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In Vivo Evaluation of Wearable Head Impact Sensors

Overview of attention for article published in Annals of Biomedical Engineering, August 2015
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Title
In Vivo Evaluation of Wearable Head Impact Sensors
Published in
Annals of Biomedical Engineering, August 2015
DOI 10.1007/s10439-015-1423-3
Pubmed ID
Authors

Lyndia C. Wu, Vaibhav Nangia, Kevin Bui, Bradley Hammoor, Mehmet Kurt, Fidel Hernandez, Calvin Kuo, David B. Camarillo

Abstract

Inertial sensors are commonly used to measure human head motion. Some sensors have been tested with dummy or cadaver experiments with mixed results, and methods to evaluate sensors in vivo are lacking. Here we present an in vivo method using high speed video to test teeth-mounted (mouthguard), soft tissue-mounted (skin patch), and headgear-mounted (skull cap) sensors during 6-13 g sagittal soccer head impacts. Sensor coupling to the skull was quantified by displacement from an ear-canal reference. Mouthguard displacements were within video measurement error (<1 mm), while the skin patch and skull cap displaced up to 4 and 13 mm from the ear-canal reference, respectively. We used the mouthguard, which had the least displacement from skull, as the reference to assess 6-degree-of-freedom skin patch and skull cap measurements. Linear and rotational acceleration magnitudes were over-predicted by both the skin patch (with 120% NRMS error for [Formula: see text], 290% for [Formula: see text]) and the skull cap (320% NRMS error for [Formula: see text], 500% for [Formula: see text]). Such over-predictions were largely due to out-of-plane motion. To model sensor error, we found that in-plane skin patch linear acceleration in the anterior-posterior direction could be modeled by an underdamped viscoelastic system. In summary, the mouthguard showed tighter skull coupling than the other sensor mounting approaches. Furthermore, the in vivo methods presented are valuable for investigating skull acceleration sensor technologies.

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Geographical breakdown

Country Count As %
United States 1 <1%
Unknown 264 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 46 17%
Researcher 32 12%
Student > Bachelor 31 12%
Student > Master 30 11%
Student > Doctoral Student 14 5%
Other 47 18%
Unknown 65 25%
Readers by discipline Count As %
Engineering 75 28%
Sports and Recreations 35 13%
Neuroscience 18 7%
Medicine and Dentistry 16 6%
Agricultural and Biological Sciences 10 4%
Other 30 11%
Unknown 81 31%