{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T21:56:52Z","timestamp":1791669412785,"version":"4.3.4"},"reference-count":43,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2020,10,31]],"date-time":"2020-10-31T00:00:00Z","timestamp":1604102400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Wayfinding and navigation can present substantial challenges to visually impaired (VI) people. Some of the significant aspects of these challenges arise from the difficulty of knowing the location of a moving person with enough accuracy. Positioning and localization in indoor environments require unique solutions. Furthermore, positioning is one of the critical aspects of any navigation system that can assist a VI person with their independent movement. The other essential features of a typical indoor navigation system include pathfinding, obstacle avoidance, and capabilities for user interaction. This work focuses on the positioning of a VI person with enough precision for their use in indoor navigation. We aim to achieve this by utilizing only the capabilities of a typical smartphone. More specifically, our proposed approach is based on the use of the accelerometer, gyroscope, and magnetometer of a smartphone. We consider the indoor environment to be divided into microcells, with the vertex of each microcell being assigned two-dimensional local coordinates. A regression-based analysis is used to train a multilayer perceptron neural network to map the inertial sensor measurements to the coordinates of the vertex of the microcell corresponding to the position of the smartphone. In order to test our proposed solution, we used IPIN2016, a publicly-available multivariate dataset that divides the indoor environment into cells tagged with the inertial sensor data of a smartphone, in order to generate the training and validating sets. Our experiments show that our proposed approach can achieve a remarkable prediction accuracy of more than 94%, with a 0.65 m positioning error.<\/jats:p>","DOI":"10.3390\/s20216238","type":"journal-article","created":{"date-parts":[[2020,10,31]],"date-time":"2020-10-31T21:39:56Z","timestamp":1604180396000},"page":"6238","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Deep Learning-Based Positioning of Visually Impaired People in Indoor Environments"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0074-9786","authenticated-orcid":false,"given":"Payal","family":"Mahida","sequence":"first","affiliation":[{"name":"School of Computer, Data and Mathematical Sciences, Western Sydney University, Second Ave, Kingswood 2747, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seyed","family":"Shahrestani","sequence":"additional","affiliation":[{"name":"School of Computer, Data and Mathematical Sciences, Western Sydney University, Second Ave, Kingswood 2747, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hon","family":"Cheung","sequence":"additional","affiliation":[{"name":"School of Computer, Data and Mathematical Sciences, Western Sydney University, Second Ave, Kingswood 2747, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1347","DOI":"10.1109\/COMST.2016.2637663","article-title":"A Survey of Selected Indoor Positioning Methods for Smartphones","volume":"19","author":"Davidson","year":"2016","journal-title":"IEEE Commun. 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