{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T15:40:41Z","timestamp":1780501241543,"version":"3.54.1"},"reference-count":52,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2019,2,18]],"date-time":"2019-02-18T00:00:00Z","timestamp":1550448000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program","doi-asserted-by":"publisher","award":["2018YFB0505200"],"award-info":[{"award-number":["2018YFB0505200"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the BUPT Excellent Ph.D. Students Foundation","award":["CX2018102"],"award-info":[{"award-number":["CX2018102"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61872046, 61671264 and 61671077"],"award-info":[{"award-number":["61872046, 61671264 and 61671077"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Accurate stride-length estimation is a fundamental component in numerous applications, such as pedestrian dead reckoning, gait analysis, and human activity recognition. The existing stride-length estimation algorithms work relatively well in cases of walking a straight line at normal speed, but their error overgrows in complex scenes. Inaccurate walking-distance estimation leads to huge accumulative positioning errors of pedestrian dead reckoning. This paper proposes TapeLine, an adaptive stride-length estimation algorithm that automatically estimates a pedestrian\u2019s stride-length and walking-distance using the low-cost inertial-sensor embedded in a smartphone. TapeLine consists of a Long Short-Term Memory module and Denoising Autoencoders that aim to sanitize the noise in raw inertial-sensor data. In addition to accelerometer and gyroscope readings during stride interval, extracted higher-level features based on excellent early studies were also fed to proposed network model for stride-length estimation. To train the model and evaluate its performance, we designed a platform to collect inertial-sensor measurements from a smartphone as training data, pedestrian step events, actual stride-length, and cumulative walking-distance from a foot-mounted inertial navigation system module as training labels at the same time. We conducted elaborate experiments to verify the performance of the proposed algorithm and compared it with the state-of-the-art SLE algorithms. The experimental results demonstrated that the proposed algorithm outperformed the existing methods and achieves good estimation accuracy, with a stride-length error rate of 4.63% and a walking-distance error rate of 1.43% using inertial-sensor embedded in smartphone without depending on any additional infrastructure or pre-collected database when a pedestrian is walking in both indoor and outdoor complex environments (stairs, spiral stairs, escalators and elevators) with natural motion patterns (fast walking, normal walking, slow walking, running, jumping).<\/jats:p>","DOI":"10.3390\/s19040840","type":"journal-article","created":{"date-parts":[[2019,2,19]],"date-time":"2019-02-19T04:08:20Z","timestamp":1550549300000},"page":"840","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":83,"title":["Pedestrian Stride-Length Estimation Based on LSTM and Denoising Autoencoders"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6551-6807","authenticated-orcid":false,"given":"Qu","family":"Wang","sequence":"first","affiliation":[{"name":"School of Information and Communication Engineering, Beijing University of Posts and Telecommunication, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Langlang","family":"Ye","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6827-4225","authenticated-orcid":false,"given":"Haiyong","family":"Luo","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aidong","family":"Men","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, Beijing University of Posts and Telecommunication, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fang","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Beijing University of Posts and Telecommunication, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Huang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Advanced Optical Communication Systems and Networks, Peking University, Beijing 100871, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1109\/JSAC.2015.2430274","article-title":"Magicol: Indoor Localization Using Pervasive Magnetic Field and Opportunistic WiFi Sensing","volume":"33","author":"Shu","year":"2015","journal-title":"IEEE J. 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