Internet of Things Applied in Healthcare Based on Open Hardware with Low-Energy Consumption
10.4258/hir.2019.25.3.230
- Author:
Leonardo Juan RAMIREZ LOPEZ
1
;
Gabriel PUERTA APONTE
;
Arturo RODRIGUEZ GARCIA
Author Information
1. TIGUM Research Group, Universidad Militar Nueva Granada, Bogota, Colombia. leonardo.ramirez@unimilitar.edu.co
- Publication Type:Case Report
- Keywords:
Internet;
Healthcare;
Physiologic Monitoring;
Remote Sensing Technology;
Pulse Oximetry
- MeSH:
Delivery of Health Care;
Heart Rate;
Internet;
Models, Theoretical;
Monitoring, Physiologic;
Oximetry;
Oxygen;
Remote Sensing Technology
- From:Healthcare Informatics Research
2019;25(3):230-235
- CountryRepublic of Korea
- Language:English
-
Abstract:
OBJECTIVES: The Internet of Things (IoT) and its applications are growing simultaneously. These applications need new intelligent devices along heterogeneous networking. Which makes them costly to implement indeed. Platforms and open devices designed for open-source hardware are possible solutions. This research was conducted under an IoT design, implementation, and assessment model for the remote monitoring of pulse oximetry via oxygen partial saturation (SpO2) and heart rate (HR) with low-energy consumption. METHODS: This study focused on the development of SpO2 and HR measurements that will allow the monitoring and estimation in real time of the user's state and health related to the established parameters. Measurements were acquired and recorded using a remote web server that recorded the acquired variables for further processing. The statistical analysis data allows comparison of the registered data measured with theoretical models. RESULTS: The IoT model was developed use Bluetooth low-energy devices, which comply with low-cost and open-hardware solutions operated via ‘HTTP requests’ for data transmission and reception from a cloud server to an edge device. Network performance assessment was conducted to guarantee the availability and integrity of the acquired values and signals. The system measured SpO2 and HR variables. The most significant result was to achieve energy consumption 20% lower than that of devices in the market. CONCLUSIONS: In summary, the acquired data validation based on the IoT model had a transmission error of 0.001% which proves its applicability in healthcare.
