Algorithm for 3D orientation estimation based on Kalman Filter and Gradient Descent

Individual wearable device orientation plays a crucial role in rehabilitation application, especially in the process of monitoring dynamic changes of movement for clinical prognosis. This paper proposed a combined approach to fuse Inertial Measurement Unit (IMU) sensors via adaptive Kalman Filter considering practical recommendation of error covariance. Our approach associates orientation estimate using gyroscope via Kalman Filter with optimal instant attitude estimate of accelerometer and magnetometer using Gradient Descent. Experiments were carried out to compare our proposed approach with well-known approaches such as Kalman filter, Tilt Kalman filter, and Madgwick algorithm. The results show that the proposed method displayed accurate and precise measurements, especially for internal/external rotation (z axis orientation), in real time.