As the percentage of elderly people in developed countries increases worldwide, the healthcare of this collective is a worrying matter, especially if it includes the preservation of their autonomy. In this direction, many studies are being published on Ambient Assisted Living (AAL) systems, which help to reduce the preoccupations raised by the independent living of the elderly. In this study, a systematic review of the literature is presented on fall detection and Human Activity Recognition (HAR) for the elderly, as the two main tasks to solve to guarantee the safety of elderly people living alone. To address the current tendency to perform these two tasks, the review focuses on the use of Deep Learning (DL) based approaches on computer vision data. In addition, different collections of data like DL models, datasets or hardware (e.g. depth or thermal cameras) are gathered from the reviewed studies and provided for reference in future studies. Strengths and weaknesses of existing approaches are also discussed and, based on them, our recommendations for future works are provided.

翻译：随着发达国家老年人口比例在全球范围内持续上升，该群体的医疗保健问题日益令人担忧，尤其是涉及维护其自主生活能力方面。为此，大量关于环境辅助生活系统的研究相继发表，这些系统有助于缓解老年人独立生活所带来的顾虑。本研究对面向老年人的跌倒检测与人类活动识别进行了系统性文献综述，作为保障独居老人安全需解决的两项核心任务。针对当前执行这两项任务的研究趋势，本综述重点分析了基于深度学习的计算机视觉数据方法。此外，我们从所综述的研究中收集了不同类型的资源（包括深度学习模型、数据集及硬件设备，如深度摄像头或热成像摄像头），为未来研究提供参考。同时深入探讨了现有方法的优缺点，并据此提出了未来工作的建议。