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Thirteen healthy subjects with no signs of upper limb injury were recruited for this study and instrumented with IMU sensors and optical markers. Three IMU calibration methods were compared: N-pose calibration, functional calibration, and manual alignment. Subjects executed simple single-plane single-joint tasks for each upper limb joint as well as more complex multijoint tasks. We performed a 3-way analysis of variance on range of motion error, root mean squared error, and offset to assess differences between calibrations, tasks, and anatomical axes. Differences in the 3 IMU calibrations are minor and not statistically significant for most tasks and anatomical axes, with the exception of the offset interaction calibration\u2009\u00d7\u2009axes (<jats:italic>P<\/jats:italic>\u2009&lt;\u2009.001, <jats:inline-formula\/>). Specifically, manual alignment gives the best offset estimation on the abduction\/adduction and internal\/external rotation axes. Therefore, we recommend the use of a static N-pose calibration procedure as the preferred IMU calibration method to model the humerothoracic joint, as this setup is the simplest as it only requires accurate positioning of the trunk sensor.<\/jats:p>","DOI":"10.1123\/jab.2023-0276","type":"journal-article","created":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T18:43:31Z","timestamp":1731955411000},"page":"37-46","source":"Crossref","is-referenced-by-count":5,"title":["Effects of Different Inertial Measurement Unit Sensor-to-Segment Calibrations on Clinical 3-Dimensional Humerothoracic Joint Angles Estimation"],"prefix":"10.1123","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-2098-1870","authenticated-orcid":true,"given":"Alessandro","family":"Bonfiglio","sequence":"first","affiliation":[{"name":"Euleria Health, Rovereto, Italy"},{"name":"DISI\u2014Information Engineering and Computer Science Department, University of Trento, Trento, Italy"},{"name":"E3DA\u2014Energy Efficient Embedded Digital Architectures, Fondazione Bruno Kessler, Trento, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9047-9868","authenticated-orcid":true,"given":"Elisabetta","family":"Farella","sequence":"additional","affiliation":[{"name":"E3DA\u2014Energy Efficient Embedded Digital Architectures, Fondazione Bruno Kessler, Trento, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Tacconi","sequence":"additional","affiliation":[{"name":"Euleria Health, Rovereto, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3518-7464","authenticated-orcid":true,"given":"Raoul M.","family":"Bongers","sequence":"additional","affiliation":[{"name":"Department of Human Movement Sciences, University Medical Center Groningen, Groningen, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"100","reference":[{"key":"r1","series-title":"J Biomech","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbiomech.2020.109832","article-title":"Determining anatomical frames via inertial motion capture: a survey of methods","volume":"106","author":"Vitali RV","year":"2020","unstructured":"Vitali RV, Perkins NC. 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