British Journal of Computer, Networking and Information Technology (BJCNIT)

Healthcare Sensor –Based Physiological Parameters Data Aggregation and Analytics Scheme for Monitoring of Patients in Internet of Medical Things (IoMT) Enabled E-Healthcare Platform.

Abstract

Inadequate mechanism for efficient patient health monitoring and data aggregation mechanism in healthcare services has posed serious bottleneck to healthcare delivery the world over. Healthcare sensors have become an accessible means for the communication of data from patients to medical personnel. The use of Healthcare sensors is also an invention in medical practice which involves measuring vital physiological parameters of patients with the objectives of detecting disorders to mitigating them and preventing severe complications. In this research, a system architecture for effective tracking of patients’ health by deploying the Internet of Medical things (IoMT) as an approach is developed. The methodology used for this research was the Design Science research methodology. The choice of the methodology was born out of the fact that the methodology involves the construction and evaluation of the prototype (artefacts) that address a considerably acknowledged problem. In this adopted approach, real-time data are sent to a local server through communication channels (Wi-Fi) and then transmitted to the Internet of Things (IoT) server through a designated network route using a Wi-Fi module. For efficient transmission of vital signs to the cloud, a Blynk IoT- server was used as platform. Two types of sensors DS18B20 an AD8232 ECG were deployed in monitoring and tracking body temperature and heart rate respectively, the XAMPP server was used as the local server platform. The outcome of this research include a developed artefacts and a mechanism where real-time numerical data is communicated to the report platform which is further transformed into ordered numbers. The novelty in this work involves collating real-time data from sensors attached to patients in IoMT-enabled environments, thereafter a simple linear regression model was deployed to convert these real-time data to ordered numbers which indicate the level of severity of the vital signals collated. These ordered numbers are converted to visuals to enhance ease of view. To the best of our knowledge, this approach has not been implemented by previous research studies reviewed. Based on our findings, we recommend that: the prototype system be developed and deployed in healthcare facilities and the designed web applications be plugged into an existing web domains of healthcare facilities to enhance timely intervention by the healthcare experts.