Fall Detection of the Elderly Using Denoising LSTM-Based Convolutional Variant Autoencoder

As societies age, the issue of falls has become increasingly critical for the health and safety of the elderly. Fall detection in the elderly has traditionally relied on supervised learning methods, which require data on falls, which is difficult to obtain in real situations. In addition, the complexity of integrating deep learning (DL) models into wearable devices for real-time fall detection has been challenging due to limited computational resources. In this article, we propose a novel fall detection method using unsupervised learning based on a denoising long short-term memory (LSTM)-based convolutional variational autoencoder (CVAE) model to solve the problem of lack of fall data. By utilizing the proposed data debugging and hierarchical data balancing techniques, the proposed method achieves an
$F1$
score of 1.0 while reducing the parameter count by 25.6 times compared to the state-of-the-art unsupervised DL method. The resulting model occupies only 157.65 kB of memory, making it highly suitable for integration into wearable devices.