Edge computing in 5G cellular networks for real-time analysis of electrocardiography recorded with wearable textile sensors

TL;DR

This paper demonstrates that 5G cellular networks combined with edge computing enable real-time analysis of ECG data from wearable sensors, showing promising latency and accuracy for vital sign monitoring and emergency detection.

Contribution

It introduces a lightweight IoT platform utilizing 5G and edge devices for continuous ECG analysis with deep learning, highlighting the performance benefits of 5G over previous cellular generations.

Findings

5G achieves an average latency of 110ms for ECG transmission

Data corruption in ECG samples is only 0.07% with 5G

Deep learning inference takes approximately 170ms

Abstract

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.