Deep Learning for Computer Vision based Activity Recognition and Fall
  Detection of the Elderly: a Systematic Review
F. Xavier Gaya-Morey Cristina Manresa-Yee Jose M. Buades-Rubio
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.