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This study introduces a Vector AutoRegressive (VAR) framework to jointly model linear acceleration and angular velocity during head impacts, capturing the inherent coupling between translational and rotational dynamics.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Controlled impacts were performed on a helmeted Hybrid III 50th percentile male headform using a pendulum impactor across multiple locations (front, front-oblique, side, rear-oblique) and three impact intensities (30\u00b0, 50\u00b0, 70\u00b0 pendulum angles). Helmet-mounted Inertial Measurement Unit (IMU) signals were processed using VAR models trained on reference headform data at 30\u00b0 and 50\u00b0 impacts. The proposed framework was also evaluated for the estimation of head kinematics at untrained impact intensities (70\u00b0).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>At the trained intensities, the VAR approach generally improved angular velocity reconstruction across impact configurations, with PMPE and RMSE reductions of up to 20.9\u00a0pp (\u221281.1%) and 288.8 \u00b0\/s (\u221285.7%), respectively, at 30\u00b0. Linear acceleration improvements were more limited, with PMPE and RMSE reductions of up to 171.8\u00a0pp (\u221285.7%) and 10.9\u00a0g (\u221274.8%), also at 30\u00b0. At the untrained 70\u00b0 intensity, angular velocity PMPE decreased at all locations but significantly only at the front-oblique, while RMSE decreased significantly at the front and front-oblique (up to\u2009\u2212\u2009341.1 \u00b0\/s,\u2009\u2212\u200952.2%). In contrast, linear acceleration PMPE degraded at all locations, increasing by up to 68.6\u00a0pp (+726.4%), while RMSE showed either deterioration or non-significant improvement.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>In conclusion, the VAR modeling framework offers a promising multivariate approach for reconstructing head impact kinematics from a single IMU-instrumented helmet. However, limited generalization to higher, untrained intensities highlights the need for further development before real-time head injury assessment can be supported.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s10439-026-04395-0","type":"journal-article","created":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T19:36:22Z","timestamp":1790796982000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Estimating Linear and Rotational Head Kinematics in Ice Hockey from a Single Helmet-Mounted IMU Using Vector Autoregressive Models"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3548-2188","authenticated-orcid":false,"given":"Dario","family":"Sciacca","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anisoara","family":"Ionescu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,30]]},"reference":[{"issue":"11","key":"4395_CR1","doi-asserted-by":"publisher","first-page":"981","DOI":"10.1503\/cmaj.120511","volume":"185","author":"CH Tator","year":"2013","unstructured":"Tator, C. 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