Automatic human fall detection is a challenging task of healthcare in smart homes, and video cameras have been proved to be efficient in addressing this problem. Although existing methods perform relatively well, they are all built upon "hand-crafted" features, thus constraining the performance of the model to some presumed conditions and scenarios, and making it vulnerable to any deviation from the assumed settings. In this paper, we propose a deep-learning-based approach for human fall detection, using long short-term memory neural network. Our model is not restricted to any specific circumstances, and performance evaluations show that it outperforms all the existing methods.