ArticleAnnals of biomedical engineering2025
Helmet-Head Decoupling in Ice Hockey Impacts: An In-lab Exploratory Study Using Autoregressive Modeling of Acceleration Data Measured from a Helmet-Mounted Inertial Measurement Unit (IMU).
Article in Annals of biomedical engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- In-lab assessment of a ice hockey helmet-integrated sensor fusion framework for torque estimation: A proof-of-concept study.JSAMS plus · 2026Article
- Estimating Linear and Rotational Head Kinematics in Ice Hockey from a Single Helmet-Mounted IMU Using Vector Autoregressive Models.Annals of biomedical engineering · 2026Article
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2 authors.
Funding
Abstract
purposeThis study aims to develop and validate in-lab a novel approach for estimating head linear acceleration in ice hockey impacts using IMU-instrumented helmets. The use of AutoRegressive (AR) modeling was investigated as a solution to mitigate the decoupling observed between the helmet and the head.
methodsA series of impacts were conducted on a helmeted Hybrid III 50th percentile male Anthropometric Test Device (ATD). The impacts were performed using a custom-built pendulum impactor in four directions (front, front-oblique, side and back-oblique) and at two energies, 33 and 79 J, except for the back-oblique direction, which was tested only at 33 J. The processing pipeline included impact segmentation, main direction estimation and application of the AR-based transfer function modeling. The error with respect to the reference signals from the headform was quantified and the transformed signals were compared with the unprocessed (raw) and lowpass filtered signals. The generalization capabilities of the transfer function were also evaluated on a different helmet type.
resultsThe application of the transfer function resulted in a reduction of up to 9.04 g (57%) and 27.54% for the average Root Mean Squared Error (RMSE) and peak Mean Absolute Percentage Error (MAPE), respectively, with a consistent error decrease across all impact directions, compared to the lowpass filtered signal. However, when evaluated on a different helmet model, the transfer function showed larger errors.
conclusionThe proposed methodology effectively improves the estimation of head linear acceleration across all impact directions. Nevertheless, performance varies with helmet type, indicating the need for helmet-specific adjustments (e.g., through model retraining).
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