{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T23:04:36Z","timestamp":1790723076048,"version":"4.1.0"},"reference-count":60,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T00:00:00Z","timestamp":1770854400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Okawa Foundation","award":["24-12"],"award-info":[{"award-number":["24-12"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The margin of stability (MoS) is a metric used to assess dynamic postural stability during walking. Although MoS is typically computed from optical motion capture data, previous studies have shown that MoS can be approximated from six-axis kinematic signals\u2014linear acceleration and angular velocity\u2014measured by inertial measurement units (IMUs). With IMU-equipped devices such as smartphones and smartwatches becoming widespread, it is increasingly common for individuals to carry two or more such devices in daily life. This study aimed to identify combinations of two body locations that most effectively predict MoS. IMU sensors were attached to ten body locations while participants walked on a treadmill. Principal motion analysis, a reductive regression method for multidimensional time-series data, was employed for MoS prediction, and cross-validation was used for reliable model evaluation. Appropriate combinations of two IMU sensors achieved mean errors of approximately 30 mm and 11 mm in anterior and mediolateral MoS, respectively, compared with reference values derived from optical motion capture. These errors were comparable to the intrinsic standard deviations of MoS, suggesting that IMU-based MoS estimation is sufficiently accurate for the classification of individuals at high fall risk.<\/jats:p>","DOI":"10.3390\/s26041211","type":"journal-article","created":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T15:28:11Z","timestamp":1770910091000},"page":"1211","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Dynamic Gait Stability Estimated Using One or Two Inertial Measurement Units Worn on the Human Body"],"prefix":"10.3390","volume":"26","author":[{"given":"Haoyun","family":"Peng","sequence":"first","affiliation":[{"name":"Department of Computer Science, Tokyo Metropolitan University, 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2116-7734","authenticated-orcid":false,"given":"Shogo","family":"Okamoto","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Tokyo Metropolitan University, 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroki","family":"Watanabe","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Tokyo Metropolitan University, 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4169-3734","authenticated-orcid":false,"given":"Yasuhiro","family":"Akiyama","sequence":"additional","affiliation":[{"name":"Department of Mechanics and Robotics, Shinshu University, 3-15-1 Tokita, Ueda, Nagano 386-8567, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"161","DOI":"10.9759\/hppt.4.161","article-title":"Falls and fall prevention in elderly people: Summary of recent reviews","volume":"4","author":"Sakita","year":"2015","journal-title":"Jpn. 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