Propagation of errors from skull kinematic measurements to finite element tissue responses

Abstract

Real-time quantification of head impacts using wearable sensors is an appealing approach to assess concussion risk. Traditionally, sensors were evaluated for accurately measuring peak resultant skull accelerations and velocities. With growing interest in utilizing model-estimated tissue responses for injury prediction, it is important to evaluate sensor accuracy in estimating tissue response as well. Here, we quantify how sensor kinematic measurement errors can propagate into tissue response errors. Using previous instrumented mouthguard validation datasets, we found that skull kinematic measurement errors in both magnitude and direction lead to errors in tissue response magnitude and distribution. For molar design instrumented mouthguards susceptible to mandible disturbances, 150-400% error in skull kinematic measurements resulted in 100% error in regional peak tissue response. With an improved incisor design mitigating mandible disturbances, errors in skull kinematics were reduced to <50%, and several tissue response errors were reduced to <10%. Applying 30[Formula: see text] rotations to reference kinematic signals to emulate sensor transformation errors yielded below 10% error in regional peak tissue response; however, up to 20% error was observed in peak tissue response for individual finite elements. These findings demonstrate that kinematic resultant errors result in regional peak tissue response errors, while kinematic directionality errors result in tissue response distribution errors. This highlights the need to account for both kinematic magnitude and direction errors and accurately determine transformations between sensors and the skull.

Head Impact Sensor Studies In Sports: A Systematic Review Of Exposure Confirmation Methods

“…23,28,33 Increasingly, research studies have used such technology to collect head impact data in sports, commonly American football. 30 Validation of kinematics recorded by head impact sensors in laboratory settings has typically used anthropometric test devices, 44 cadavers 17 and human volunteers. 47 Furthermore, field studies have evaluated the accuracy of sensors to measure head impact exposure using video analysis.…”

Concussion classification via deep learning using whole-brain white matter fiber strains

“…More recently, white matter (WM) fiber strain [11][12][13][14] is also being explored as a potential improvement. There is growing interest in utilizing modelsimulated responses to benchmark the performance of other kinematic injury metrics [6,[15][16][17].…”

Design and Virtual Testing of American Football Helmets–A Review

“…A different approach is presented by Bruneau and Cronin [66], Post et al [67] and Kuo et al [68]. These scientific groups investigated the head motion with a helmet to prescribed skull kinematics.…”

“…A number of impact tests (Cronin: 6; Post: 27; Kuo: 108) with recorded kinematics were conducted. However, Kuo et al [68] recorded the data using an instrumented mouthguard. Prescribing rigidbody kinematics to the head centre of gravity and treating the skull as a rigid allowed the authors to apply angular and linear head velocities measured from the first approach [57,[74][75][76].…”