Study And Design Of Smart Home For Abnormal Behavior Recognition In Elderly Healthcare

As the world economy continues to develop,the problem of population aging is becoming increasingly prominent.To this end,China proposes the “9073” pension model,that is,90% of the elderly are self-care by the family,7% enjoy the community home care service,and 3% enjoy the institutional pension service.However,the number of existing home care service practitioners and professionalism is far away,not meeting the requirements.With the development of artificial intelligence and Internet of Things technology,smart home technology can provide a series of life support for elderly people living alone,track the health status of residents,and realize the identification of abnormal behavior of residents,which can effectively alleviate this problem.However,the following problems exist in the actual development and application of smart home:(1)The current smart home is mainly used in smart furniture,environmental monitoring and intelligent security,paying no attention to the health status of residents and the application of abnormal behavior detection at home.(2)The risk awareness of smart residential sensing systems is not strong.At the same time,we must also pay attention to the protection of privacy while collecting household health information.(3)For the monitoring of abnormal behaviors of the elderly,the current use of wristbands,RFID tags and the accuracy of the user’s abnormal behavior recognition is not high.In order to solve the above problems,this study proposes a new abnormal behavior detection idea,providing detection and early warning for the abnormal behavior of elderly at home.The main work is as follows:(1)Combing the common abnormal behaviors and causes of the elderly,and constructing a model of abnormal leaning and falling behavior.Typical abnormal behavior detection methods are combed from the perspective of computer vision,wearable devices and indoor positioning sensors.(2)Propose a method for obtaining the human body gray map based on the ordinary camera and obtaining the human skeleton map and its three-dimensional coordinates by deep learning techniques and Euclidean transformation(3)A set of abnormal behavior monitoring system was constructed.Combining the abnormality of the elderly and the fall behavior model to extract the key features of the human skeleton,the SVM machine learning method is used to classify and identify the data,and the recognition of the abnormal behavior of the elderly is realized.The experimental accuracy rate is 97%.The method and abnormal behavior monitoring system constructed in this study can greatly improve the abnormal behavior perception effect of the elderly in the home.It is used for the construction of intelligent housing for the elderly,which can effectively alleviate the pension pressure of relevant government departments and families.It has important research and application.value.