Time Series Analysis Based Human Fall Prediction Method
TONG Li-Na1,2 ,SONG Quan-Jun1 ,GE Yun-Jian1
1.Laboratory of Robot Sensor and Human-Machine Interaction,Institute of Intelligent Machines,Chinese Academy of Sciences,Hefei 230031 2.Department of Automation,School of Information Science and Technology,University of Science and Technology of China,Hefei 230027
Abstract A method for human fall prediction based on time series of human action states is proposed. Firstly, the acceleration time series in characteristic body region is got by information fusion procedure. Secondly, the segments before the collision of body with lower objects in fall processes is chosen as samples to train hidden Markov model (HMM). Then, the current-time fall risk is analyzed by the real-time matching degree between input series and HMM. The experimental result shows that the proposed method gets good result in predicting falls, and the fall events and other daily life activities can be distinguished effectively by it.
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2012, Vol. 25 
