{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T04:21:48Z","timestamp":1774585308817,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,20]],"date-time":"2023-07-20T00:00:00Z","timestamp":1689811200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"German Research Foundation (DFG)","award":["ES 434\/8-1"],"award-info":[{"award-number":["ES 434\/8-1"]}]},{"name":"German Research Foundation (DFG)","award":["820820"],"award-info":[{"award-number":["820820"]}]},{"name":"IMI Mobilise-D","award":["ES 434\/8-1"],"award-info":[{"award-number":["ES 434\/8-1"]}]},{"name":"IMI Mobilise-D","award":["820820"],"award-info":[{"award-number":["820820"]}]},{"name":"Deutsche Forschungsgemeinschaft and Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","award":["ES 434\/8-1"],"award-info":[{"award-number":["ES 434\/8-1"]}]},{"name":"Deutsche Forschungsgemeinschaft and Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","award":["820820"],"award-info":[{"award-number":["820820"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Wearable sensors are able to monitor physical health in a home environment and detect changes in gait patterns over time. To ensure long-term user engagement, wearable sensors need to be seamlessly integrated into the user\u2019s daily life, such as hearing aids or earbuds. Therefore, we present EarGait, an open-source Python toolbox for gait analysis using inertial sensors integrated into hearing aids. This work contributes a validation for gait event detection algorithms and the estimation of temporal parameters using ear-worn sensors. We perform a comparative analysis of two algorithms based on acceleration data and propose a modified version of one of the algorithms. We conducted a study with healthy young and elderly participants to record walking data using the hearing aid\u2019s integrated sensors and an optical motion capture system as a reference. All algorithms were able to detect gait events (initial and terminal contacts), and the improved algorithm performed best, detecting 99.8% of initial contacts and obtaining a mean stride time error of 12 \u00b1 32 ms. The existing algorithms faced challenges in determining the laterality of gait events. To address this limitation, we propose modifications that enhance the determination of the step laterality (ipsi- or contralateral), resulting in a 50% reduction in stride time error. Moreover, the improved version is shown to be robust to different study populations and sampling frequencies but is sensitive to walking speed. This work establishes a solid foundation for a comprehensive gait analysis system integrated into hearing aids that will facilitate continuous and long-term home monitoring.<\/jats:p>","DOI":"10.3390\/s23146565","type":"journal-article","created":{"date-parts":[[2023,7,21]],"date-time":"2023-07-21T01:58:38Z","timestamp":1689904718000},"page":"6565","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["EarGait: Estimation of Temporal Gait Parameters from Hearing Aid Integrated Inertial Sensors"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5891-218X","authenticated-orcid":false,"given":"Ann-Kristin","family":"Seifer","sequence":"first","affiliation":[{"name":"Machine Learning and Data Analytics Lab (MaD Lab), Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg (FAU), 91052 Erlangen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8708-0426","authenticated-orcid":false,"given":"Eva","family":"Dorschky","sequence":"additional","affiliation":[{"name":"Machine Learning and Data Analytics Lab (MaD Lab), Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg (FAU), 91052 Erlangen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5686-281X","authenticated-orcid":false,"given":"Arne","family":"K\u00fcderle","sequence":"additional","affiliation":[{"name":"Machine Learning and Data Analytics Lab (MaD Lab), Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg (FAU), 91052 Erlangen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2715-8591","authenticated-orcid":false,"given":"Hamid","family":"Moradi","sequence":"additional","affiliation":[{"name":"Machine Learning and Data Analytics Lab (MaD Lab), Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg (FAU), 91052 Erlangen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9651-0966","authenticated-orcid":false,"given":"Ronny","family":"Hannemann","sequence":"additional","affiliation":[{"name":"WS Audiology, 91058 Erlangen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0417-0336","authenticated-orcid":false,"given":"Bj\u00f6rn M.","family":"Eskofier","sequence":"additional","affiliation":[{"name":"Machine Learning and Data Analytics Lab (MaD Lab), Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg (FAU), 91052 Erlangen, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"884208","DOI":"10.3389\/fdgth.2022.884208","article-title":"Wearable use in an observational study among older adults: Adherence, feasibility, and effects of clinicodemographic factors","volume":"4","author":"Paolillo","year":"2022","journal-title":"Front. 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