Abstract
This preliminary study uses a fine-tree machine learning algorithm to replicate bruxism biofeedback systems by detecting bruxism episodes using a wearable sensor system. The detection of bruxism grinding was performed among five different resting/sleeping positions-laying on the front, back, left, and right, and sitting up from four participants. A sequence of ten activities (each activity is a combination of sleeping position and grinding or not grinding) was recorded while wearing the wireless sensing system on the front of the chin directly under the mouth. Both time and frequency domain features were extracted from each axis of the wearable sensor system's accelerometer data sets. They were used to determine the presence of teeth grinding with 98% accuracy, and these features were used and experimented with to optimize the classification accuracy of the system.