Publications
カンファレンス (国際) Estimation of Precise Step Lengths by Segmenting the Motion of Individual Steps
Yuichi Ikeuchi (Ritsumeikan University), Daichi Yoshikawa (Ritsumeikan University), Kota Tsubouchi, Nobuhiko Nishio (Ritsumeikan University)
2022 International Conference on Indoor Positioning and Indoor Navigation (IPIN2022)
2022.9.7
Pedestrian dead reckoning (PDR) is one of the representative methods of indoor positioning, and many researchers have developed PDR methodologies that can accurately estimate step lengths of users. We propose a novel method to improve the accuracy of the step-length estimation by dividing up a gait into finer movements than a single step and extracting features. To our knowledge, no other studies have attempted to improve precision by breaking down the motion of a single step. This is not only in the step-length estimation, but also in machine learning studies for PDR. We propose two methods of breaking down a step: one is to segment the step data at the extreme values of the norm of the acceleration vectors, and the other is to segment the data so that the extreme values are at the middle points of the segmentation. The step length is estimated by extracting features from each segment. The results of an evaluation shows that these methods improved accuracy by 10% over the compared method.