Smartwatch-Based Kinematic Walking Direction Estimation Using Paired Principal Component Analysis

The dynamic behavior of pedestrians causes a misalignment problem between the sensor orientation and the walking direction, which hinders the performance of pedestrian dead reckoning (PDR) systems. Pedestrians wearing smartwatches are constantly faced with this problem when running. In this paper, we propose a novel kinematic modeling of arm swing that segments arm swing motion from the movement of the center of mass of the body. The proposed decomposition method allows for effective negation of the sensor outputs due to the redundant motion that obstructs the estimation of the true walking direction. The correct direction vector is computed by deducting the direction vector of the arm swing from that of the entire motion, which are both derived from performing two separate principal component analyses (PCA). The performance of the proposed method was evaluated through several experiments. In the running track experiment, the proposed method demonstrates the best performance, with 57% – 70% performance improvement compared to the existing methods. In the general scenario involving both walking and running, the proposed method outperforms the baseline method by 56%, improving the generality of the PCA-based methods.