Currently, research and development of lifestyle support robots in daily life is being actively conducted. Health-case 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 subject's movement. This fatigue estimation system is based on a robust feature extraction method. As an indicator of fatigue, LF/HF-ratio 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: remote-PPG (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, line-of-sight vector, and size of the ellipse fitted with eyes and mouth landmark points. Therefore, proposed method simply use time-varying 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 model based on a Petri net (PN) is proposed for controlling flexible manufacturing systems (FMSs). The model can describe not only normal sequential processes but also interruptive exceptional handling processes by means of simple structures. A layered structure and two types of token are introduced into a PN to describe exception handling and error recovery. An application in which this model was used to program a programmable logic controller (PLC) is described. The description of cell control can be simplified by the model.>