A Sensor-Based Data Analytics for Patient Monitoring in Connected Healthcare Applications

Nowadays, keeping a strong and good health is one of the main concern of the general public or governments. The Internet of Things (IoT) has been emerged as an efficient solution to build smart healthcare systems deployed either at hospitals or in-home. Such networks rely on biomedical sensors which are used in electronics-based medical equipment to remotely collect vital signs of patients (pressure, temperature, hart rate, oxygen saturation etc.). Generally, these biosensors are implemented on or inside the patient's body and take three types of record data such as numerical, images and videos. However, the big data collected by various biomedical sensors along with the need of emergency detection, the limited sensor energies, and the prediction of the progress of patient situation are the major challenges for health-based IoT applications. In order to overcome these challenges, we propose, in this paper, an efficient sensor-based data analytics for real-time patient monitoring and assessment to help both hospital and medical staff. The proposed mechanism consists in three phases: Emergency detection, adapting sensing frequency and real time prediction of patient situation. Through simulations on real health data, we show the effectiveness of our mechanism compared to other exiting techniques.