Prof. Cecilia Mascolo: EAI Distinguished lecture on Sounding out audio data and wearable systems for health diagnostics

Prof. Cecilia Mascolo from the University of Cambridge describes how wearable devices, such as our smartphones, watches and earbuds can help to improve the way we understand our behavior and well-being in her Distinguished lecture.

Prof. Mascolo also outlined the usage of microphones and how the input from these devices can be useful in understanding behavior and improving our health. The first example of research she presented is mainly related to quite a topical study related to COVID-19 and the usage of smartphones to obtain audio crowd source from participants. It is possible to develop machine learning models to understand if a person is COVID-positive by using their audio: voice, coughing and breathing via a crowdsourced app.

The second study that Prof. Mascolo talked about is related to inner ear microphones that can be used to also understand movement and to recognize an activity and gestures by using the audio, collected by certain microphones.

She describes the ways to sense the behavior that is generally difficult to sense with the help of continuous input for the diagnostics. Very often there’s information that doctors would like to obtain that often requires user input and attention. So, the way out could be the proxy server, so that users don’t have to continuously input their data.

Prof. Cecilia Mascolo is the mother of a teenage daughter but also a Full Professor of Mobile Systems in the Department of Computer Science and Technology, University of Cambridge, UK. She is co-director of the Centre for Mobile, Wearable System and Augmented Intelligence and Deputy Head of Department for Research. Her research interests are in mobile systems and data for health, human mobility modelling, sensor systems and networking and mobile data analysis. She was listed in “10 Women in Networking /Communications You Should Know” in 2016.

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Watch EAI’s previous Prof. Takahiro Hara: EAI Distinguished lecture on Cross-domain Approaches or User Activity prediction in Online and Real Worlds