中文摘要：

提出一种基于人体动作状态序列时序分析法的人体摔倒预测方法．融合特征部位加速胰；信息为时间序列，选取摔倒过程中人体与低势物体碰撞前的过程序列段作为样本训练隐马尔可夫模型（HMM），通过分析输入序列与HMM的匹配程度实时分析当前时刻人体摔倒的风险．实验证明该方法取得良好的预测效果，并且可有效区分摔倒过程与其它日常生活行为过程．

英文摘要：

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 pro,cedure. 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-t~ime 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.