Accurate Fall Detection Algorithm Based on SBPSO-SVM Classifier
Weimin Xiong, Yunkun Ning, Shengyun Liang, Guoru Zhao, Yingnan Ma, Xing Gao, Yuwei Zhu · 2018
For the purpose of improving the medical care which aims at the elderly and the chronic patients who are prone to falls, this paper makes use of Standard Binary Particle Swarm Optimization(SBPSO) to search for the combination of best feature subset and parameters (C, g), which can be used to train the SVM(Support Vector Machine). Experiments results show that the proposed method can get higher accuracy (about 99%) compared with non-optimized SVM, k-NN (k Nearest Neighbors) and threshold-based method when dealing with the classification of ADL (Activities in Daily Life) and abnormal falls.