra, Wiyli Yustanti (Author) https://creativecommons.org/licenses/by-sa/4.0
Gait Variability and Phase Segmentation in Obese and Normal Individuals Using Multi-Location IMUs and Hidden Markov Models Supervised Marginal
Obesity is known to disrupt motor control and biomechanics; however, detailed gait alterations in individuals with obesity remain underexplored, particularly in dynamic and real-world walking conditions. This study aims to quantitatively characterize gait differences between individuals with obesity and those of normal weight by analyzing postural and temporal gait parameters. The investigation focuses on pitch, roll, and cadence dynamics using body-worn inertial sensors, with phase transition modeling via Hidden Markov Models. This work proposes a novel framework that integrates multi-location Inertial Measurement Unit (IMU) sensors and a Hidden Markov Model–Supervised Marginal (HMM-SM) approach to detect and classify gait phases with high accuracy, offering practical value for clinical gait assessment and personalized rehabilitation. IMU sensors were placed on the waist, thigh, calf, and heel to record gait data from participants in both obese and normal-weight groups. Gait segmentation and phase modeling were conducted using 4-, 5-, and 8-state HMMs. Quantitative analysis revealed significantly greater postural variability in the obese group during slow walking, with standard deviations in roll and pitch reaching 20.68° and 9.23°, respectively—much higher than the normal-weight group (0.60° and 0.26°). Hidden state transitions from 5-state pitch HMMs showed a very strong effect size for the obese group (Cramér’s V = 0.72) compared to a moderate effect for the normal-weight group (V = 0.33). Similar patterns were observed for roll and cadence. In terms of segmentation accuracy, the 4- and 5-state HMMs outperformed the 8-state model, achieving accuracy levels above 99%, while the 8-state model reached only ~93%. The findings demonstrate that obesity significantly alters gait dynamics, particularly in postural stability and gait phase transitions. The proposed IMU-based HMM-SM framework effectively captures these changes, offering a reliable tool for gait analysis in clinical and biomechanical applications.
Copyright (c) 2025 Suto Setiyadi, Husneni Mukhtar , Willy Anugrah Cahyadi, Diyah Widiyasari, Mohamad Ramdhani, Nigel Bryan Tang (Author) https://creativecommons.org/licenses/by-sa/4.0
Hyperparameter optimization of breast ultrasound image classification models using ant colony optimization based on texture features
Breast cancer is one of the most prevalent types of cancer in humans and has the highest cumulative risk compared to other types of cancer. Accurate diagnosis and efficient intervention for this disease are very important to improve patient survival. This study aims to optimize machine learning algorithms using a limited number of features in order to produce an efficient breast cancer classification model that remains competitive with deep learning–based models. Furthermore, this study is expected to assist pathologists and doctors in the treatment of breast cancer. The dataset used in this study consists of three classes (benign, malignant, and normal) with a total of 780 breast ultrasound images from 600 patients. All images were processed and augmented to enrich data variation before modeling using five classification algorithms: Random Forest, SVM, Decision Tree, Gradient Boosting, and k-NN. Modeling was conducted in two scenarios: without optimization and with hyperparameter optimization using the Ant Colony Optimization algorithm. The results showed that the GLCM angle orientation had a relatively small effect on model performance. The best accuracy for each orientation was achieved with k-NN+ ACO (0.95 at 00), SVM+ACO (0.94 at 450), SVM+ACO (0.90 at 900), and RF+ACO (0.95 at 1350).
