Detecting falling events from video for providing timely assistance to the fallen person is a challenging problem in computer vision due to the absence of large-scale fall datasets and the presence of many covariate factors like varying view angles, illumination, and clothing. In this paper, to address this problem, an effective approach for fall detection has been proposed. We have developed a recurrent neural network (RNN) with LSTM architecture that models the temporal dynamics of the 2D pose information of a fallen person. Human 2D pose information, which has proven effective in analyzing fall patterns as it ignores people's body appearance and environmental information while capturing true motion information, makes the proposed model simpler and faster. Experimental results have verified that our proposed method has achieved 99.0% sensitivity on both of the benchmark datasets of fall detection FDD and URFD.