Rapid prototyping IoT solutions based on Machine Learning

Nowadays Machine Learning (ML) has reached an all-time high, and this is evident by considering the increasing number of successful start-ups, applications and services in this domain. ML techniques are being developed and applied to an ever-growing range of fields, from on-demand delivery to smart home. Nevertheless, these solutions are failing at getting mainstream adoption among interaction designers due to high complexity. In this paper we present the integration of two Machine Learning algorithms into UAPPI, our open source extension of the prototyping environment MIT App Inventor. In UAPPI much of the complexity related to ML has been abstracted away, providing easy-to-use graphical blocks for rapid prototyping Internet of Things solutions. We report on limits and opportunities emerged from the first two scenario-based explorations of our design process.