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As the percentage of elderly people in developed countries increasesworldwide, the healthcare of this collective is a worrying matter, especiallyif it includes the preservation of their autonomy. In this direction, manystudies are being published on Ambient Assisted Living (AAL) systems, whichhelp to reduce the preoccupations raised by the independent living of theelderly. In this study, a systematic review of the literature is presented onfall detection and Human Activity Recognition (HAR) for the elderly, as the twomain tasks to solve to guarantee the safety of elderly people living alone. Toaddress the current tendency to perform these two tasks, the review focuses onthe use of Deep Learning (DL) based approaches on computer vision data. Inaddition, different collections of data like DL models, datasets or hardware(e.g. depth or thermal cameras) are gathered from the reviewed studies andprovided for reference in future studies. Strengths and weaknesses of existingapproaches are also discussed and, based on them, our recommendations forfuture works are provided.

Deep Learning for Computer Vision based Activity Recognition and Fall Detection of the Elderly: a Systematic Review