Real-time fall prediction and protection can significantly reduce the risk of fall injury and improve the self-care ability and physical and mental health of the elderly living alone. In order to improve the recognition accuracy, recall rate and specificity of the fall prediction algorithm, and reduce the fall discrimination and inflating time of the air bag protection system, a SVM-based multi-threshold fall prediction algorithm and air bag protection system were designed to realize the real-time prediction and protection of the fall behavior. Firstly, the acceleration sensor worn on the waist is used to collect motion data. Then, SVM algorithm is used to obtain the acceleration, acceleration and attitude Angle threshold of classified falls and daily behaviors. Finally, the prediction algorithm is reconstructed on the single chip microcomputer to realize the real-time prediction of falls and determine whether the air bag protection system is triggered according to the prediction results. The experimental results show that the recognition accuracy, recall rate and specificity of the proposed algorithm for falls are 97.3%, 99% and 96.1%, respectively. The average inflating time of the protective air bag is 350.4ms, which has the advantages of high recognition accuracy and short inflating time, thus strengthening the application of the system in real-time fall prediction and protection.