Catheter associated urinary tract infections (CAUTI) are among the most common nonpayment hospital acquired conditions. Inexperienced health care providers placing indwelling urinary catheters are associated with an increased risk of CAUTI. The creation of high-fidelity simulators may reduce CAUTI risk during critical early learning. As a first step toward the creation of accurate simulators our group set out to characterize the mechanical aspects of urethral catheterization. This work presents an inexpensive, yet practical means of acquiring motion and force data from urethral catheter insertion procedures using OpenCV ArUco markers. Evaluation of the video system’s accuracy was done to understand the performance characteristics within the boundaries of the procedure’s target workspace. The tracking accuracy was validated to be roughly ± 3 mm in the plane of the camera, and ± 10–25 mm along its axis depending on the distance. Feasibility of using this platform in a clinically relevant setting was demonstrated by capturing the force and motion data when performing urinary catheterization on cadaveric donors (N=2).