Copyright (c) 2025 Ahmad Fauzi, Lukmanda Evan Lubis, Raju Wandira, Syarto Musthofa (Author) https://creativecommons.org/licenses/by-sa/4.0
Deep Learning-Based Hippocampal Segmentation and MTA Classification Using U-Net with ResNet-50 Backbone
Medial Temporal Atrophy (MTA) is a key biomarker in the early diagnosis of dementia. However, its assessment through manual inspection of MRI scans is subjective, time-consuming, and prone to inter-observer variability. This creates the need for automated systems that can provide accurate, consistent, and clinically interpretable evaluations. This study aims to develop a hybrid deep learning framework that integrates U-Net with a ResNet-50 backbone for simultaneous hippocampal segmentation and MTA grading, thereby reducing diagnostic subjectivity and bridging the gap between image processing and clinical interpretation. The main contribution of this work lies in the dual functionality of the proposed architecture: not only producing precise segmentation masks of the hippocampal region but also classifying the degree of atrophy into MTA scores (0–4), which previous studies on hippocampal segmentation have not addressed. The proposed method employs a U-Net for pixel-level segmentation, enhanced with a ResNet-50 backbone to stabilize gradient propagation and enrich feature representation during encoding. Results demonstrated excellent performance, achieving a training accuracy of 99.9% with strong convergence between training and validation curves. On a test set of 32 coronal MRI slices, the model correctly classified 26 samples, misclassifying only 6. Overall, the proposed U-Net with ResNet-50 backbone provides an accurate and reliable end-to-end solution for hippocampal segmentation and MTA grading. Its clinical performance demonstrates parity with expert radiologists, underscoring its potential as a decision-support tool in dementia diagnosis. Future work will focus on extending this framework to 3D U-Net architectures, enabling the integration of volumetric MRI features to enhance robustness and generalizability across diverse datasets further
Copyright (c) 2025 Aldienannisa Devin Salsabila, Fatimah Fatimah, Darmini Darmini, Selamet Budi Kurniawan (Author) https://creativecommons.org/licenses/by-sa/4.0
Design of Instrument Measurement for X-Ray Radiation with Geiger Muller
Radiation monitoring aims to know firsthand the rate of radiation exposure in a work area to ensure the safety and health of workers who will work in the radiation emitting area in accordance with the principle of ALARA (As Low As Reasonably Achievable). This study developed a nature X-ray radiation measuring device using a Geiger Muller tube detector and can display the results of the measurement of numbers in microSievert units and Counter Per Minute to the LCD Character display and Android which have function to reduce the radiation exposure received by the radiation workers. The output of the detector is processed using Arduino Uno. Comparison of the results of the module with a calibrated standard survey meter measures the reference that the module can be used. The radiation detection system testing of this module is carried out to adjust the current condition of the Covid-19 pandemic, so that the module tests the background radiation (natural radiation). Based on module testing and experiments, it was obtained that data from 10 times data collection showed the accuracy value of the radiation measuring device using a Geiger Muller detector was 90.71% for the measurement of background radiation in a closed room. The Geiger Muller detector is not accurate for measuring small radiation exposures, the module can be used to measure background radiation and fluoroscopy X-ray radiation
X-Ray; Geiger Muller; Arduino Uno, Android;
Copyright (c) 2020 https://creativecommons.org/licenses/by-sa/4.0
Modification of Infant Warmer with PID Temperature Controlled with Apgar Monitoring and Respiration Rate
Newborns, both healthy and premature babies, have a difficult problem, newborns withstood the environment with new ones that previously depended on the mother's uterus and also as a place for initial examination in nayi after just being discussed. The purpose of this study was to add a baby warmer with PID temperature control and add respiration parameters and APGAR. The contribution in this study is the PID control system which is used to control temperature and is also a sensor of respiration rates to determine respiration in infants. So that the values ​​of the temperature sensor and Respiration Rate sensor can be stable it is necessary to adjust the sensor's reading time. The LM35 sensor can activate temperature in the body, the Flex Sensor is used to read respiration values ​​in infants. Based on data collection of respiration rates taken from adults, an average value of 18.5 times per minute was obtained in the first attempt, and 21 times per minute in the second trial. Respiratory rate values ​​that have not been stable take data because there are still external factors, such as changes from respondents and others. The results of this research can be implemented on baby warmers to improve the application of updates to the baby.
