We are the Camarillo Lab at Stanford University. Over the past five years, we have been developing and validating an instrumented mouthguard for measuring head impact kinematics on the field (primarily in American football). We are now ready to begin dissemination of our new, Stanford Instrumented Mouthguard v2.0 (MiG2.0) to researchers across the country interested in deploying our device to athletes to make measurements of concussive head impacts.

Novel design enables brain strain precision > 95% 1,2

The mouthguard is designed to mitigate disturbances caused by lower jaw dynamics (A) 1. Our design incorporates optimal placement of the sensors over the incisors and standoffs at the molars to isolate the sensors from lower jaw bite forces (B). To validate our device, we performed dummy experiments and compared acceleration and brain strain prediction (from WHIM finite element model3) against dummy references (C). We find that the new design can measure rotational acceleration with 12% of reference, and predict peak principal brain strains within 1% of reference (D).

Impact detection system to enable sensitive and specific event classification 3,4

Most devices on the market are unable to differentiate between impacts and other high acceleration events. With our impact detection algorithm2, impacts are classified as on teeth or off teeth using a proximity sensor. Next, the acceleration signals are processed and classified using a machine learning algorithm. Our system achieved 97.1% accuracy in classifying field data, with area under the ROC curve close to 1, while simple linear acceleration thresholding did not perform much better than random guessing.

In the past decade, researchers have made great strides in understanding the mechanisms underlying concussions. However, due to a lack of specific information and accurate measurements of concussions in humans, we have been unable to confirm many of our hypotheses. With this dissemination, we will build a community of researchers collecting high quality head kinematics data from concussions in order to form a large, pooled set of concussion data. If you are interested in collecting concussion data from the field, we invite you to participate in our program to use the MiG2.0 as your wearable head impact sensor. We ascribe to an open source philosophy, which means that if your participation in our program also gives you access to concussion data from other researchers in the community. With this, we hope to build a strong community to push our understanding of concussions forward.


1) Kuo, Calvin, et al. "Effect of the mandible on mouthguard measurements of head kinematics." J Biomech 49.9 (2016): 1845-1853.

2) Kuo, Ji, Camarillo, et. al. 2017, Submitted

3) Wu, Lyndia C., et al. "A head impact detection system using SVM classification and proximity sensing in an instrumented mouthguard." IEEE TBME. 61.11 (2014)

4) Wu, L. et. al. “Accurate Detection of Football Head Impacts Using Biomedical Features and Support Vector Machine Classification”. Under Review Scientific Reports