Real-time orientation estimation for archery bow stability using a low-cost IMU and Kalman filter

Accurate assessment of bow stability is essential for objective performance evaluation in archery training. However, real-time orientation estimation using low-cost inertial sensors remains challenging due to measurement noise and gyroscope drift. This study presents a low-cost embedded orientation estimation system that combines an MPU9250 Inertial Measurement Unit (IMU) with a discrete Kalman Filter to improve the accuracy and stability of bow orientation measurements. The system was implemented on an Arduino Nano microcontroller and experimentally validated under controlled static reference conditions at three predefined orientations (0°, 45°, and 90°). Performance was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and standard deviation to quantify estimation accuracy and measurement consistency. The proposed system achieved MAE values of 0.12° and 0.15°, with corresponding RMSE values of 0.18° and 0.22° for the Pitch and Roll axes, respectively, demonstrating substantial improvement over unfiltered measurements. The Kalman Filter effectively suppressed high-frequency measurement noise while preserving stable orientation estimates suitable for real-time embedded implementation. Although the current validation was limited to static reference conditions, the proposed system provides a practical and cost-effective solution for real-time orientation estimation and bow stability monitoring, offering a promising platform for future dynamic performance analysis in archery and other precision sports.