This paper presents the method of detecting driver's drowsiness level from facial expressions. Our method is executed according to the following flow: taking a driver's facial image, tracing the facial features by image processing, and classifying the driver's drowsiness level by pattern classification. We found that facial expression had the highest linear correlation with brain waves as the general index of drowsiness during monotonous driving. After analyzing the facial muscle activities, we determined 17 feature points on face for detecting driver drowsiness. A camera set on a dashboard recorded the driver's facial image. We applied Active Appearance Model (AAM) for measuring the 3-dimensional coordinates of the feature points on the facial image. In order to classify drowsiness into 6 levels, we applied k-Nearest-Neighbor method. As a result, the average Root Mean Square Errors (RMSE) among 13 participants was less than 1.0 level. Our method also detected the driver's smile.

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This paper presents the method of detecting driver's drowsiness level from the facial expression. The motivation for this research is to realize the novel safety system which can detect the driver's slight drowsiness and keep the driver awake while driving. The brain wave is commonly used as the drowsiness index. However, it is not suitable for the in-vehicle system since it is measured with sensors worn over the head. We precisely investigated the relationship between the change of brain wave and other drowsiness indices that can be measured without any contact; PERCLOS, heart rate, lane deviation, and facial expression. We found that the facial expression index had the highest linear correlation with the brain wave. Therefore, we selected the facial expression as the drowsiness-detection index and automated the drowsiness detection from the facial expression. Three problems need to be solved for automation; (1) how to de ne the features of drowsy expression, (2) how to capture the features from the driver's video-recorded facial image, and (3) how to estimate the driver's drowsiness index from the features. First, we found that frontalis muscle, zygomaticus major muscle, and masseter muscle activated with increase of drowsiness in more than 75 percents of participants. According to the result, we determined the coordinates data of points on eyebrows, eyelids, and mouth as the features of drowsiness expression. Second, we calculated the 3D coordinates data of the features by image processing with Active Appearance Model (AAM). Third, we applied k-Nearest-Neighbor method to classify the driver's drowsiness level. Eleven participants' data of the features and the drowsiness level estimated by trained observers were used as the training data. We achieved the classi cation of the drivers' drowsiness in a driving simulator into 6 levels. The average Root Mean Square Errors (RMSE) among 12 participants was less than 1.0 level.

The purpose of this study was to develop and validate a brief version of the Japanese Academic and Athletic Identity Scale (AAIS-JB), which would enable the survey to be easily conducted online nationwide in Japan. In addition, this study determined the centrality of academic and athletic identities in the elite student-athletes with scholarships and the sub-elite student-athletes with no scholarships. Participants ( n = 1009) consisted of student-athletes from 20 universities (5 districts) in Japan, of which 560 were elite athletes (i.e. receiving scholarships) and 449 were sub-elite athletes (i.e. not receiving scholarships). Results showed content validity, factorial validity, and reliability of the brief version of the AAIS-JB. Both athletic and academic identities were significantly higher for the elite student-athletes than for the sub-elite student-athletes. Differences between the elite and sub-elite student-athletes provide important insights into the further development of intercollegiate sports in Japan. Sports administrators and the coaching staffs engaged with student-athletes can support student-athletes by understanding more about the dominant dimensions of their identity to maximize its positive impact on student-athletes.

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