Infant warmer, temperature, PID, Respiration Rate
Central Monitor Based Personal Computer with SpO2 and Body Temperature Parameters Via Wireless Xbee Pro
Central patient monitor that is not real-time and continues will cause inaccuracies monitoring results and also sending data that is still using cable will cause limited distance. The purpose of this research is to design a central monitoring based personal computer via Xbee Pro. The contribution of this research is, the system works in real-time and continues, more parameters, using wireless, longer transmission distances. So that monitoring can be done in real-time and continue via wireless with more distance, then the wireless system uses the Xbee Pro module which has larger output power and uses the same number of wireless modules between transmitter and receiver. Body temperature was measured using the LM35 sensor and oxygen saturation in the blood was measured using the MAX30100 sensor. Data is sent using Xbee Pro and displayed on a personal computer. At the distance of receiving data approximately 25 meters with a wall divider, obtained results of smooth monitoring without any loss of data. The results showed that the average SpO2 error value was 0.34% in module 1 and 0.68% in module 2. The average value of body temperature error was 0.46% in module 1 and 0.72% in module 2. The results of this research can be implemented in a centralized patient monitoring system at the hospital, making it easier for health workers to monitor multiple patients, with the results of monitoring in real-time and continue, more parameters, via wireless with greater distance.
Design of Vital Sign Monitor with ECG, BPM, and Respiration Rate Parameters
Vital sign monitor is a device used to monitor a patient's vital sign, in the form of a heartbeat, pulse, blood pressure, temperature of the heart's pulse form continuously. Condition monitoring in patients is needed so that paramedics know the development of the condition of inpatients who are experiencing a critical period. Electrocardiogram (ECG) is a physiological signal produced by the electrical activity of the heart. Recording heart activity can be used to analyze how the characteristics of the heart. By obtaining respiration from outpatient electrocardiography, which is increasingly being used clinically to practice to detect and characterize the abnormal occurrence of heart electrical behavior during normal daily activities. The purpose of this study is to determine that the value of the Repiration Rate is taken from ECG signals because of its solidity. At the peak of the R ECG it has several respiratory signals such as signals in fluctuations. An ECG can be used to determine breathing numbers. This module utilizes leads ECG signals to 1 lead, namely lead 2, respiration rate taken from the ECG, BPM in humans displayed on a TFT LCD. This research module utilizes the use of filters to obtain ECG signals, and respiration rates to display the results on a TFT LCD. This module has the highest error value of 0.01% compared to the Phantom EKG tool. So this module can be used for the diagnosis process.
Development of Wireless Central Monitor Using X-bee Pro S2C (Electrocardiogram and Heart Rate)
Centralized monitoring of the condition of patients with serious conditions that are carried out continuously and in real-time is very important. in the development of previous researchers have some shortcomings, namely sending data still using cable, completeness of parameters that are still small, close delivery distance, not real-time, and continue. The purpose of this study is that the thick system is done wirelessly, more parameters, longer delivery distance, and can monitor electrocardiogram and heart rate in real-time and continue. the contribution of this research is that the wireless system can send ECG and bpm in real-time, long-distance, and continuously. To make deliveries in real-time, this study uses 2 transmitters and 2 receivers. Electrocardiogram signal obtained from tapping Lead II, then processed using a microcontroller circuit and the results in the form of a heart signal will be. Data is sent using X-Bee Pro. Data is displayed in the form of a patient's heart and BPM signals. In measuring BPM values ​​obtained error values ​​in module 1 0.1388% for BPM 240 and 0.093% for BPM 180, in module 2 0.1388% for BPM 240 and 0.185% for BPM 180, data transmission can be done well at a distance of 8 meters, 10 meters, and 25 meters with a barrier. The results of this study indicate that sending wirelessly can be done at a certain distance and in real-time. This research can be implemented in a central monitor in a hospital with more patients
Copyright (c) 2020 Bariroh Izzatul Ulumiddiniyah (Author) https://creativecommons.org/licenses/by-sa/4.0
Use Chest Vibrator to Prevent Pulmonary Infection in Patients with COPD
