Fatigue Estimation using Facial Expression features and RemotePPG Signal Currently, research and development of lifestyle support robots in daily life is being actively conducted. Healthcase is one such function robots. In this research, we develop a fatigue estimation system using a camera that can easily be mounted on robots. Measurements taken in a real environment have to be consider noises caused by changes in light and the subjectxe2x80x99s movement. This fatigue estimation system is based on a robust feature extraction method. As an indicator of fatigue, LFHFratio was calculated from the power spectrum of RR interval in the electrocardiogram or the blood volume pulse (BVP). The BVP can be detected from the fingertip by using the photoplethysmography (PPG). In this study, we used a contactless PPG: remotePPG (rPPG) detected by the luminance change of the face image. Some studies show facial expression features extracted from facial video are also useful for fatigue estimation. dimension reduction of past method using LLE spoiled the information in the large dimention of feature. We also developed a fatigue estimation method with such features using a camera for the healthcare robots. It used facial landmark points, lineofsight vector, and size of the ellipse fitted with eyes and mouth landmark points. Therefore, proposed method simply use timevarying shape information of face like size of eyes, or gaze direction. We verified the performance of proposed features by the fatigue state classification using Support Vector Machine (SVM).

A new method for muscular visual fatigue detection using electrooculogram Abstract   Objective  Muscular visual fatigue (MVF)is increasingly common in clinic; However, there is no objective and effective means for the detection of muscular visual fatigue. This study focuses on a new method for muscular visual fatigue detection based on electrooculogram (EOG).    Methods  We analyzed the mechanism that develops muscular visual fatigue and designed an experiment to induce muscular visual fatigue intentionally. And we recorded electrooculogram and critical fusion frequency (CFF) in the process. Then we got four electrooculogram physiological indicators and correlation between them and critical fusion frequency was analyzed. Finally, the indicators tendency, statistical difference and support vector machine (SVM) analysis were carried out.    Results  The work shows that both wavelet packet barycenter frequency (WPBF) and average blink time (ABT) are significantly correlated with critical fusion frequency, tendency of both them has a good consistency, there is a significant difference for them both before and after muscular visual fatigue and that the trained support vector machine has a classification accuracy of 0.796 (SD 0.172) for states before and after muscular visual fatigue.    Conclusion  Wavelet packet barycenter frequency and average blink time can be used for muscular visual fatigue detection, a certain degree of muscular visual fatigue occurred after induction and the trained support vector machine can achieve a good classification detection. We conclude that wavelet packet barycenter frequency and average blink time can be used for accurate muscular visual fatigue detection.    Significance  This study is of great significance in muscular visual fatigue prevention and treatment.