{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T13:04:55Z","timestamp":1790859895277,"version":"4.1.0"},"reference-count":17,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Res Notes"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Gravitational artifacts bias Inertial Measurement Unit (IMU) signals, complicating applications such as Seismocardiography (SCG). This study aimed to validate a gravity-artifact reduction algorithm using Madgwick orientation estimation in a controlled, physics-based simulation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A computational phantom was developed using the PyBullet physics engine to generate paired datasets of gravity-free acceleration and simulated IMU signals (with gravitational artifacts). The Madgwick Attitude and Heading Reference System (AHRS) filter was applied to estimate the sensor orientation, followed by a low-pass-filtered gravity projection to subtract artifacts. The gravity-removed acceleration closely matched the gravity-free reference across all axes. Absolute mean differences were consistently below 0.006 m\/s\n                      <jats:sup>2<\/jats:sup>\n                      , and Pearson correlation coefficients exceeded 0.98. Cross-correlation analysis revealed zero temporal lag, while Bland-Altman analysis showed no proportional bias. The highest precision was observed along the gravitational\n                      <jats:italic>z<\/jats:italic>\n                      -axis. These simulation-only results represent an upper bound under idealized conditions. Experimental validation remains necessary before practical deployment.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s13104-026-08032-w","type":"journal-article","created":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T12:33:54Z","timestamp":1790858034000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Research note on gravity artifact reduction in IMU signals using Madgwick orientation estimation"],"prefix":"10.1186","volume":"19","author":[{"given":"Moritz","family":"Toppm\u00f6ller","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Urs-Vito","family":"Albrecht","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,10,1]]},"reference":[{"issue":"1","key":"8032_CR1","doi-asserted-by":"publisher","first-page":"e63306","DOI":"10.2196\/63306","volume":"13","author":"UV Albrecht","year":"2024","unstructured":"Albrecht UV, Mielitz A, Rahman KMA, Kulau U. Identifying gravity-related artifacts on ballistocardiography signals by comparing weightlessness and normal gravity recordings (ARTIFACTS): protocol for an observational study. JMIR Res Protoc. 2024;13(1):e63306 https:\/\/doi.org\/10.2196\/63306","journal-title":"JMIR Res Protoc"},{"issue":"7","key":"8032_CR2","doi-asserted-by":"publisher","first-page":"1346","DOI":"10.1109\/TBME.2006.875664","volume":"53","author":"AM Sabatini","year":"2006","unstructured":"Sabatini AM. Quaternion-based extended Kalman filter for determining orientation by inertial and magnetic sensing. IEEE Trans Biomed Eng. 2006;53(7):1346\u201356. https:\/\/doi.org\/10.1109\/TBME.2006.875664","journal-title":"IEEE Trans Biomed Eng"},{"issue":"8","key":"8032_CR3","doi-asserted-by":"publisher","first-page":"19302","DOI":"10.3390\/s150819302","volume":"15","author":"R Valenti","year":"2015","unstructured":"Valenti R, Dryanovski I, Xiao J. Keeping a good attitude: a quaternion-based orientation filter for IMUs and MARGs. Sensors. 2015;15(8):19302\u201330. https:\/\/doi.org\/10.3390\/s150819302","journal-title":"Sensors"},{"issue":"1-2","key":"8032_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/2000000094","volume":"11","author":"M Kok","year":"2017","unstructured":"Kok M, Hol JD, Sch\u00f6n TB. Using inertial sensors for position and orientation estimation. Found Trends Signal Process. 2017;11(1-2):1\u2013153. https:\/\/doi.org\/10.1561\/2000000094","journal-title":"Found Trends Signal Process"},{"key":"8032_CR5","doi-asserted-by":"crossref","unstructured":"Madgwick SOH, Harrison AJL, Vaidyanathan R. Estimation of IMU and MARG orientation using a gradient descent algorithm. In: 2011 IEEE International Conference on Rehabilitation Robotics. Zurich: IEEE; 2011. p. 1\u20137. 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