Implementation of a sensor node for monitoring and classification of physiological signals in an edge computing system

• Autores: Castro Nieto, Antero, Espino Campos, Rafael, Yauri, Ricardo, Gamarra, Segundo
• Formato: artículo
• Fecha de Publicación: 2022
• Institución: Universidad Tecnológica del Perú
• Repositorio: UTP-Institucional
• Lenguaje: español
• OAI Identifier: oai:repositorio.utp.edu.pe:20.500.12867/6008
• Enlace del recurso: https://hdl.handle.net/20.500.12867/6008; http://doi.org/10.11591/ijeecs.v28.i1.pp98-105
• Nivel de acceso: acceso abierto
• Materia: Artificial intelligence; Electrocardiographic; Internet of things; Edge computing; https://purl.org/pe-repo/ocde/ford#2.02.03

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.