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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Teknokes
Sumber, Sumber
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Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science Health Professions Materials Science & Nanotechnology

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Automatic Blood Collection and Mixer in a Blood Transfusion System Equiped with Barrier Indicators Putra, Chandra Bimantara; Ariswati, Her Gumiwang; Sumber, Sumber; Zahar, Muzni
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 2 No. 2 (2020): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v2i2.225

Abstract

A blood collection monitor is a device used to measure and shake the blood bag during a blood transfusion so that the blood in the bag does not clot and is mixed with anticoagulant fluid in the bag properly. This study aims to design an automatic blood collection and mixer for the transfusion blood system. The advantage of the proposed design is accompanied by a safety system in the form of a barrier indicator that is connected to an alarm. The alarm served to give a warning to blood donors if there is an obstacle or there is no increase in volume as much as 20ml for 1 minute as recommended by the world blood bank association. This device can work with three different sizes of blood bags. In this study, a loadcell sensor is used to detect the amount of blood fluid that enters the bag. Furthermore, then it is converted into milliliter volume. In order to collect the blood, a shaker is drove using a motor controlled by Arduino microcontroller. From the measurement, for the entire size of the blood bag, we found that the deviation is 0, UA is 0, and the average error is 0. Thus, it can be concluded that this device can be used properly. In the future, it can be developed a blood infusion with the flowrate measurement to determine the speed of blood during donation
Development of Measuring Device for Non-Invasive Blood Sugar Levels Using Photodiode Sensor Dwi S, Frendi Agung; Utomo, Bedjo; Sumber, Sumber
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 2 No. 2 (2020): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v2i2.228

Abstract

Diabetes mellitus is one of the deadliest diseases faced by Indonesians. To measure blood sugar levels, the most widely used tool is an invasive tool, namely by injuring the patient's body. Techniques like this make people reluctant to measure glucose levels in their blood regularly. Therefore, this research aims to design and build a non-invasive blood sugar measuring device using a photodiode sensor. So that this tool can be used by all groups, both medical and non-medical personnel to measure blood sugar non-invasively. In this study, blood was drawn from several patients with Miletus diabetes and carried out direct blood measurements using a photodiode sensor. The results obtained from this study are that there is an error value in the voltage measurement circuit with the calculation of the resistance value to obtain the voltage value. The error value obtained is 5%, the linear regression value is 0.996. From the measurement results, it can be concluded that the photodiode sensor can be used to measure blood sugar non-invasively
Embedded Machine Learning on ESP32 for Upper-Limb Exoskeletons Based on EMG Triwiyanto, Triwiyanto; Maghfiroh, Anita Miftahul; Forra Wakidi, Levana; Dita Musvika, Syevana; Utomo, Bedjo; Sumber, Sumber; Nugraha, Priyambada Cahya; Caesarendra, Wahyu
Jurnal Teknokes Vol. 18 No. 4 (2025): Desember
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jteknokes.v18i4.134

Abstract

Stroke remains one of the primary causes of long-term disability worldwide and frequently results in persistent impairment of upper limb motor function. To support more effective and intensive rehabilitation, there is a need for wearable devices that can interpret muscle activity and autonomously assist limb movement without relying on an external computer. This study aims to design and implement an upper-limb rehabilitation exoskeleton that is driven by electromyography (EMG) signal classification using machine learning and by real-time elbow angle monitoring, with all models deployed directly on an ESP32 microcontroller. The proposed exoskeleton is built from lightweight, ergonomic 3D-printed components and operates in both unilateral and bilateral modes. Its main contributions include: (1) embedding real-time EMG classification models on the ESP32 so that the device can function independently, (2) integrating EMG-based motor control with elbow angle feedback from an MPU6050 inertial measurement unit, and (3) incorporating a load cell to estimate biceps force during training. EMG signals from the forearm flexor muscles are processed to extract statistical features such as variance (VAR), waveform length (WL), integrated EMG (IEMG), and root mean square (RMS). These features are used to train Random Forest, Decision Tree, Support Vector Machine (SVM), and XGBoost classifiers. The trained models are converted to C code using the micromlgen library for execution on the ESP32. System evaluation involved thirty male participants aged 20–25 years with body weights between 50–85 kg. All tested models achieved 100% accuracy in distinguishing relaxed versus grasping muscle contractions, while the correlation of elbow angles between unilateral and bilateral ESP32 systems reached 0.9469, indicating highly consistent motion detection. The Decision Tree model was selected for deployment due to its superior memory efficiency on the microcontroller. These results demonstrate that the developed ESP32-based exoskeleton provides a practical, efficient, and easily integrable solution for wearable stroke rehabilitation
Evaluating the effectiveness of digital filtering techniques in electronic stethoscopes: a study of Kalman and Butterworth filters Setioningsih, Endang Dian; Sumber, Sumber; Triwiyanto, Triwiyanto; Abdullayev, Vugar; Amrinsani, Farid; Raharjo, Bima Maulana; Fa’altin, Tetrik
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.8674

Abstract

Heart sound, or phonocardiogram (PCG), signals are often distorted by noise from respiration, movement, and the surrounding environment, which complicates accurate cardiac feature extraction in portable monitoring systems. This study aims to design and evaluate an effective digital filtering method to enhance PCG signal quality obtained from a low-cost acquisition system based on a condenser microphone sensor and an ESP32 microcontroller. The main contribution of this work is the implementation and comparison of two noise-reduction approaches: Butterworth band-pass filters of various orders and the Kalman filter applied to PCG signals acquired from mannequin-based simulations. Data were recorded for 10 seconds at a 1000 Hz sampling rate, processed in MATLAB, and analyzed using the fast Fourier transform (FFT) to determine the optimal frequency ranges. Experimental results demonstrate that the 8th-order Butterworth band-pass filter achieved the highest signal-to-noise ratio (SNR) improvement, averaging 25.659 dB, outperforming other configurations. These findings indicate that an appropriately tuned Butterworth filter provides a simpler yet robust solution for real-time PCG denoising in embedded systems. Future work will integrate the filtering process directly into the ESP32 firmware and evaluate its performance on human subjects to enhance clinical applicability.
Co-Authors Abdul Kholiq Abdullayev, Vugar Alhaq, Elmira Rofida Amrinsani, Farid Anita Miftahul Maghfiroh Ariswati, Her Gumiwang Assalim Tetra Putra, Moch. Prastawa Aulia, Farahun Nisa Dita Musvika, Syevana Dwi K, Rizky Dwi S, Frendi Agung Dyah Titisari, Dyah Fa’altin, Tetrik Febrianti, Fani Forra Wakidi, Levana Hamzah, Torib Hamzah, Torib Hamzah Luthfiyah, Sari Mak’ruf, Muhammad Ridha Muzachim, Zuva Nugraha, Priyambada C. Nugraha, Priyambada Cahya Pudji, Andjar Putra, Chandra Bimantara Rabani, Rifqi Ragimova, Nazila Raharjo, Bima Maulana Rathod, Yagnik RATNA TAN Setioningsih, Endang Dian Sintiyah, Elisa Ayu Syaifudin , Syaifudin Ullah, Naqeeb Utomo, Bedjo Wahyu Caesarendra Wardhana, Farisy Azis Satria Yudha Setiawan, Singgih Zahar, Muzni