Research Article
Specification of Quality of Context Requirements for Digital Phenotyping Applications
@INPROCEEDINGS{10.1007/978-3-031-34586-9_43, author={Lu\^{\i}s Eduardo Costa Laurindo and Ivan Rodrigues de Moura and Luciano Reis Coutinho and Francisco Jos\^{e} da Silva e Silva}, title={Specification of Quality of Context Requirements for Digital Phenotyping Applications}, proceedings={Pervasive Computing Technologies for Healthcare. 16th EAI International Conference, PervasiveHealth 2022, Thessaloniki, Greece, December 12-14, 2022, Proceedings}, proceedings_a={PERVASIVEHEALTH}, year={2023}, month={6}, keywords={Digital Phenotyping Acquisition and Distribution Quality of Context (QoC) Incorporation of QoC Requirements Domain-Specific Language}, doi={10.1007/978-3-031-34586-9_43} }- Luís Eduardo Costa Laurindo
Ivan Rodrigues de Moura
Luciano Reis Coutinho
Francisco José da Silva e Silva
Year: 2023
Specification of Quality of Context Requirements for Digital Phenotyping Applications
PERVASIVEHEALTH
Springer
DOI: 10.1007/978-3-031-34586-9_43
Abstract
Digital phenotyping applications use sensor data from personal digital devices (e.g., smartphones, smart bands) to quantify moment-to-moment human phenotype at the individual in-situ level. Ensuring the quality and distribution of the data used is essential requirement in the domain of these applications. Context Quality (QoC) refers to the Information Quality (QoI) used and the Quality of Service (QoS) level of information distribution. QoI is measured by parameters that define how reliable the information is. On the other hand, QoS is provided by specifying the quality of service for distributing context data. Some aspects can degrade the QoC of the application, such as information from sensors being imprecise, wireless communication technologies used in the acquisition and distribution of information, scalability problems can cause information delay, and intermittent connection due to user mobility can result in data loss. Therefore, this study conceives a process for incorporating QoC requirements and a Domain-Specific Language (DSL) to specify these requirements in digital phenotyping applications. A case study was carried out where the scenario of an application for monitoring workers’ health was considered. It was possible to prove the expressiveness and simplicity of the proposed language when using it to define the instances of the application classes responsible for the acquisition and distribution of context information.
