Continuous monitoring of cough may provide insights into the health of individuals as well as the effectiveness of treatments. Smart-watches, in particular, are highly promising for such monitoring: they are inexpensive, unobtrusive, programmable, and have a variety of sensors. However, current mobile cough detection systems are not designed for smartwatches, and perform poorly when applied to real-world smartwatch data since they are often evaluated on data collected in the lab.In this work we propose CoughWatch, a lightweight cough detector for smartwatches that uses audio and movement data for in-the-wild cough detection. On our in-the-wild data, CoughWatch achieves a precision of 82% and recall of 55%, compared to 6% precision and 19% recall achieved by the current state-of-the-art approach. Furthermore, by incorporating gyroscope and accelerometer data, CoughWatch improves precision by up to 15.5 percentage points compared to an audio-only model.

Containers are an emerging approach for application deployment on the edge, as they are modular, lightweight, and easy to use for development and maintenance. However, deploying containers in an edge computing environment brings new challenges: high latency links, limited resources, and user mobility. This work proposes a new edge deployment architecture that accelerates deployment and updates for edge applications. By overcoming the design limitations of current registries, the accelerator would reduce the deployment, start-up, and update times of container-based applications.