{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T16:13:19Z","timestamp":1772554399004,"version":"3.50.1"},"reference-count":21,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2023,8,16]],"date-time":"2023-08-16T00:00:00Z","timestamp":1692144000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and Technology, Taiwan ROC","award":["111-2811-E-011-014-MY3"],"award-info":[{"award-number":["111-2811-E-011-014-MY3"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The gait pattern of exoskeleton control conflicting with the human operator\u2019s (the pilot) intention may cause awkward maneuvering or even injury. Therefore, it has been the focus of many studies to help decide the proper gait operation. However, the timing for the recognization plays a crucial role in the operation. The delayed detection of the pilot\u2019s intent can be equally undesirable to the exoskeleton operation. Instead of recognizing the motion, this study examines the possibility of identifying the transition between gaits to achieve in-time detection. This study used the data from IMU sensors for future mobile applications. Furthermore, we tested using two machine learning networks: a linearfFeedforward neural network and a long short-term memory network. The gait data are from five subjects for training and testing. The study results show that: 1. The network can successfully separate the transition period from the motion periods. 2. The detection of gait change from walking to sitting can be as fast as 0.17 s, which is adequate for future control applications. However, detecting the transition from standing to walking can take as long as 1.2 s. 3. This study also find that the network trained for one person can also detect movement changes for different persons without deteriorating the performance.<\/jats:p>","DOI":"10.3390\/s23167203","type":"journal-article","created":{"date-parts":[[2023,8,16]],"date-time":"2023-08-16T10:09:33Z","timestamp":1692180573000},"page":"7203","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Machine Learning for Human Motion Intention Detection"],"prefix":"10.3390","volume":"23","author":[{"given":"Jun-Ji","family":"Lin","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei City 106319, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Che-Kang","family":"Hsu","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei City 106319, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei-Li","family":"Hsu","sequence":"additional","affiliation":[{"name":"School and Graduate Institute of Physical Therapy, National Taiwan University, No. 17, Xuzhou Rd., Zhongzheng Dist., Taipei City 100025, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tsu-Chin","family":"Tsao","sequence":"additional","affiliation":[{"name":"Mechanical and Aerospace Engineering, Samueli School of Engineering, University of California, Los Angeles, CA 90095, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5011-7934","authenticated-orcid":false,"given":"Fu-Cheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei City 106319, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jia-Yush","family":"Yen","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei City 106319, Taiwan"},{"name":"Department of Mechanical Engineering, National Taiwan University of Science and Technology, No. 43, Keelung Rd., Sec. 4, Da\u2019an Dist., Taipei City 106335, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"664","DOI":"10.3389\/fbioe.2020.00664","article-title":"Supervised Machine Learning Applied to Wearable Sensor Data Can Accurately Classify Functional Fitness Exercises within a Continuous Workout","volume":"8","author":"Preatoni","year":"2020","journal-title":"Front. 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