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Proceedings Paper

Using machine learning to emulate human hearing for predictive maintenance of equipment
Author(s): Dinesh Verma; Graham Bent
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Paper Abstract

At the current time, interfaces between humans and machines use only a limited subset of senses that humans are capable of. The interaction among humans and computers can become much more intuitive and effective if we are able to use more senses, and create other modes of communicating between them. New machine learning technologies can make this type of interaction become a reality. In this paper, we present a framework for a holistic communication between humans and machines that uses all of the senses, and discuss how a subset of this capability can allow machines to talk to humans to indicate their health for various tasks such as predictive maintenance.

Paper Details

Date Published: 4 May 2017
PDF: 8 pages
Proc. SPIE 10190, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VIII, 101900K (4 May 2017); doi: 10.1117/12.2279891
Show Author Affiliations
Dinesh Verma, IBM Thomas J. Watson Research Ctr. (United States)
Graham Bent, IBM Emerging Technology Services (United Kingdom)


Published in SPIE Proceedings Vol. 10190:
Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VIII
Tien Pham; Michael A. Kolodny, Editor(s)

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