To limit the spread of COVID-19, thermal screening cameras were installed everywhere. These cameras observe many thermal faces. These thermal face data are generally used to monitor strange temperatures for COVID-19 screening or to maintain social distancing. Big data of Thermal face generated everywhere should be used in the more practical functions. We proposed a method to measure non-contact breathing signals using thermal face data. In addition, breathing signals data estimated from thermal face data was converted to DICOM waveform Information Object Definitions (IODs) for interoperability management of medical data. The proposed method was tested on a golden reference (chest belt) with a mean accuracy of 93.52 %. a proposed method that can extract breathing signals using thermal screening cameras that are widely available around the world and manage data as healthcare interoperability information can show important potential in the public, telemedicine field in the future.

In the past few years, the use of big data has been important for public health. However, it was difficult to use medical imaging as public data. Several problems, such as data privacy and inconsistent measurement system, were not suitable for the interoperability in big data. In this paper, we proposed the reference model that satisfies the interoperability of medical imaging data and able to be shared in public data server. The de-identification method minimizes personal information, in order to protect the confidentiality of patients. The quantification method for medical imaging was to calibrate the measured values using a reference material. Finally, de-identified and quantified medical imaging data was shared with a public server.