  Fall prevalence is high among elderly people, which is challenging due to the
severe consequences of falling. This is why rapid assistance is a critical
task. Ambient assisted living (AAL) uses recent technologies such as 5G
networks and the internet of medical things (IoMT) to address this research
area. Edge computing can reduce the cost of cloud communication, including high
latency and bandwidth use, by moving conventional healthcare services and
applications closer to end-users. Artificial intelligence (AI) techniques such
as deep learning (DL) have been used recently for automatic fall detection, as
well as supporting healthcare services. However, DL requires a vast amount of
data and substantial processing power to improve its performance for the IoMT
linked to the traditional edge computing environment. This research proposes an
effective fall detection framework based on DL algorithms and mobile edge
computing (MEC) within 5G wireless networks, the aim being to empower
IoMT-based healthcare applications. We also propose the use of a deep gated
recurrent unit (DGRU) neural network to improve the accuracy of existing
DL-based fall detection methods. DGRU has the advantage of dealing with
time-series IoMT data, and it can reduce the number of parameters and avoid the
vanishing gradient problem. The experimental results on two public datasets
show that the DGRU model of the proposed framework achieves higher accuracy
rates compared to the current related works on the same datasets.
