Background

Fall detection has been extensively explored in both academia and industry. Different methods with different levels of accuracy have been developed for detecting people falling. Using cameras and image processing [1], location and depth sensors [2], and wearable sensors which is the main focus of the current work. Authors of SmartFall [3] used the data collected from a microsoft band-2 smartwatch to train a deep recurrent neural network for detecting people falling. Up-Fall [4] takes a multimodal approach by combining fall data from wearable sensors, visual sensors, and ambient sensors. Multiple useful datasets are included in this dataset. They developed a large multimoal dataset [5] for fall detection