Abstract
We describe the design and development of sensor nodes, based on Edge computing technologies, for the processing and classification of events detected in physiological signals such as the electrocardiographic signal (ECG is the electrical signal of the heart), temperature, heart rate, and human movement. The edge device uses a 32-bit Tensilica microcontroller-based module with the ability to transmit data wirelessly using Wi-Fi. In addition, algorithms for classification and detection of movement patterns were implemented to be implemented in devices with limited resources and not only in high-performance computers. The Internet of Things and its application in smart environments can help non-intrusive monitoring of daily activities by implementing support vector machine (SVM is a machine learning algorithm) for implementation in embedded systems with low hardware resources. This paper shows experimental results obtained during the acquisition, transmission, and processing of physiological signals in a edge computing system and their visualization in a web application.
| Original language | English |
|---|---|
| Pages (from-to) | 98-105 |
| Number of pages | 8 |
| Journal | Indonesian Journal of Electrical Engineering and Computer Science |
| Volume | 28 |
| Issue number | 1 |
| DOIs | |
| State | Published - Oct 2022 |
| Externally published | Yes |
Bibliographical note
Funding Information:This work was developed as part of the Doctorate studies at the Faculty of Systems and Informatics Engineering of the Universidad Nacional Mayor de San Marcos and in the laboratories of INICTEL-UNI (National Institute of Research and Training in Telecommunications-Universidad Nacional de Ingeniería).
Publisher Copyright:
© 2022 Institute of Advanced Engineering and Science. All rights reserved.
Keywords
- Artificial intelligence
- Edge computing
- Electrocardiographic
- Internet of things
- Vector support machine
- WebSocket