Chest infection is an infection that affects your lungs, either in the major respiratory tract (bronchitis) or in small air sacs (pneumonia). There is an accumulation of pus and liquid (mucus), and the respiratory tract becomes swollen, making breathing difficult. Chronic obstructive pulmonary disease (COPD) is a serious cause of death globally. This disease is characterized by episodes of acute exacerbations or aggravation that are superimposed upon a gradual decrease in pulmonary function. The study developed a device for vibratory techniques in chest physiotherapy. Vibration is a pressure applied to the chest during exhalation to move the secret into the large respiratory tract. Measurements of the mechanical impedance of the respiratory system in frequencies from about 5 Hz to about 70 Hz in the higher frequency range should be evaluated on the basis of the lung model. In this device, using frequencies that are often used in the field: 30, 40, 50 Hz and a timer of 3 to 5 minutes. This device uses a 12V DC motor as a vibration medium that will be connected to the engine inside the paddle. It uses IC NE 555 as an important component of the conductor circuit. This module uses an LCD screen of 16x2 characters as screen. The result was found that by using IC NE 555 as an important driver in showing acceptable system accuracy, only a minimum error value of ± 0.008% and a maximum error value of ± 0.02%. The advantage of this module is that it is equipped with a 3-5 minutes timer so that it can provide efficient therapy according to the time needed and is equipped with an LCD display to make it easier to observe the time
Copyright (c) 2019 Sari Luthfiyah (Author) https://creativecommons.org/licenses/by-sa/4.0
The Impact of Using Digital Filter and Analog Filter on EMG Signal
Many cases of accidents which resulted in humans having to surgery to save them, then performend muscle therapy to help the patient’s recovery after going through the post-surgery. This therapy has a purpose, so that the patient’s body is expected to return to normal. An exoskeleton is a tool like an additional clothing that aims not only to protect, but also to increase the wearer's abilities. Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. The purpose of this study was to analyze the differences in the use of analog and digital filters on EMG, as well as the effect on the exoskeleton simulation. The method used in the main design consists of the myoware module, notch circuit, low pass filter, arduino uno, DAC module, teraterm software, and matlab. The intercepted signal is taken from the biceps using a disposable electrode (AG/AGCL.). The EMG signal tapped by the myoware module will continue to another circuit, then recorded on the Teraterm software, and analyzed in MATLAB. The voltage value on the analog filter is 1.541 Volt during relaxation and 2.086 Volt during contraction, while the digital filter that has passed through the DAC has a value of 41.8 mVolt during relaxation and 269.1 mVolt during contraction. The results of this study obtained that digital and analog filter values ​​have an average difference of 5 to 30. The conclusion of this research tool can detect changes in the use of analog and digital filters, in the future research can be developed by comparing other types of digital filters along with replacement to wireless systems. The benefit or purpose of this research is as a simulation of exoskeleton skeletal motion and can see the difference between the use of digital and analog filters
Copyright (c) 2019 Endang Dian Setioningsih (Author) https://creativecommons.org/licenses/by-sa/4.0
Electrocardiogram 12 Lead in Transverse Plane Based on Computer
An electrocardiogram (ECG) is a graph produced by an electrocardiograph to detect heart abnormalities by measuring the electrical activity produced by the heart. Heart disease currently represents 16% of total deaths from all causes. Based on data from the Basic Health Research (Riskesdas) in 2018, the incidence of heart disease is increasing from year to year. At least 15 out of 1000 people, or about 2,784,064 individuals in Indonesia suffer from heart disease. The purpose of this research is to make a 12 lead electrocardiogram (ECG) PC based with storage (transverse plane) to make it easier for users to see the condition of heart signals. The contribution of this research is that devices can display heart signals on a computer and the results can be saved in the form of images and excel. This study used a pre-experimental method. The ECG signal is obtained from the phantom which is connected using an ECG cable. The results of the leads are in the form of heart signals and BPM which will be processed on the microcontroller and then the results will be displayed on a computer and can be saved. Measurement of the BPM value using Phantom on V2 obtained an error value of 0.25% for 120 BPM, 0.125% for 240 BPM. The maximum limit in the BPM error tolerance is ±5%. The results showed that the system as a whole worked well. The shape of the V2 signal generated by the module is the same as the manufacturer's ECG.
