Edge Artificial Intelligence: Real-Time Noninvasive Technique for Vital Signs of Myocardial Infarction Recognition Using Jetson Nano
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...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Advances in Human-Computer Interaction |
| Online Access: | http://dx.doi.org/10.1155/2021/6483003 |
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| author | H. M. Mohan S. Anitha Rifai Chai Sai Ho Ling |
| author_facet | H. M. Mohan S. Anitha Rifai Chai Sai Ho Ling |
| author_sort | H. M. Mohan |
| collection | DOAJ |
| description | 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%. |
| format | Article |
| id | doaj-art-c1c8243661664a489ff1c3a8a18e78d0 |
| institution | DOAJ |
| issn | 1687-5893 1687-5907 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Human-Computer Interaction |
| spelling | doaj-art-c1c8243661664a489ff1c3a8a18e78d02025-08-20T03:23:28ZengWileyAdvances in Human-Computer Interaction1687-58931687-59072021-01-01202110.1155/2021/64830036483003Edge Artificial Intelligence: Real-Time Noninvasive Technique for Vital Signs of Myocardial Infarction Recognition Using Jetson NanoH. M. Mohan0S. Anitha1Rifai Chai2Sai Ho Ling3R/S, Department of ECE, ACS College of Engineering, Visvesvaraya Technological University, Belagavi, IndiaDepartment of ECE, ACS College of Engineering, Bangalore, IndiaSchool of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, AustraliaSchool of Biomedical Engineering, University of Technology Sydney, Ultimo, AustraliaThe 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%.http://dx.doi.org/10.1155/2021/6483003 |
| spellingShingle | H. M. Mohan S. Anitha Rifai Chai Sai Ho Ling Edge Artificial Intelligence: Real-Time Noninvasive Technique for Vital Signs of Myocardial Infarction Recognition Using Jetson Nano Advances in Human-Computer Interaction |
| title | Edge Artificial Intelligence: Real-Time Noninvasive Technique for Vital Signs of Myocardial Infarction Recognition Using Jetson Nano |
| title_full | Edge Artificial Intelligence: Real-Time Noninvasive Technique for Vital Signs of Myocardial Infarction Recognition Using Jetson Nano |
| title_fullStr | Edge Artificial Intelligence: Real-Time Noninvasive Technique for Vital Signs of Myocardial Infarction Recognition Using Jetson Nano |
| title_full_unstemmed | Edge Artificial Intelligence: Real-Time Noninvasive Technique for Vital Signs of Myocardial Infarction Recognition Using Jetson Nano |
| title_short | Edge Artificial Intelligence: Real-Time Noninvasive Technique for Vital Signs of Myocardial Infarction Recognition Using Jetson Nano |
| title_sort | edge artificial intelligence real time noninvasive technique for vital signs of myocardial infarction recognition using jetson nano |
| url | http://dx.doi.org/10.1155/2021/6483003 |
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