Conversational agents are increasingly becoming digital partners in our everyday computational experiences. Although rich, and fresh in content, they are oblivious to users’ locality beyond geospatial weather and traffic conditions. We introduce conversational agents that are hyper-local, embedded deeply into the urban infrastructure providing rich, purposeful, detail, and in some cases playful information relevant to a neighborhood. These agents are spatially constrained, and one can only interact with them once she is in close vicinity at street-level granularity. In other words, the city provides personal, stateful, spontaneous service to its citizens through the agents installed in urban landmarks. Drawing lessons from two user studies, we identify the requirements for this system. We then discuss the architecture of these agents that leverage covert communication channels and machine learning algorithms that run on the edge and wearable devices to offer meaningful conversational experience in urban settings.

We present eSense - an open and multi-sensory in-ear wearable platform for personal-scale behaviour analytics. eSense is a true wireless stereo (TWS) earbud and supports dual-mode Bluetooth and Bluetooth Low Energy. It is also augmented with a 6-axis inertial measurement unit and a microphone. We demonstrate the eSense platform, the data exploration tool with the open APIs for the real-time visualisation of multi-modal sensory data, and its manifestation in a 360 ^◦ workplace well-being application.

We present eSense - an open and multi-sensory in-ear wearable platform to detect and monitor human activities. eSense is a true wireless stereo (TWS) earbud with dual-mode Bluetooth and Bluetooth Low Energy and augmented with a 6-axis inertial measurement unit and a microphone. We showcase the eSense platform, its data APIs to capture real-time multi-modal sensory data in a data exploration tool, and its manifestation in a 360^◦ workplace well-being application.