Assessment of an IoT platform for data collection and analysis for medical sensors

Abstract

Health facilities produce an increasing and vast amount of data that must be efficiently analyzed. New approaches for healthcare monitoring are being developed every day and the Internet of Things (IoT) came to fill the still existing void on real-time monitoring. A new generation of mechanisms and techniques are being used to facilitate the practice of medicine, promoting faster diagnosis and prevention of diseases. We proposed a system that relies on IoT for storing and monitoring medical sensors data with analytic capabilities. To this end, we chose two approaches for storing this data which were thoroughly evaluated. Apache HBase presents a higher rate of data ingestion, when collaborating with the Kaa IoT platform, than Apache Cassandra, exhibiting good performance storing unstructured data, as presented in a healthcare environment. The outcome of this system has shown the possibility of a large number of medical sensors being simultaneously connected to the same platform (6000 records sent by the second or 48 ECG sensors with a frequency of 125Hz). The results presented in this paper are promising and should be further investigated as a comprehensive system would benefit the patient's diagnosis but also the physicians.