Tooth brushing pattern classification using three-axis accelerometer and magnetic sensor for smart toothbrush.

The concept of intelligent toothbrush, capable of monitoring brushing motion, orientation through the grip axis, during toothbrushing was suggested in our previous study. In this study, we describe a tooth brushing pattern classification algorithm using three-axis accelerometer and three-axis magnetic sensor. We have found that inappropriate tooth brushing pattern showed specific moving patterns. In order to trace the position and orientation of toothbrush in a mouth, we need to know absolute coordinate information of toothbrush. By applying tilt-compensated azimuth (heading) calculation algorithm, which is generally used in small telematics devices, we could find the inclination and orientation information of toothbrush. To assess the feasibility of the proposed algorithm, 8 brushing patterns were preformed by 6 individual healthy subjects. The proposed algorithm showed the detection ratio of 98%. This study showed that the proposed monitoring system was conceived to aid dental care personnel in patient education and instruction in oral hygiene regarding brushing style.

Using neural network to recognize human emotions from heart rate variability and skin resistance.

The purpose of this paper is to estimate emotions using Neural network and the changes in activities of autonomic nervous system(ANS). Since ANS cannot be controlled artificially, we presumed that the changes in emotions would be reflected to the changes in ANS. In order to observe those changes, we provided the subjects with some video clips which can induce a variety of emotions and measured the changes in ANS, especially in Heart Rate Variability(HRV) and in Galvanic Skin Response(GSR). With those analyzed results from the experiments, we established an algorithm based on Neural network, finally we could reach the estimating rate of 80.2%