Extension of a High-Resolution Intelligence Implementation via Design-to-Robotic-Production and -Operation strategies

This paper extends the development of a responsive built-environment capable of expressing intelligence with respect to both ICTs and Adaptive Architecture. The present implementation is built with mutually informing Design-to-Robotic- Production & -Operation (D2RP&O) strategies and methods developed at Delft University of Technology (TUD). With respect to D2RP, a responsive stage built with deliberately differentiated and function-specific components is revisited and modified. With respect to D2RO, a partially meshed, self-healing, and highly heterogeneous Wireless Sensor and Actuator Network (WSAN) is expanded to integrate proprietary-yet-free cloud-based services. This WSAN is equipped with Machine Learning (ML) mechanisms based on Support Vector Machine (SVM) classifiers for Human Activity Recognition (HAR). The frequency and/or absence of certain activities, in conjunction with processed data streamed from environment-embedded sensing mechanisms, trigger actuations in the built-environment in order to mitigate fatigue, encourage activity/interactivity; and to promote general well-being in the user. A voice-enabled mechanism based on Amazon®’s Alexa Voice Service (AVS) is integrated into the ecosystem to connect the built-environment with services and resources in the World Wide Web (WWW). Furthermore, a notifications mechanism based on Google®’s Gmail© API as well as Twilio®’s REST© API enable instances of fatigue to be reported to third-parties. The present interdisciplinary development attempts to promote an alternative approach to existing Ambient Intelligence (AmI) and Ambient Assisted Living (AAL) frameworks.