论文标题
朝着基于WiFi的秋季秋季检测进行对抗数据增强
Towards a Robust WiFi-based Fall Detection with Adversarial Data Augmentation
论文作者
论文摘要
最近基于WiFi的秋季检测系统由于其优势比其他感觉系统引起了很多关注。多亏了机器学习和深度学习技术，各种实现在性能方面取得了令人印象深刻的进步。但是，许多这样的高精度系统在未看到的环境中无法实现鲁棒性时具有较低的可靠性。为了解决这个问题，本文通过对抗数据增强研究了一种概括方法。我们的结果表明，尽管性能并不重要，但在看不见的领域的深度学习系统中有了略有改善。
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.
