{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T01:37:50Z","timestamp":1791941870334,"version":"4.3.4"},"reference-count":34,"publisher":"Human Kinetics","issue":"5","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,10,1]]},"abstract":"<jats:p>Ear-worn wearables (aka: earbuds, hearables, or earables) are commonly used by runners for entertainment, and many modern devices also include inertial sensors for user interaction. We propose harnessing the technology embedded in earbuds to capture fundamental aspects of running mechanics and make them available to the wider community of users, outside a lab setting. While other wearables such as insoles\u00a0or ankle-\/sacrum-mounted inertial measurement units have already been presented, ear-worn devices may have a better potential for adoption and therefore offer an optimal compromise between validity of running gait analysis and usability. Thirty healthy participants (18 males, 12 females) ran on an instrumented treadmill (54,000 gait cycles) and floor-mounted force plates (2800 gait cycles) at a variety of speeds. Building on the information brought about by the vibrations transmitted to, and motion of, the head, we devised a gait event detection algorithm and a regression model to predict vertical ground reaction force waveforms. The validation of outcomes against quantities from force plates shows an average mean absolute percentage error of 4.8% on temporal metrics and 9.0% on scalar ground reaction force derived metrics. Additionally, the model tracks the full vertical ground reaction force curve well, achieving an normalized root mean square error of 11.1% on unseen participants. Overall, we show comparable accuracy from an ear-worn consumer device in temporal and kinetic gait parameter estimation to specialist devices, paving the way for accessible running gait monitoring.<\/jats:p>","DOI":"10.1123\/jab.2025-0337","type":"journal-article","created":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T18:40:42Z","timestamp":1777142442000},"page":"383-392","source":"Crossref","is-referenced-by-count":2,"title":["Ear-Worn Inertial Sensors Can Predict Gait Metrics and Reconstruct Vertical Ground Reaction Force Curves During Running"],"prefix":"10.1123","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6733-0504","authenticated-orcid":true,"given":"Jake","family":"Stuchbury-Wass","sequence":"first","affiliation":[{"name":"Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9550-7637","authenticated-orcid":true,"given":"Mathias","family":"Ciliberto","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5508-7188","authenticated-orcid":true,"given":"Kayla-Jade","family":"Butkow","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5202-7892","authenticated-orcid":true,"given":"Qiang","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2474-2004","authenticated-orcid":true,"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5383-7072","authenticated-orcid":true,"given":"Ezio","family":"Preatoni","sequence":"additional","affiliation":[{"name":"Department for Health, University of Bath, Bath, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3824-234X","authenticated-orcid":true,"given":"Dong","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Computing and Information Systems, Singapore Management University, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9614-4380","authenticated-orcid":true,"given":"Cecilia","family":"Mascolo","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"100","reference":[{"key":"r1","series-title":"Wonder","article-title":"Globally, how many people practice running (as a sport), and what is the global market size for this sport?","year":"2025","unstructured":"Askwonder. 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