Fall detection (FD) systems are important assistive technologies for healthcare that can detect emergency fall events and alert caregivers. However, it is not easy to obtain large-scale annotated fall events with various specifications of sensors or sensor positions during the implementation of accurate FD systems. Moreover, the knowledge obtained through machine learning has been restricted to tasks in the same domain. The mismatch between different domains might hinder the performance of FD systems. Cross-domain knowledge transfer is very beneficial for machine-learning based FD systems to train a reliable FD model with well-labeled data in new environments. In this study, we propose domain-adaptive fall detection (DAFD) using deep adversarial training (DAT) to tackle cross-domain problems, such as cross-position and cross-configuration. The proposed DAFD can transfer knowledge from the source domain to the target domain by minimizing the domain discrepancy to avoid mismatch problems. The experimental results show that the average F1-score improvement when using DAFD ranges from 1.5% to 7% in the cross-position scenario, and from 3.5% to 12% in the cross-configuration scenario, compared to using the conventional FD model without domain adaptation training. The results demonstrate that the proposed DAFD successfully helps to deal with cross-domain problems and to achieve better detection performance.

中文翻译：

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使用深度对抗训练的领域自适应跌倒检测

跌倒检测 (FD) 系统是重要的医疗保健辅助技术，可以检测紧急跌倒事件并提醒护理人员。然而，在精确的 FD 系统的实现过程中，要获得具有各种规格的传感器或传感器位置的大规模带注释的跌倒事件并不容易。此外，通过机器学习获得的知识仅限于同一领域的任务。不同域之间的不匹配可能会阻碍 FD 系统的性能。跨域知识转移对于基于机器学习的 FD 系统非常有益，可以在新环境中使用标记良好的数据训练可靠的 FD 模型。在这项研究中，我们提出了使用深度对抗训练 (DAT) 的域自适应跌倒检测 (DAFD) 来解决跨域问题，例如交叉位置和交叉配置。所提出的 DAFD 可以通过最小化域差异来避免不匹配问题，从而将知识从源域转移到目标域。实验结果表明，与使用传统FD模型相比，在交叉位置场景中使用DAFD时平均F1-score提高范围为1.5%至7%，在交叉配置场景中为3.5%至12%。领域适应训练。结果表明，所提出的 DAFD 成功地有助于处理跨域问题并实现更好的检测性能。与使用没有域适应训练的传统 FD 模型相比，在交叉配置场景中从 3.5% 到 12%。结果表明，所提出的 DAFD 成功地有助于处理跨域问题并实现更好的检测性能。与使用没有域适应训练的传统 FD 模型相比，在交叉配置场景中从 3.5% 到 12%。结果表明，所提出的 DAFD 成功地有助于处理跨域问题并实现更好的检测性能。