BEHAVIOR RECOGNITION WITH LSTM DEEP LEARNING MODEL AND MEDIAPIPE

Human behavior recognition is crucial for assisting and monitoring the activities of patients, particularly, the elderly or young children. With the advancement of technology, modern methods based on computer vision have been developing. Deep learning is one of theprominent methods for dealing with problems related to behavior recognition. In this study, a long short-term memory deep learning model is used for identifying abnormal behaviors. The MediaPipe library is used to collect body points and consecutive frames to generate training data and recognition. The behaviors considered in this paper include headache, stomachache, fall down, and others. With the dataset collected from videos and self-recorded, the experimental results show that the proposed long short-term memorynetwork model achieves 94.54% accuracy in behavior recognition. This result demonstrates the feasibility of the proposed model for the task of behavior recognition.