Deep learning-based fall detection in Internet of Medical Things using wearable sensors

Abstract

Accidental and sudden fall is a significant cause of injury and even death in most cases, among the old age people. As it has a remarkable impact on the national health system, so extensive development and proper research for fall detection systems are very necessary. This thesis provides a comprehensive review of state of-the-art technologies and finds deep learning as the most powerful methodology. In this study, we proposed an Inception Resnet inspired cnn plus bidirectional LSTM neural network. We have trained and fine tune this model on state of the art Korean dataset which was recently published. We have calculated the magnitude of all the 3 channels of acceleration, gyroscope, and Euler data in the preprocessing step before feeding it to the network. In this study, we have considered 1D, 3D, and 6D data and trained the same model accordingly, and achieved remarkable results. It has been observed that our model performed better in the case of 8 overlapping step sizes.