Towards a Robust WiFi-based Fall Detection with Adversarial Data Augmentation
This work addresses robustness issues in fall detection for healthcare applications, but it is incremental as it builds on existing methods with minor gains.
The paper tackled the problem of low reliability in WiFi-based fall detection systems in unseen environments by using adversarial data augmentation, resulting in a slight improvement in deep learning systems, though the performance was not significant.
Recent WiFi-based fall detection systems have drawn much attention due to their advantages over other sensory systems. Various implementations have achieved impressive progress in performance, thanks to machine learning and deep learning techniques. However, many of such high accuracy systems have low reliability as they fail to achieve robustness in unseen environments. To address that, this paper investigates a method of generalization through adversarial data augmentation. Our results show a slight improvement in deep learning-systems in unseen domains, though the performance is not significant.