Bike Fitting System Based on Digital Image Processing on Road Bike - Nasution, Tigor Hamonangan; Sitohang, Andreas; Seniman, Seniman; Soeharwinto, Soeharwinto (JOIV : International Journal on Informatics Visualization, 2025)
This research aims to develop a bike fitting system based on digital image processing for road bikes. The method used in this study involves using the OpenCV and MediaPipe libraries in the Python programming language to detect the rider's body pose from a video stream captured using a webcam. The body pose data is then used to calculate important angles such as elbow, hip, knee, and ankle range related to the correct riding position for road bikes. In this research, a comparison is made between the body angles obtained and the angle range considered ideal for bike fitting on road bikes. If the body angles fall within the desired range, the system will label it as "Fit”; if the body angles are outside the selected range, the system will label it as "Not Fit." The results of this study indicate that the bike fitting system based on digital image processing using a webcam can provide helpful visual feedback in improving the rider's body position for road bikes. By observing the body angles produced and seeing the "Fit" or "Not Fit" label, cyclists can adjust their position to match the ideal position in bike fitting. The system test results show a low error rate, with elbow angle having an average error of 0.81%, hip angle of 1.37%, knee angle of 0.83%, and ankle range of 1.76%. Thus, this research contributes significantly to supporting cyclists in achieving a position appropriate to their inseam height.