Electrocardiogram, Heart Signal, BPM, PC, Storage
Copyright (c) 2019 YB. Rischa Via Octantri (Author) https://creativecommons.org/licenses/by-sa/4.0
Defibrillator with Monophasic and Biphasic Fibrillation Signal
Lack of knowledge about functions, working methods, and safety can pose a big risk (death). Defibrillators are electronic devices that flow the shock electrical signal (pulse) to the heart muscle to maintain myocardial depolarization that is experiencing cardiac fibrillation (ventricular fibrillation or atrial fibrillation). The purpose of this study is make a defibrillator as a learning material so that anyone can have sufficient knowledge about the working principle and function of the defibrillator tool. This defibrillator is equipped with two wave selections namely monophasic and biphasic with asynchronous and synchronous modes, the energy provided ranges from 10-50 joules with use on tools 10, 20, 30, 50 joules. The energy will be flowed to the patient by pressing the Discharge button on Paddle which equivalently is adapted to humans. Energy disposal is controlled by a driver then the relay will work alternately from the first condensator then the second condensator to Paddle. Energy testing is done by 10 times using a defibrillator analyzer in a predetermined test point, the results of the monofacic wave measurement obtained by the error value of each setting with the smallest value of 0.0024% and the largest error value of 0.023%. To produce energy (joule) that appropriate capacitor is needed with real value. Bifasic waves cannot be tested because the defibrillator analyzer measuring instrument cannot detect the energy value (joule) capacitor that has been delivered.
defibrillator, monophasic,biphasic, capacitor
Copyright (c) 2019 I Gusti Bagus Aditya Wijaya (Author) https://creativecommons.org/licenses/by-sa/4.0
Baby Incubator Monitoring Center for Incubator Temperature and Skin Temperature using WiFi Network
Premature infants with birth age less than 38 weeks who have a higher risk of death require continuous monitoring. Therefore, a temperature monitoring system is needed in the incubator. So the purpose of this research is to develop a baby incubator monitoring system that still uses the manual method to be faster and easier. This module has a wireless system and uses a Wi-Fi network for data transmission. By using several ESP32 modules assembled into a central system, where the data obtained from monitoring skin temperature from the DS18B20 sensor and incubator temperature monitoring data from the DHT22 sensor will be processed by the ESP32 which functions as a client and collected at one central point of the ESP32 which has been functioned as a server using the wifi network as data transmission. then the data collected on the server will be displayed on the nextion TFT display. Based on the overall measurement results using 2 baby incubators, the highest error value is 1.387% for the incubator temperature parameter and 3.911% for the skin temperature parameter. The results showed that the module that was made still contained errors in each measurement. The results of this study can be implemented to make it easier for nurses to monitor premature babies easily and quickly
Monitoring, Baby Incubator, Temperature, Skin Temperature, Wireless, ESP32
Copyright (c) 2019 Nuristadarro Nuristadarro (Author) https://creativecommons.org/licenses/by-sa/4.0
A Simple Medical Record System of Non-Invasive Blood Glucose Level Measurement Results for Diabetes Care Using Graphical User Interface (GUI) MATLAB
