A detection method for the fall behavior of elders based on hidden markov model

As the social aging process quickened, the demand to care the elderly's health and safety is increasing. The fall in the elderly population is a very common phenomenon; it has been a major health risk that diminishes the quality of life among the elderly people. In this paper, we proposed a new method using acceleration observation series to build a hidden Markov model (HMM)to detect the fall behavior. The method extracted acceleration characteristic time series from human fall course to describe the fall process, and used the acceleration characteristic time series to train HMM in order to build a random process mathematical model. The 300 samples of experimental data from 10 volunteers were obtained, and 5 - fold cross-validation was used to estimate the model. Results showed that the accuracy of the method was 98. 2%, the sensitivity was 91.3%, and the specificity was 99. 6%, showing that the proposed method gets good result in detecting fall events.