{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T21:25:06Z","timestamp":1790112306237,"version":"4.0.1"},"reference-count":87,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T00:00:00Z","timestamp":1778889600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000049","name":"National Institute on Aging of the National Institutes of Health","doi-asserted-by":"publisher","award":["1R15AG078865-01A1"],"award-info":[{"award-number":["1R15AG078865-01A1"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Wearable sensors are transformative tools for continuous gait assessment in daily life. Tripping, a leading cause of falls, is closely linked to inadequate foot clearance, making accurate foot height measurement critical for fall risk evaluation. Inertial measurement units offer a practical solution for foot trajectory reconstruction; however, conventional drift correction methods such as zero-velocity updates fail to adequately address cumulative height errors. Recent kinematic constraint-based approaches improve height accuracy but remain limited to offline processing and lack simultaneous activity classification. To address these gaps, we developed a real-time, single-IMU system for continuous foot height trajectory reconstruction with simultaneous classification of five locomotion activities deployed on a smartphone. Twenty healthy adults were recruited for model training and independent validation. Level walking maintained ground reference (0.0 cm, 95% CI: [\u22121.8, 1.8] cm), cumulative height errors remained below 1.1 cm across ramp and stair negotiation with a mean absolute error of 0.42%, and obstacle clearance was quantified. The system achieved 96.08% overall classification accuracy with less than one gait cycle latency. Toe height was estimated through rigid-body transformation with comparable accuracy to the foot height. This framework provides a practical foundation for real-time gait intervention and fall prevention applications.<\/jats:p>","DOI":"10.3390\/s26103166","type":"journal-article","created":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T12:36:40Z","timestamp":1779107800000},"page":"3166","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Real-Time Foot Height Estimation and Activity Classification Using a Foot-Mounted IMU Implemented on a Smartphone"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-8165-7375","authenticated-orcid":false,"given":"Ehsan","family":"Sharafian Moghaddam","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, University of Maine, Orono, ME 04469, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7593-6028","authenticated-orcid":false,"given":"Babak","family":"Hejrati","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, University of Maine, Orono, ME 04469, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1038\/s41746-020-00334-y","article-title":"Age and environment-related differences in gait in healthy adults using wearables","volume":"3","author":"Czech","year":"2020","journal-title":"npj Digit. 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