Falls are very common among elderly patients in hospitals and nursing homes. Therefore, this paper presents recognition of getting-up motion which is an early indicator that can be used to help prevent falls. We propose a method for human action recognition based on patterns of motions. The method is constructed by autocorrelation of features extracted from edge direction and local intensity gradients. With the focus on the local regions of the image sequence, the features extracted by our proposal method are used as input information for AdaBoost. These features are turned to discriminate between different classes of action. We evaluate our algorithms on 95 video sequences, containing 31 getting-up motions and 64 different actions.
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