A Cloud-Based Accessible Architecture for Large-Scale ADL Analysis Services

Recognizing Activities of Daily Living (ADL) plays an important role in healthcare. However, it is often impractical and sometimes impossible for a person to collect those useful data manually, not to mention constant long-term data maintenance and analysis. To address the above-mentioned challenges, we propose an architecture, in which many health-care applications and services can easily build upon, for collective long-term ADL pattern analysis that leverages several prominent advantages inherent in cloud computing. The core of the proposed infrastructure includes a module to perform MapReduce-assisted Bayesian activity recognition based on all collected ADL data. Better yet, the resultant data analysis can be delivered as a service from a service station which serves as a readily accessible interface to 3^rd party service providers and end-users. For the evaluation of the proposed architecture, a simulation of persuasive health engagement is presented and discussed as one potential application.