{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T03:35:53Z","timestamp":1771472153026,"version":"3.50.1"},"reference-count":68,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,5,28]],"date-time":"2021-05-28T00:00:00Z","timestamp":1622160000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Pedestrian dead reckoning (PDR), enabled by smartphones\u2019 embedded inertial sensors, is widely applied as a type of indoor positioning system (IPS). However, traditional PDR faces two challenges to improve its accuracy: lack of robustness for different PDR-related human activities and positioning error accumulation over elapsed time. To cope with these issues, we propose a novel adaptive human activity-aided PDR (HAA-PDR) IPS that consists of two main parts, human activity recognition (HAR) and PDR optimization. (1) For HAR, eight different locomotion-related activities are divided into two classes: steady-heading activities (ascending\/descending stairs, stationary, normal walking, stationary stepping, and lateral walking) and non-steady-heading activities (door opening and turning). A hierarchical combination of a support vector machine (SVM) and decision tree (DT) is used to recognize steady-heading activities. An autoencoder-based deep neural network (DNN) and a heading range-based method to recognize door opening and turning, respectively. The overall HAR accuracy is over 98.44%. (2) For optimization methods, a process automatically sets the parameters of the PDR differently for different activities to enhance step counting and step length estimation. Furthermore, a method of trajectory optimization mitigates PDR error accumulation utilizing the non-steady-heading activities. We divided the trajectory into small segments and reconstructed it after targeted optimization of each segment. Our method does not use any a priori knowledge of the building layout, plan, or map. Finally, the mean positioning error of our HAA-PDR in a multilevel building is 1.79 m, which is a significant improvement in accuracy compared with a baseline state-of-the-art PDR system.<\/jats:p>","DOI":"10.3390\/rs13112137","type":"journal-article","created":{"date-parts":[[2021,5,31]],"date-time":"2021-05-31T03:45:29Z","timestamp":1622432729000},"page":"2137","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["An Adaptive Human Activity-Aided Hand-Held Smartphone-Based Pedestrian Dead Reckoning Positioning System"],"prefix":"10.3390","volume":"13","author":[{"given":"Bang","family":"Wu","sequence":"first","affiliation":[{"name":"Electronic Engineering and Computer Science Department, Queen Mary University of London, Mile End Road, London E1 4NS, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9146-4470","authenticated-orcid":false,"given":"Chengqi","family":"Ma","sequence":"additional","affiliation":[{"name":"Electronic and Electrical Engineering Department, University College London (UCL), Torrington Place, London WC1E 7JE, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3156-9609","authenticated-orcid":false,"given":"Stefan","family":"Poslad","sequence":"additional","affiliation":[{"name":"Electronic Engineering and Computer Science Department, Queen Mary University of London, Mile End Road, London E1 4NS, UK"}]},{"given":"David R.","family":"Selviah","sequence":"additional","affiliation":[{"name":"Electronic and Electrical Engineering Department, University College London (UCL), Torrington Place, London WC1E 7JE, UK"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1406","DOI":"10.1109\/JIOT.2020.3011402","article-title":"A self-adaptive ap selection algorithm based on multi-objective optimization for indoor wifi positioning","volume":"8","author":"Zhang","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Li, Z., Liu, C., Gao, J., and Li, X. 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