Diabetes is a chronic metabolic disorder caused by a decrease in the pancreas to produce insulin. We present a medical records system and reminders to patients of the measurement results of non-invasive blood glucose levels. Measuring blood glucose levels is vital in avoiding potential adverse health effects like diabetes. Generally, measuring blood glucose levels using the conventional method is injure the patient's finger. Currently, the non-invasive method was famous as one of the detections of blood glucose by applying the physical properties of laser absorption. In this paper, we use the photodiode as a detector, the LED as a sensor, and a signal conditioning circuit. The results showed that non-invasive glucose monitoring has the potential to measure glucose levels with sensitivity and linearity of 3.21 mg/dL and 98%, respectively. As a result of measuring the blood glucose levels of the subject was displayed on the LCD module was designed. We designed a simple application and medical record using Blynk applications and GUI MATLAB for recording the measurement results of blood glucose level. In the future, applications that have been developed can be used by doctors for monitoring the measurement of the blood glucose level and provide information to patients by mobile applications, sending an email or message the measurement results, the decision of a disease or not, and reminds the re-measurement time
Copyright (c) 2021 https://creativecommons.org/licenses/by-sa/4.0
Automatic Measurement of Neutral Foot Posture Using Three-Dimensional Scanning
The Rearfoot Angle (RFA) is the most commonly method used for foot posture assessment, and it is also a method for evaluating the subtalar joint neutral foot posture. The purpose of this study implements the automatic computing of leg angle, foot angle, and RFA by the foot model using Three-Dimensional (3D) scanning. This study contributes to the algorithms to The purpose of this study implement the automatic and objective computing of leg angle, foot angle, and RFA by the foot model using 3D scanning. The automatic calculation on leg angle, foot angle, and neutral foot posture has been created, test, and validated completely in this study. There are two algorithm methods proposed to determine the midpoint on the leg or foot outline. The midline has been computed by linear regression through five midpoints. The leg and foot angles are calculated by the tibial and calcaneal midlines, respectively. Through the subject standing on eversion foot platforms, the neutral posture of the foot can be computed by the leg and foot angles determined by the 3D model scanned of the foot with various tilt angles. The determinations of the midpoint and midline have been demonstrated algorithm by MATLAB. Based on comparing with the goniometer measured, selecting the midpoint algorithm of the limit points and the lowest point methods to determine the tibial and calcaneal midlines respectively would carry out better results. The foot 3D scanning measurement proposed in this study has been tested and validated from the goniometer. This study can determine the leg angle, foot angle, and neutral foot postures for a subject with normal weight status, but be not suitable for that with obese weight status. In the future, this study can provide guidance for foot posture assessment and personal insoles design
Modelling, Simulation, and Stabilization of a Two-Wheeled Inverted Pendulum Robot Using Hybrid Fuzzy Control
Two wheels inverted pendulum robot has the same characteristics as inverted pendulum, which are unstable and nonlinear. Nonlinear systems can often be linearized by approximating them by a linear system obtained by expanding the nonlinear solution in a series, and then linear techniques can be used. Fuzzy logic control is the famous nonlinear controller that has been used by researchers to analyze the performance of a system due to the easiness to understand the nature of the controller. This research discusses about two wheels inverted pendulum robot design using hybrid fuzzy control. There are two types of fuzzy control, namely Fuzzy Balanced Standing Control (FBSC) to maintain stability and Fuzzy Traveling and Position Control (FTPC) to maintain position. Based on Takagi-Sugeno (T-S) fuzzy model on two wheels inverted pendulum robot, FBSC control used Parallel Distributed Compensation (PDC) with pole placement technic. Based on two wheels inverted pendulum robot movement characteristics, FTPC was designed using Mamdani Fuzzy architecture. FTPC control is used to help FBSC to maintain robot stability and to adjust to the desired position. Simulation result shows that controller for two wheels inverted pendulum robot can stabilize pendulum angle in 0 radian and close to the desired position.
