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All Journal TELEKONTRAN: Jurnal Ilmiah Telekomunikasi, Kendali dan Elektronika Terapan Indonesian Journal of Electrical Engineering and Computer Science
Robby Ikhfa Nulfatwa
Universitas Pendidikan Indonesia
Computer Science & IT Control & Systems Engineering

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Smart Asissistive Device: Alat Bantu Komunikasi Pasien Stroke Berat Dengan Gesture Recognition Berbasis Internet Of Things Muhammad Fadli Nasution; Robby Ikhfa Nulfatwa; Rahillah Nur Maryam; Fadhil Muhammad Iqbal; Raihan Yusuf Rifansyah; Agus Heri Setya Budi
Telekontran : Jurnal Ilmiah Telekomunikasi, Kendali dan Elektronika Terapan Vol 10 No 2 (2022): TELEKONTRAN vol 10 no 2 Oktober 2022
Publisher : Program Studi Teknik Elektro, Fakultas Teknik dan Ilmu Komputer, Universitas Komputer Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/telekontran.v10i2.9181

Abstract

Stroke is the highest cause of disability in the world. Most post-stroke patients experience a decrease in motor function and make the patient experience difficulties in carrying out daily activities so that they need assistance from a companion. As a result of dysartia experienced by patients, companions need to be on guard all the time by the patient's side to anticipate the patient's needs. This makes patient companions have limitations in carrying out their daily activities. Based on these problems, an innovative communication tool for post-severe stroke patients was created with gesture recognition based on the Internet of Things. This tool can assist patients in communicating their needs appropriately while at the same time making patient companions remain productive because there is no need to stand guard accompanying patients as long as they are not needed. The research method used is a planned experiment beginning with problem identification, literature study, product design, system integration, product verification and validation. Based on data analysis, the system is functioning properly, successfully sending daily activity notifications to the accompanying Android application when the patient wants to move. This tool is very useful for post-stroke patients as a tool to communicate their needs, and is also useful for companions so that time for caring for patients is not wasted. For further development, this tool can adjust the patient's motoric abilities that are still functioning, such as head movements, facial expressions, etc., as well as adding various types of activities to the tool.
Cumulative error correction of inertial navigation systems using LIDAR sensors and extended Kalman filter Silmi Ath Thahirah Al Azhima; Dadang Lukman Hakim; Robby Ikhfa Nulfatwa; Nurul Fahmi Arief Hakim; Mariya Al Qibtiya
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp878-887

Abstract

Autonomous robots have gained significant attention in research due to their ability to facilitate human work. Navigation systems, particularly localization, present a challenge in autonomous robots. The inertial navigation system is a localization system that uses inertial sensors and a wheel odometer to estimate the robot’s relative position to the initial position. However, the system is susceptible to continuous error accumulation over time due to factors like sensor noise and wheel slip. To address these issues, external sensors are required to measure the robot’s position in the environment. The extended Kalman filter (EKF) method is utilized to estimate the robot’s position based on wheel odometer and light detection and ranging (LIDAR) sensor measurements. In the prediction stage, the input to the EKF is the position measurement from the wheel odometer, while the LIDAR sensor’s position measurement is used in the update stage to improve the prediction stage results. The test results reveal that the EKF’s estimated position has a lower average error compared to the position measurement using the wheel odometer. Therefore, it can be concluded that the EKF technique is effectively applied to the robot and can correct the wheel odometer's cumulative error with the assistance of the LIDAR sensor.
Co-Authors Budi, Agus Heri Setya Dadang Lukman Hakim Fadhil Muhammad Iqbal Mariya Al Qibtiya Muhammad Fadli Nasution Nurul Fahmi Arief Hakim Rahillah Nur Maryam Raihan Yusuf Rifansyah Silmi Ath Thahirah Al Azhima