Indoor Walking Trajectory Estimation Using Mobile Device Sensors for Hand-Held and Hand-Swinging Modes

We propose an indoor location estimation method using sensors of mobile devices. First, we perform attitude estimation using each sensor. This estimation is used to estimate the attitude of the mobile device with respect to the earth. Based on the acceleration and other information obtained from the attitude estimation, we then estimate the step detection, step length, and direction of the step. Finally, the location is calculated using all the estimation results. To eliminate the need to hold the mobile device in place during the estimation process, the method is configured so that estimates may be performed while walking, while looking at the screen, and while walking and holding the device in one hand. As the proposed method does not use indoor location fingerprinting or machine learning, real-time estimation can be performed. Although the accuracy could be higher, our experimental results show that the proposed method is able to effectively estimate the location and walking trajectory.