Fuzzy T-S; Fuzzy Mamdani; Parallel Distributed; Compensation (PDC); Pole Placement ; Two Wheels Inverted; Pendulum
Effect of Closed and Opened the Door to Temperature on PID-Based Baby Incubator with Kangaroo Mode
The uneven distribution of the baby incubator temperature can cause the temperature in the baby incubator to be different at each point. The purpose of this study was to analyze the effect of the door closed and opened to the temperature at each point of sensor placement that has been determined. The study was conducted as experimental research design. In this experiment, an Incu Analyzer comparison was used as a calibrator unit, a baby skin temperature thermistor sensor, and four LM35 sensors for baby incubator room temperature with one LM35 sensor as a PID control system carried out by trial-and-error method. Based on the results of measurements was made with the design, when the chamber is open, it produces an average error value of T1 4.083%, T2 6.06%, T3 3.78%, T4 4.88%, and T5 1.48%, while when the chamber is closed, it produces an average error value T1 0.75. %, T2 0.88%, T3 1.15%, T4 0.74%, and T5 0.87%. Measurement of skin temperature using a thermometer has an average error value of 1.1%. The results showed that uneven heat transfer, lack of air distribution, different sensor placements at each point, and non-standard chamber sizes were factors that were uneven at each point. Based on the results of the study, it was found that the use of a working system on this device can be implemented to control the temperature of the baby incubator by knowing the temperature distribution at each point
LM35 Sensor, Temperature, Distance, Baby Incubator
Design a Monitoring Device for Heart-Attack Early Detection Based on Respiration Rate and Body Temperature Parameters
Acute myocardial infarction, commonly referred to as a heart attack, is the most common cause of sudden death where a monitoring tool is needed that is equipped with a system that can notify doctors to take immediate action. The purpose of this study was to design a heart attack detection device through indicators of vital human signs. The contribution of this research is that the system works in real-time, has more parameters, uses wireless, and is equipped with a system to detect indications of a heart attack. In order for wireless monitoring to be carried out in real-time and supported by a detection system, this design uses a radio frequency module as data transmission and uses a warning system that is used for detection. Respiration rate was measured using the piezoelectric sensor, and body temperature was measured using the DS18B20 temperature sensor. Processing of sensor data is done with ESP32, which is displayed wirelessly by the HC-12 module on the PC. If an indication of a heart attack is detected in the parameter value, the tool will activate a notification on the PC. In every indication of a heart attack, it was found that this design can provide notification properly. The results showed that the largest respiratory error value was 4%, and the largest body temperature error value was 0.55%. The results of this study can be implemented in patients who have been diagnosed with heart attack disease so that it can facilitate monitoring the patient's condition
Heart Attack; Respiration; Body Temperature; Wireless
Vital Sign Monitor Device Equipped with a Telegram Notifications Based on Internet of Thing Platform
Vital Sign Monitor is a tool used to diagnose a patient who needs intensive care to know the condition of the patient. Parameters used in monitoring the patient's condition include body temperature and respiration. The contribution of this research designed a vital sign monitoring tool with IoT-based notifications so that remote monitoring can be done by utilizing web Thinger.io, LCD, RGB LEDs as a display of the results of the study and notify telegrams if it becomes abnormal to the patient's condition. Therefore, in order to produce accurate data in the process of data retrieval, a relaxed position of the patient is required and the stability of the wi-fi network so that monitoring is not hampered. The study used the DS18B20 digital temperature sensor placed on the axilla and the piezoelectric sensor placed on the abdomen of the patient. The results of the study were obtained by taking data on patients. The resulting temperature value will be compared to the thermometer, which produces the highest error value of 0.56%, which is still possible because the tolerance limit is 1oC. and for the collection of respiration values that have been compared to the patient monitor obtained the highest error value of 6.2%, which is still feasible because the tolerance limit is 10%. In this study, there is often a crash library between the temperature sensor and other sensors, so for further research, recommend to replacing the temperature sensor
Vital Sign Monitor; Piezoelectric; DS18B20; Respiration Rate; Temperature
Central Monitor Based On Personal Computer Using Single Wireless Receiver
Central monitor is a tool that monitors the patient's condition with several devices into one display on a personal computer (PC). Pulse Oximetry serves to monitor the state of oxygen saturation in the patient's blood without going through blood gas analysis. This tool uses a wireless delivery system, HC-11 that can transmit data as far as 10 meters without obstruction. This tool uses a finger sensor, an analog signal conditioner and a microcontroller that is processed to produce a percentage value of SpO2 which is then sent through HC-11. The workings of this tool are very simple by entering the finger sensor on the finger and then it will be detected by the finger sensor that will be displayed on the PC. Digital data from the ADC Atmega is received by the personal computer (PC) via Bluetooth HC-011. Furthermore, the data is processed with the Delphi program and displayed on the monitor. Appears on the Delphi application. After measurement, get an error in the SpO2 parameter, the biggest error is 1.02% and get the smallest error 0.8%.
