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All Journal Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Teknokes Indonesian Journal of electronics, electromedical engineering, and medical informatics ELECTRA : Electrical Engineering Articles International Journal of Advanced Health Science and Technology Frontiers in Community Service and Empowerment Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Teknokes
Anita Miftahul Maghfiroh
Department Of Medical Electronics Technology, Poltekkes Kemenkes Surabaya, Surabaya, Indonesia
Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Materials Science & Nanotechnology Nursing Public Health Other

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Digital Filter Design to Reduce Motion Artifacts in Electrocardiogram Signals Based on IIR Filter Maghfiroh, Anita Miftahul; Setiawan, Singgih Yudha; Mujahid, Muhammad Umer Farooq
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 5 No. 4 (2023): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v5i4.187

Abstract

Developed a new method to overcome motion artifacts in Electrocardiogram (ECG) signals, which often interfere with accurate clinical analysis. Motion artifacts, such as body movements, can cause significant distortions in the ECG signal, resulting in incorrect interpretation and affecting medical diagnosis. The main objective of this research is to design and implement an infinite impulse response (IIR) filter with a predetermined sequence, namely orders 2, 4, 6, and 8 to reduce motion artifacts in the ECG signal. We aim to improve ECG signal quality by preserving important ECG signal information and reducing noise caused by motion artifacts. This research contributes to developing more precise and reliable ECG signal processing techniques. The proposed method provides an effective approach to handling motion artifacts, enabling more accurate and reliable ECG interpretation by medical professionals. We used an ECG simulator that provides body movement simulation as a basis for experiments. The detected ECG signal is processed with a predetermined order IIR filter. We compare the filtered signal to the original signal to measure the effectiveness of reducing motion artifacts. Experimental results show that the applied IIR filter efficiently reduces motion artifacts in the ECG signal. The SNR assessment showed a significant improvement, proving the success of this method in maintaining ECG signal quality. The result is that in the 2nd order, the SNR value is 22.25 dB, in the 4th order the SNR value is 22.75 dB, in the 6th order the SNR value is 22.99 dB, in the 6th order the SNR value is 22.99 dB. 8 obtained an SNR value of 23dB. This study successfully demonstrated that using IIR filters in a specified order effectively reduces motion artifacts in the ECG signal, increases SNR, and maintains the integrity of clinical information in the ECG signal.
Design an Infusion Device Analyzer with Flow Rate Parameters using High Sensitive Photodiode Sensor Pudji, Andjar; Maghfiroh, Anita Miftahul; Thongpance, Nuntachai
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 3 No. 2 (2021): May
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v3i2.201

Abstract

Infusion devices are the basis for primary health care, that is to provide medicine, nutrition, and hydration to patients. One of the infusion devices is a syringe pump and an infusion pump. This device is very important to assist the volume and flow that enters the patient's body, especially in situations related to neonatology or cancer treatment. Therefore, a comparison tool is needed to see whether the equipment is used or not. The purpose of this research is to make an infusion device analyzer (IDA) design with a flow rate parameter. The contribution of this research is that the tool can calculate the correct value of the flow rate that comes out of the infusion pump and syringe pump. The water released by the infusion pump or syringe pump will be converted into droplets which are then detected by the sensor. This tool uses an infrared sensor and a photodiode. The results obtained by the sensor will come by Arduino nano and code it to the 16x2 Character Liquid Crystal Display (LCD) and can be stored on an SD Card so that it can be analyzed further. In setting the flow rate for the syringe pump of 100 mL / hour, the error value is 3.9, 50 ml / hour 0.02, 20 mL / hour 0.378, 10 mL / hour 0.048, and 5 mL / hour 0.01. The results show that the average error of the syringe pump performance read by the module is 0.87. The results obtained from this study can be implemented for the calibration of the infusion pump and the syringe pump so that it can be determined whether the device is suitable or not
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; 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
Co-Authors Abdullayev, Vugar Achmad Rizal Achmad Rizal Andjar Pudji Ariswati, Her Gumiwang Bambang Guruh Irianto Bayu Trisono Aji Pambudi C. N, Priyambada Dita Musvika, Syevana Dyah Titisari Dyah Titisari, Dyah Endro Yulianto Farid Amrinsani Farisy Azis Satria Wardhana Forra Wakidi, Levana Hidayanti, Nuril Huynh, Phuoc-Hai I Dewa Hari Wisana Levana Forra Wakidi Liliek Soetjiatie Mak’ruf, Muhammad Ridha Maulani, Salsabilla Kusuma Mujahid, Muhammad Umer Farooq Musvika, Syevana Dita Nina Havilda Nuntachai Thongpance Nuril Hidayanti Pudji, Andjar Rafi Maulana Firmansyah Sari, Alvy Noorlatifa Septiana, Silvi Dwi Setiawan, Singgih Yudha Shubhrojit Misra Sima Das Singgih Yudha Setiawan Steyve Nyatte Sumber Sumber Sumber, Sumber Susilo, Dody Syaifudin Syaifudin Syaifudin, Syaifudin Syevana Dita Musvika T.P., Moch. Prastawa Assalim Thongpance, Nuntachai Triwiyanto , Triwiyanto Triwiyanto Triwiyanto Utomo, Bedjo Vijay Kumar Sharma Vugar Abdullayev Wahyu Caesarendra Wakidi, Levana Forra Wardhana, Farisy Azis Satria Yudha Setiawan, Singgih