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All Journal Jurnal Ilmu Dasar Mechatronics, Electrical Power, and Vehicular Technology
Sasono, Muchamad Arif Hana
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Control & Systems Engineering Electrical & Electronics Engineering Mathematics

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Navigation System of Electric Car with Faster R-CNN for Pediatric Patient Transportation Qoyima, Rosida Amalia Nurul; Kaloko, Bambang Sri; Anam, Khairul; Sasono, Muchamad Arif Hana; Efendi, Dicky Yusril
Jurnal ILMU DASAR Vol 26 No 1 (2025)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/jid.v26i1.50186

Abstract

The use of electric cars as a means of transportation for pediatric patients has the main purpose of having a positive effect on the psychology of pediatric patients before surgery. Therefore, it is expected to accelerate the healing process. An electric car navigation system that can recognize the environment is needed. This article aims to develop a camera-based semi-autonomous navigation system using the faster R-CNN method to detect markers as electric car direction. This method optimizes the range of interest (RoI) layer to produce optimal features. Faster R-CNN is faster in generating accurate region proposals compared to R-CNN and Fast R-CNN. Various Faster R-CNN models were tested in image data processing for marker detection as the electric car steering system. Test results on FPS variations show that the best results were obtained when using the Faster R-CNN MobileNet V3 Large 320 FPN model with a value of 11.3f ps for the forward marker, 18.9 fps for the stop marker, 22.6 fps for the left turn marker and 11.1 fps for the right turn marker. With this model, the results obtained are quite good in testing the performance of the car navigation system. The results obtained in the success of the test are 70% for the forward marker test, 100% for the stop marker test, 90% for the left turn marker and 100% for the right turn marker.
Electric wheelchair navigation based on hand gestures prediction using the k-Nearest Neighbor method Anam, Khairul; Nahela, Safri; Sasono, Muchamad Arif Hana; Rizal, Naufal Ainur; Putra, Aviq Nurdiansyah; Wahono, Bambang; Putrasari, Yanuandri; Wardana, Muhammad Khristamto Aditya; Salim, Taufik Ibnu
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 16, No 1 (2025)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/j.mev.2025.1229

Abstract

The advancement of technology in the medical field has led to innovations in assistive devices, including wheelchairs, to enhance the mobility and independence of individuals with disabilities. This study investigates the use of electromyography (EMG) signals from hand muscles to control a wheelchair using the k-Nearest Neighbor (kNN) classification method. kNN is a classification algorithm that identifies objects based on the proximity of similar objects in the feature space. The wheelchair control process begins with the development of a kNN model trained on EMG signal data collected from five respondents over 30 seconds. The data was processed using feature extraction techniques, namely Mean Absolute Value (MAV) and Root Mean Square (RMS), to identify motion characteristics corresponding to five types of movement: forward, backward, right, left, and stop. The extracted features were classified using the kNN algorithm implemented on a Raspberry Pi 3. The classification results were then used to control the wheelchair through an Arduino UNO microcontroller connected to a BTS7960 motor driver. The study achieved an average accuracy of 96% with the MAV feature and ? = 3. Furthermore, combining MAV and RMS features significantly improved classification accuracy. The highest accuracy was obtained using the combination of MAV and RMS features with ? = 3, demonstrating the effectiveness of feature selection and parameter tuning in enhancing the system's performance.
Co-Authors Anam, Khairul Anam, M. Khairul Bambang Sri Kaloko Bambang Wahono Efendi, Dicky Yusril Nahela, Safri Putra, Aviq Nurdiansyah Qoyima, Rosida Amalia Nurul Rizal, Naufal Ainur Salim, Taufik Ibnu Wardana, Muhammad Khristamto Aditya Yanuandri Putrasari