@article{ref1,
title="A head impact detection system using SVM classification and proximity sensing in an instrumented mouthguard",
journal="IEEE transactions on bio-medical engineering",
year="2014",
author="Wu, Lyndia and Zarnescu, Livia and Nangia, Vaibhav and Cam, Bruce and Camarillo, David",
volume="61",
number="11",
pages="2659-2668",
abstract="Injury from blunt head impacts causes acute neurological deficits and may lead to chronic neurodegeneration. A head impact detection device can serve both as a research tool for studying head injury mechanisms and a clinical tool for real-time trauma screening. The simplest approach is an acceleration thresholding algorithm, which may falsely detect high-acceleration spurious events such as manual manipulation of the device. We designed a head impact detection system that distinguishes head impacts from non-impacts through two subsystems. First, we use infrared proximity sensing to determine if the mouthguard is worn on the teeth to filter out all offteeth events. Second, on-teeth, non-impact events are rejected using a support vector machine classifier trained on frequency domain features of linear acceleration and rotational velocity. The remaining events are classified as head impacts. In a controlled laboratory evaluation, the present system performed substantially better than a 10g acceleration threshold in head impact detection (98% sensitivity, 99.99% specificity, 99% accuracy, and 99.98% precision, compared to 92% sensitivity, 58% specificity, 65% accuracy, and 37% precision). Once adapted for field deployment by training and validation with field data, this system has the potential to effectively detect head trauma in sports, military service, and other high-risk activities.<p /> <p>Language: en</p>",
language="en",
issn="0018-9294",
doi="10.1109/TBME.2014.2320153",
url="http://dx.doi.org/10.1109/TBME.2014.2320153"
}