A Novel Posture Recognition Based on Time Series Supervised Learning Algorithm

Falling from bed during the process of getting out of bed is a serious accident for disabled and elderly patients. It's crucial to accurately predict the intention to get out of bed. However, simply relying on the change of pressure or posture may cause misprediction. In this paper, we propose a novel posture recognition method based on time series analysis and supervised learning algorithm. Firstly, we depict the relationship of posture, pressure and the intention to get out of bed through the preliminary experiment. Secondly, we analyze the pressure changes in continuous time and identify the process of leaving bed by means of comparing the target time series. To reduce the misjudgment of behavior caused by differences in individual behavior time, dynamic time warping (DTW) algorithm is utilized to improve the traditional Euclidean distance calculation method. Thirdly, we compare the accuracy of four different supervised learning arithmetics on posture recognition. Nonlinear SVM achieved the best recognition of 96.8%. Finally, we combine the results of time series analysis and the classification results of supervised learning algorithms to predict the intention to get out of bed. The experimental result shows that our proposed method can accurately predict the intention to get out of bed.