Copyright (c) 2019 https://creativecommons.org/licenses/by-sa/4.0
Measuring Respiration Rate Based on Android
Respiratory rate measurement tool is a technique used to determine the number of respiratory activities a person every minute. In the classification of the number of breathing per minute which can be divided into three groups, namely the so-called eupnea/normal, above average breathing called tachypnea, while below the average so-called bradypnea. This method is highly dependent on the concentration of the mind and senses actor sensitivity measurement and observation. Therefore, human nature is easy to forget, tired and bored, so now developed a method of measurement or observation of respiratory rate electronically. In this study, respiratory rate measurement making use flex sensor by placing the sensor in the patient's stomach and will detect the curvature of the patient's stomach. Results from the patient's respiratory displayed on the LCD Character and android using HC-05 Bluetooth as the media sender. The results of the measurement data of the 10 respondents indicated the average - average error of 3.2%. After testing and data collection can be concluded that the appliance is eligible to use because it is still within the tolerance range of 10%
Respiratory rate, Flex Sensor, HC-05 Bluetooth, Android
Digital Sphygmomanometer Based on Arduino Using TFT LCD Display
Sphygmomanometer is a human blood pressure measuring device which has several types such as mercury, digital, and aneroid. At this time, mercury Sphygmomanometer is not allowed to be used anymore considering the side effects of mercury which can harm the skin and even respiration. This study helps to reduce the use of mercury type Sphygmomanometer. The researchers make Arduino TFT LCD display based Sphygmomanometer so that the use of mercury can be reduced and make it easier for users to process measurement. The researchers used the oscillometry method in making the device to calculate the systolic and diastolic pressure. The result of blood pressure measurement has a systolic error rate of 0,08% and a diastolic error rate 0,09%.
Digital Sphygmomanometer, Arduino, TFT LCD, Oscillometri Method
Eye vision is a person's visual acuity examination that is usually done using a Snellen chart. The Snellen chart is a poster that contains a number of letters that are different on each line with the letters getting closer to the bottom lines. Eye vision examination using a normal Snellen chart is done at a distance of 6 meters because someone is considered normal if he is able to read on the 20/20 line in feet or 6/6 in meters. Snellen chart control android is an electronic Snellen chart that controls the light transfer using Android. Commands originating from the Mitapp application on android are sent to the Bluetooth HC-05 module in the min sys Atmega16 circuit. Atmega 16 was chosen because it has a sufficient number of pins to control the displacement of the lights used by 23 lights. The appearance of the MIT app application has been adjusted to the letters contained in the Snellen chart, making it easier for operator to correct the results of the readings that have been performed by the eye vision patient
Eye Vision; Snellen Chart; Atmega 16; Bluetooth HC-05; Android
A Centrifuge Calibrator Based on Personal Computer Equipped with Data Processor
Calibration is an activity to determine the conventional truth of the value of the appointment of a measuring instrument by comparing traceable standards to national and international standards for measurement or international units and certified reference materials. The purpose of this study is to develop a system of efficient and practical centrifuge calibrators by sending the calibration results directly via Bluetooth to a PC. The main series of centrifuge calibrators are Arduino modules, laser sensors, and Bluetooth. The high low signal is obtained from the reflection of the laser beam aimed at the reflector point on the centrifuge plate, processed in the Arduino module and displayed on the LCD, the calibration results can be directly seen in the Delphi program. The design of this module is also equipped with a Bluetooth transmitter to send data to a PC. This module can be used in medical equipment calibration laboratories. Based on the results of testing and data collection on the 8 Tube centrifuge with a Lutron Tachometer ratio, the error value was 0.0136%. After planning, experimenting, making modules, testing modules, and collecting data, it can be concluded that the tool "centrifuge calibrator equipped with PC-based data processors" can be used and according to planning because the fault tolerance does not exceed 10%.