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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Chai, Rifai
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Computer Science & IT Electrical & Electronics Engineering

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Measuring anxiety level on phobia using electrodermal activity, electrocardiogram and respiratory signals Ain, Khusnul; Rahma, Osmalina Nur; Purwanti, Endah; Varyan, Richa; Ittaqilah, Sayyidul Istighfar; Arfensia, Danny Sanjaya; Sosialita, Tiara Dyah; Qulub, Fitriyatul; Chai, Rifai
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp337-348

Abstract

People with spider phobia experience excessive anxiety reactions when exposed to spiders that will interfere with daily life. Diagnosing and measuring anxiety levels in patients with spider phobia is a complex challenge. Conventional diagnosis requires psychological evaluations and clinical interviews that take time and often result in a high degree of subjectivity. Therefore, there is a need for a more objective and efficient approach to measuring anxiety levels in patients. This study performs anxiety level classification based on electrodermal activity, electrocardiogram (ECG) and respiratory signals using the dataset of Arachnophobia subjects. Each raw data is preprocessed using 24 types of features. Feature performance is processed using the recursive feature elimination method. Data processing was performed in 3 anxiety levels (high, medium, low) and two anxiety levels (high, low) with the support vector machine method and hold-out validation method (7:3). The performance of the model is evaluated by showing the accuracy, precision, recall and F1 score values. The polynomial kernel can perform optimal classification and obtain 100% accuracy in 2 classes and three classes with 100% precision, recall, and F1 score values. This result shows excellent potential in measuring anxiety levels that correlate with mental health issues.
Online PID-neural network for tracking lower limb rehabilitation exoskeleton angular position Hanifah, Ummi; Adinda, Aura; Rahmatillah, Akif; Sapuan, Imam; Ain, Khusnul; Septanto, Harry; Chai, Rifai
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9395

Abstract

Gait trajectory tracking control is an essential component of a lower limb rehabilitation exoskeleton (LLRE). Meanwhile, the proportional-integral-derivative (PID) controller remains popular for a variety of applications, including LLRE. Nonetheless, employing PID presents a significant issue, namely determining how to choose or tune the parameters. This paper addresses the LLRE’s hipknee angular position tracking system based on an online PID-NN controller, i.e., a PID controller, whose parameters are online modified by a trained neural network (NN). A proposed framework for designing the PID-NN controller is elaborated. Numerical verifications are carried out by comparing the performance of the PID-based control system, whose parameters have been tuned using Ziegler-Nichols (ZN), without and using NN. Performance comparisons involving the presence of external disturbance are also carried out. The simulation results show that the proposed PID-NN-based control system provides better performance with lower mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) values.
Co-Authors Adinda, Aura Arfensia, Danny Sanjaya Fitriyatul Qulub Imam Sapuan Ittaqilah, Sayyidul Istighfar Khusnul Ain Osmalina Nur Rahma Rahmatillah, Akif Septanto, Harry Solly Aryza Sosialita, Tiara Dyah Ummi Hanifah Varyan, Richa