Sistem Monitoring Electric-Powered Wheelchair (Epw) Berbasis Sensor Accelerometer Terintegrasi Kalman Filter Dan Auto- Encoder Machine Learning - Dewaruci, Jalasecca Wya; Widhiada, I Wayan; Dwijana, I Gusti Komang (Teknik Desain Mekanika, 2024)
Abstract In their daily lives, people with disabilities require a wheelchair as a mobility aid. Over time and with technological advancements, the improvement of wheelchairs for people with disabilities has not only focused on quantity but also on enhancing functionality. The development of control systems includes the use of direct joystick-based controls and the modification of manual wheelchair propulsion systems to electric. The use of Electric Powered Wheelchairs (EPW) needs to be supervised by a third party to ensure the safety of people with disabilities. However, in certain conditions, individuals with disabilities require personal space, and continuous direct supervision by a third party may not be feasible Monitoring systems can assist in supervising people with disabilities, but the accuracy of signals from the accelerometer sensor, the foundation of the monitoring system, needs careful consideration. This can be addressed by integrating Kalman Filter and Autoencoder algorithms. To evaluate these algorithms in this study, EPW testing was conducted on track patterns 1 and 2. The obtained accuracy values were (1) without integrating Kalman Filter and Autoencoder Machine Learning, 16.84%, (2) with Kalman Filter integration, 93.06%, (3) with Autoencoder Machine Learning integration, 95.59%, and (4) with Kalman Filter and Autoencoder Machine Learning integration, 98%. This indicates that the algorithms used to reduce noise from the output data of the accelerometer sensor have good capabilities in reducing noise from the accelerometer sensor's output data. Keywords: electric powered wheelchair, monitoring system, sensor MPU6050, kalman filter, autoencoder