Inferring User Intents from Motion in Hearing Healthcare

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedings – Annual report year: 2018Researchpeer-review

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Sensors in our phones and wearables, leave digital traces of our activities. With active user participation, these devices serve as personal sensing devices, giving insights to human behavior, thoughts, intents and personalities. We discuss how acoustical environment data from hearing aids, coupled with motion and location data from smartphones, may provide new insights to physical and mental health. We outline an approach to model soundscape and context data to learn preferences for personalized hearing healthcare. Using Bayesian statistical inference we investigate how physical motion and acoustical features may interact to capture behavioral patterns. Finally, we discuss how such insights may offer a foundation for designing new types of participatory healthcare solutions, as preventive measures against cognitive decline, and physical health.

Original languageEnglish
Title of host publicationProceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers
PublisherAssociation for Computing Machinery
Publication date2018
Pages670-675
ISBN (Print)978-1-4503-5966-5
DOIs
Publication statusPublished - 2018
Event2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers - Singapore, Singapore
Duration: 8 Oct 201812 Oct 2018

Conference

Conference2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers
CountrySingapore
CitySingapore
Period08/10/201812/10/2018
CitationsWeb of Science® Times Cited: No match on DOI

    Research areas

  • Hearing impairment, User behavior, Health, Aging, Augmented audio, Activity, Motion, Mental health
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