Collaborative Internet of Things (C-IoT) Data Analysis for Enhancing Activity Recognition and Preventing Serious Health Problems

Collaborative Internet of Things (C-IoT) is an emerging paradigm that has generated a large amount of accessible and usable data in real-time, constituting an interesting tool for decision-making problems. The paper presents a healthcare data analytics approach based on collecting and analyzing data from C-IoT. We first provide an architectural plan involving several distributed sites collaborating to collect necessary data for patient's health conditions monitoring. We use for that well-known IoT technologies like Radio Frequency IDentification (RFID), Near Field Communication(NFC), Beacons and Ambient Assisted Living sensors. We then learn on such data to create adequate patient profiles and use the Pearson Correlation Coefficient (PCC) over a long period of time data to detect potential health risks. Finally, decisions are made regarding the patient's condition according to the symptoms/diseases detected.