A Secured Healthcare Model for Sensor Data Sharing With Integrated Emotional Intelligence

• Published in: IEEE sensors journal Vol. 22; no. 16; pp. 16306 - 16313
• Main Authors: Satamraju, Krishna Prasad, Balakrishnan, Malarkodi
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.08.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Access control; Authentication; blockchain; Blockchains; Body sensors; Counterfeit; Cryptography; Data retrieval; Electronic devices; Emotional intelligence; Face recognition; Health care; healthcare; Information sharing; Intelligent sensors; Internet of Things; Medical services; Network latency; Network reliability; Physical unclonable function; Security; Sensors; Smart contracts
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2022.3189268

Integration of healthcare and Internet of Things (IoT) has a potential to revolutionize the medical treatments, diagnosis and predict medical issues thereby enabling patients, families, doctors and medical insurers stay connected for a proactive delivery of services. However, IoT devices operate in infrastructure-less environments, and hence data security and privacy is always a concern. Counterfeit IoT devices create huge menace in sensitive applications. Moreover, conventional healthcare systems are not patient-centric in the sense that they do not include patient's emotions during treatments. EI helps the clinicians better understand and mange medical procedures. This paper presents a human-centered approach in healthcare domain by the integration of emotional intelligence (EI) and sensor network built around IoT devices. A Raspberry Pi connected with sensors and a camera acts as an IoT device. These sensors collect body vital parameters and Facial expression recognition (FER) based EI. The system is hosted on an Ethereum permissioned blockchain for reliability, security and tamper-proof data sharing and storage. Devices in the network are authenticated using physical unclonable functions (PUFs). Comparative analysis confirms that the PUF-based authentication is 330% faster than conventional methods. The system offers latency of as low as 20 ms. Using smart contracts, the proposed model provides role-based access control and helps in building scalable and harmonious digital healthcare platforms.