Edge Artificial Intelligence: Real-Time Noninvasive Technique for Vital Signs of Myocardial Infarction Recognition Using Jetson Nano

Abstract

The history of medicine shows that myocardial infarction is one of the significant causes of death in humans. The rapid evolution in autonomous technologies, the rise of computer vision, and edge computing offers intriguing possibilities in healthcare monitoring systems. The major motivation of the work is to improve the survival rate during a cardiac arrest through an automatic emergency recognition system under ambient intelligence. We present a novel approach to chest pain and fall posture-based vital sign detection using an intelligence surveillance camera to address the emergency during myocardial infarction. A real-time embedded solution persuaded from “edge AI” is implemented using the state-of-the-art convolution neural networks: single shot detector Inception V2, single shot detector MobileNet V2, and Internet of Things embedded GPU platform NVIDIA’s Jetson Nano. The deep learning algorithm is implemented for 3000 indoor color image datasets: Nanyang Technological University Red Blue Green and Depth, NTU RGB + D dataset, and private RMS dataset. The research mainly pivots on two key factors in creating and training a CNN model to detect the vital signs and evaluate its performance metrics. We propose a model, which is cost-effective and consumes low power for onboard detection of vital signs of myocardial infarction and evaluated the metrics to achieve a mean average precision of 76.4% and an average recall of 80%.

Incremental and parallel proximal SVM algorithm tailored on the Jetson Nano for the ImageNet challenge

“…Because of tremendous growth in manufacturing powerful, low-cost embedded devices, the edge computing becomes a popular choice for machine learning and IoT projects (Ajani et al , 2021; Koul et al , 2019; Kulkarni et al , 2020; Kurniawan, 2021; Latif et al , 2021; Mazzia et al , 2020; Norris, 2020; Pooyandeh and Sohn, 2021; Rahmaniar and Hernawan, 2021; Taylor et al , 2018). The health applications, computer vision and deep learning are tailored on the Jetson Nano (Black, 2022; Budek, 2021; Franklin, 2019; Mishra and Devleker, 2021; Mittal, 2019; Mohan et al , 2021; Rehman et al , 2021; Zualkernan et al , 2022) and the Raspberry Pi (Daher et al , 2021; Glegola et al , 2021; Iodice, 2018).…”

Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions

“…These devices can be used in hospitals, clinics, or at home and allow real-time monitoring of the patient's health status. Using edge computing, these devices can analyze vital signs in real time and alert healthcare providers to anomalies [40,41]. Edge computing facilitates remote patient monitoring and enables healthcare providers to monitor health statuses in real time and detect changes that signal the possible onset of a chronic disease [42,43].…”