Research On Face Recognition Methods For Rehabilition Nursing Robots Beds

With the aging of the population is more and more serious, the number of disabled and semi disabled elderly population accounts for the proportion of the total population rise and nursing care, health care and rehabilitation of the elderly has become one of the important issues of the whole society. At present, the rehabilitation center, medical institutions, community or home care mainly rely on the nursing staff, the high degree of automation, intelligent rehabilitation nursing machine equipment application is not yet universal. In this paper, the research background of the rehabilitation nursing robot bed is studied in this paper. This paper in the light of the present situation of the rehabilitation nursing bed robotic single control mode, carers dependence is too strong, low degree of automation, in the study of face detection, face recognition, facial expression recognition based, to facial expression recognition expert system for robots for rehabilitation nursing beds at the core of the face recognition system for in-depth study.The main research contents are as follows:1. On the basis of studying the basic principle of Adaboost algorithm, a fast and efficient face detection method is proposed, which combines the Adaboost algorithm to detect human face and eye detection.The method ensures the real-time face detection and improves the accuracy of face detection.2. By using the linear discriminant analysis(LDA) method to extract facial features, the face recognition is performed by calculating the Euclidean distance between the features of the face projection. On the basis of this, the method of region approximation is used to track the human face.3. In view of the application of this system, the strategy of facial expression recognition based on the successful face recognition is proposed. First, facial expression recognition needs to be extracted. Secondly, using image segmentation algorithm, segmentation of facial features region. By using the relative relation between the facial features and the facial features, the local feature changes of facial feature points are extracted, and then the corresponding expression types are identified by the expression classifier. To identify the expression type input to the inference engine in the expert system, according to user defined prior to the rule base of the expert system, inference finally out the expression type needs of users, also send commands drive lower machine. Not only enhances the security of the system, but also improves the accuracy of expression recognition.4. In the rule set of expression recognition expert system, the expression result and the change rate of vital sign parameters are used as the front part of the expert system.No matter it is the change of the expression or the change of vital sign parameters, it can control the movement of the rehabilitation nursing robot bed, so as to improve the intelligence degree of the rehabilitation nursing robot bed.On the basis of the above research results, the face recognition system in the rehabilitation nursing robot bed is constructed. The system was tested with 10 images of 5 expressions, and the accuracy of the system was 90.4%. Users of the system through the expression self operation robots for rehabilitation nursing bed, can reduce the working strength of the medical personnel, improve the efficiency of nursing staff, enhance the user satisfaction, but also make the patient get more reliable care, the very value of social marketing.