Heuristic Optimization based Abnormal Posture Detection Algorithm

Abstract

This paper studies the abnormal posture detection algorithm based on heuristic optimization. Using the data collected by sensors, the features such as acceleration and angular velocity are extracted and put into the classifiers for training. We select the appropriate heuristic algorithm according to different classifier models for optimization. The results demonstrate that, in binary classification experiment, the accuracy ratio of the K-Nearest Neighbor (KNN) model is 99.54%, and the AUC is 0.99. In quad classification experiment, The Support Vector Machine (SVM) model has a 94.32% accuracy ratio and a 0.95 AUC, which has the optimal performance.