Brain concussions are notoriously difficult to detect. Since CT scanners are rarely available on the sides of football fields, detecting the intensity of head impacts can help players and coaches decide whether to seek medical care. There are already devices in existence that are worn to detect impacts to the head, but researchers at Stanford University have developed a new mouthguard that can sense impacts while ignoring situations where the device is dropped or otherwise handled in unexpected ways.
The new mouthguard has infrared sensors that make sure the device is being worn on the teeth when it’s sensing for impacts. Additionally, an algorithm ignores events that are really non-impacts through a “support vector machine classifier trained on frequency domain features of linear acceleration and rotational velocity,” according to a study published in IEEE Transactions on Biomedical Engineering. Once the system knows the device is being worn on the teeth and that the event is not a false impact, it provides a highly accurate and precise identification of true impacts.
From the study abstract:
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.
IEEE Transactions on Biomedical Engineering: A Head Impact Detection System Using SVM Classification and Proximity Sensing in an Instrumented Mouthguard…