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  • Harnessing digital phenotyping to deliver real-time interventional bio-feedback

    Rights statement: © ACM, 2019. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in UbiComp/ISWC '19 Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers, 2019 http://doi.acm.org/10.1145/3341162.3344838

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    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

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Harnessing digital phenotyping to deliver real-time interventional bio-feedback

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Publication date9/09/2019
Host publicationUbiComp/ISWC '19 Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers
Place of PublicationNew York
PublisherACM
Pages1206-1209
Number of pages4
ISBN (Print)9781450368698
Original languageEnglish

Abstract

With the decreasing cost and increasing capability of sensor and mobile technology along with the proliferation of data from social media, ambient environment and other sources, new concepts for digital prognostic and technological quantification of wellbeing are emerging. These concepts are referred to as Digital Phenotyping. One of the main challenges facing these technologies development is connecting how to design an easy to use and personalized devices which benefits from interventional feedback by leveraging on-device processing in real-time. Tangible interfaces designed for wellbeing possess the capabilities to reduce anxiety or manage panic attacks, thus improving the quality of life of the general population and vulnerable members of society. Real-time Bio-feedback presents new opportunities in Artificial Intelligence (AI) with the possibility for mental wellbeing to be inferred automatically allowing interventional feedback to be automatically applied and for the feedback to be individually personalised. This research explores future directions for Bio-feedback including the opportunity to fuse multiple AI enabled feedback mechanisms that can then be utilised collectively or individually.

Bibliographic note

© ACM, 2019. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in UbiComp/ISWC '19 Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers, 2019 http://doi.acm.org/10.1145/3341162.3344838