Novel noniterative orientation estimation for wearable motion sensor units acquiring accelerometer, gyroscope, and magnetometer measurements

We propose a novel noniterative orientation estimation method based on the physical and geometrical properties of the acceleration, angular rate, and magnetic field vectors to estimate the orientation of motion sensor units. The proposed algorithm aims that the vertical (up) axis of the earth coordinate frame is as close as possible to the measured acceleration vector and that the north axis of the earth makes an angle with the detected magnetic field vector as close as possible to the estimated value of the magnetic dip angle. We obtain the sensor unit orientation based on the rotational quaternion transformation between the earth and the sensor unit frames. We evaluate the proposed method by incorporating it into an activity recognition scheme for daily and sports activities, which requires accurately estimated sensor unit orientations to achieve invariance to the orientations at which the units are worn on the body. Using four different classifiers on a publicly available data set, the proposed methodology achieves an average activity recognition accuracy higher than the state-of-the-art methods, as well as being computationally efficient enough to be executed in real time.