Fall Detection Approach Using Variational Autoencoders with Self-Attention Features - Tomorn Soontornnapar; Tuchsanai Ploysuwan (My University, 2023)
In this paper, we propose an alternative method for fall detection using variational autoencoders (VAEs) with an attention mechanism on an existing dataset. The dataset consists of 6 different fall cases from 21 people. For effective fall detection, we introduce the use of the magnitude of the acceleration vector (MAV) of wearable gyroscope data and apply fast-Fourier transform (FFT) to create new features. These FFT features are then passed through attention modules with self-combination to form attention features. Our experimental results show that the VAE with self-attention features achieved an average accuracy of 90.7% and an F1 score of 93.8% in fall detection, demonstrating the effectiveness of the proposed method in utilizing gyroscope sensors for fall detection in the context of threshold criteria.
Keywords: Context-Aware Activity Recognition Systems; Gait Recognition and Analysis; Anomaly Detection Techniques and Applications