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Traditional navigation methods mainly support two-dimensional stable motion modes such as walking; special tasks such as rescue and disaster relief, medical search and rescue, in addition to normal walking, are usually accompanied by running, going upstairs, going downstairs and other motion modes, which will greatly affect the dynamic performance of the traditional zero-velocity update algorithm. Based on a wearable multi-node inertial sensor network, this paper presents a method of multi-motion modes recognition for indoor pedestrians based on gait segmentation and a long short-term memory artificial neural network, which improves the accuracy of multi-motion modes recognition. In view of the short effective interval of zero-velocity updates in motion modes with fast speeds such as running, different zero-velocity update detection algorithms and integrated navigation methods based on change of waist\/foot headings are designed. The experimental results show that the overall recognition rate of the proposed method is 96.77%, and the navigation error is 1.26% of the total distance of the proposed method, which has good application prospects.<\/jats:p>","DOI":"10.3390\/s22135022","type":"journal-article","created":{"date-parts":[[2022,7,4]],"date-time":"2022-07-04T23:38:55Z","timestamp":1656977935000},"page":"5022","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Method for Autonomous Multi-Motion Modes Recognition and Navigation Optimization for Indoor Pedestrian"],"prefix":"10.3390","volume":"22","author":[{"given":"Zhengchun","family":"Wang","sequence":"first","affiliation":[{"name":"College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China"},{"name":"Navigation Research Center, School of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhi","family":"Xiong","sequence":"additional","affiliation":[{"name":"College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China"},{"name":"Navigation Research Center, School of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Xing","sequence":"additional","affiliation":[{"name":"School of Railway Transportation, Shanghai Institute of Technology, Shanghai 201418, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiming","family":"Ding","sequence":"additional","affiliation":[{"name":"College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China"},{"name":"Navigation Research Center, School of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yinshou","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China"},{"name":"Navigation Research Center, School of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1134\/S2075108716010089","article-title":"The Pedestrian Integrated Navigation System with micro IMU\/GPS\/magnetometer\/barometric altimeter","volume":"7","author":"Liu","year":"2016","journal-title":"Gyroscopy Navig."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3116","DOI":"10.1109\/JSEN.2017.2689802","article-title":"Performance Enhancement of MEMS-Based INS\/UWB Integration for Indoor Navigation Applications","volume":"17","author":"Fan","year":"2017","journal-title":"IEEE Sens. 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