  Fifth-generation (5G) cellular networks promise higher data rates, lower
latency, and large numbers of interconnected devices. Thereby, 5G will provide
important steps towards unlocking the full potential of the Internet of Things
(IoT). In this work, we propose a lightweight IoT platform for continuous vital
sign analysis. Electrocardiography (ECG) is acquired via textile sensors and
continuously sent from a smartphone to an edge device using cellular networks.
The edge device applies a state-of-the art deep learning model for providing a
binary end-to-end classification if a myocardial infarction is at hand. Using
this infrastructure, experiments with four volunteers were conducted. We
compare 3rd, 4th-, and 5th-generation cellular networks (release 15) with
respect to transmission latency, data corruption, and duration of machine
learning inference. The best performance is achieved using 5G showing an
average transmission latency of 110ms and data corruption in 0.07% of ECG
samples. Deep learning inference took approximately 170ms. In conclusion, 5G
cellular networks in combination with edge devices are a suitable
infrastructure for continuous vital sign analysis using deep learning models.
Future 5G releases will introduce multi-access edge computing (MEC) as a
paradigm for bringing edge devices nearer to mobile clients. This will decrease
transmission latency and eventually enable automatic emergency alerting in near
real-time.
