Human fall is one of the very critical health issues, especially for elders and disabled people living alone. The number of elder populations is increasing steadily worldwide. Therefore, human fall detection is becoming an effective technique for assistive living for those people. For assistive living, deep learning and computer vision have been used largely. In this review article, we discuss deep learning (DL)-based state-of-the-art non-intrusive (vision-based) fall detection techniques. We also present a survey on fall detection benchmark datasets. For a clear understanding, we briefly discuss different metrics which are used to evaluate the performance of the fall detection systems. This article also gives a future direction on vision-based human fall detection techniques.
翻译:人类坠落是极为重要的健康问题之一,特别是对独居的老年人和残疾人而言。全世界老年人口的数量正在稳步增加。因此,人类坠落检测正在成为帮助这些人生活的有效技术。对于辅助性生活、深层学习和计算机的愿景,我们主要使用了。在本评论文章中,我们讨论了基于深层次学习(DL)的最先进非侵入性(视觉)跌落检测技术。我们还介绍了关于跌落检测基准数据集的调查。为了明确理解,我们简要讨论了用来评估跌落检测系统性能的不同指标。这一文章还就基于愿景的人类坠落检测技术提供了未来方向。