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Real-time Estimation of Physical Activity Intensity for Daily Living

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publication2nd IET International Conference on Technologies for Active and Assisted Living (TechAAL 2016)
Publisher or commissioning bodyInstitution of Engineering and Technology (IET)
Pages11-16
Number of pages6
Volume2016
Edition4
ISBN (Print)9781785613937
DOIs
DateAccepted/In press - 27 Oct 2016
DateE-pub ahead of print - 29 Dec 2016
DatePublished (current) - 2016
Event2nd IET International Conference on Technologies for Active and Assisted Living (TechAAL 2016) - Savoy Place, London, United Kingdom
Duration: 24 Oct 201625 Oct 2016
Conference number: 2
http://www.theiet.org/events/tpn/techaal/index.cfm

Conference

Conference2nd IET International Conference on Technologies for Active and Assisted Living (TechAAL 2016)
Abbreviated titleTechAAL
CountryUnited Kingdom
CityLondon
Period24/10/1625/10/16
Internet address

Abstract

Estimating a person’s energy expenditure and activity intensity over time is an important component in managing various health conditions or tracking lifestyle choices. To implement an automatic estimation, most current systems ultimately require users to wear sensor devices. In contrast, this paper presents a framework for the contact-free, real-time estimation of energy expenditure, applicable to daily living scenarios. This is a new application in real-time computer vision. We demonstrate the effectiveness and the benefits of utilising a basic set of features and evaluate the resulting framework on the challenging SPHERE-calorie dataset. To ensure accurate evaluation, automated estimates are compared against a simultaneously taken indirect calorimetry ground truth based on per breath gas exchange. Following detailed experiments, we conclude that the proposed real-time vision pipeline is suitable for monitoring physical activity levels in a controlled environment with higher accuracy than the commonly used manual estimation via metabolic lookup tables (METs), whilst being significantly faster than existing automated methods.

    Research areas

  • Assistive monitoring, computer vision, energy expenditure, activities of daily living

    Structured keywords

  • