Falling motion detection algorithm based on deep learning

Falling is a significant cause of injuries and even death in the elderly. The timely detection of the fall action helps to rescue people who may have physical health problems due to the fall, so fall detection is necessary. The traditional fall detection methods are mostly based on wearable devices, which need to be worn all the time, and the cost of the device is high. In recent years, the fall detection method based on computer vision has become a research hot spot. This paper proposes a framework for falling motion detection based on deep learning. To quickly and accurately classify human movements, a method using bone key points as the feature descriptors of human movements is proposed. The OpenPose algorithm is used to extract the human skeleton point information as the primary human body feature, and then use the deep learning method to classify further and recognise our action features. In this paper, four types of daily actions, such as falling and walking, are classified and recognised. The results show that the algorithm achieves an accuracy of 99.4% on our dataset. Simultaneously, 86.1% accuracy is reached in the public dataset fall detection dataset.