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Human-centred artificial intelligence for mobile health sensing: challenges and opportunities

Published version
Peer-reviewed

Repository DOI


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Authors

Spathis, Dimitris 
Ghosh, Abhirup 
Mascolo, Cecilia 

Abstract

Advances in wearable sensing and mobile computing have enabled the collection of health and well-being data outside of traditional laboratory and hospital settings, paving the way for a new era of mobile health. Meanwhile, artificial intelligence (AI) has made significant strides in various domains, demonstrating its potential to revolutionize healthcare. Devices can now diagnose diseases, predict heart irregularities and unlock the full potential of human cognition. However, the application of machine learning (ML) to mobile health sensing poses unique challenges due to noisy sensor measurements, high-dimensional data, sparse and irregular time series, heterogeneity in data, privacy concerns and resource constraints. Despite the recognition of the value of mobile sensing, leveraging these datasets has lagged behind other areas of ML. Furthermore, obtaining quality annotations and ground truth for such data is often expensive or impractical. While recent large-scale longitudinal studies have shown promise in leveraging wearable sensor data for health monitoring and prediction, they also introduce new challenges for data modelling. This paper explores the challenges and opportunities of human-centred AI for mobile health, focusing on key sensing modalities such as audio, location and activity tracking. We discuss the limitations of current approaches and propose potential solutions.

Description

Peer reviewed: True


Publication status: Published

Keywords

mobile health, artificial intelligence for health, wearable sensing

Journal Title

Royal Society Open Science

Conference Name

Journal ISSN

2054-5703

Volume Title

10

Publisher

The Royal Society
Sponsorship
H2020 European Research Council (Project 833296)
Wellcome Trust Project (213939)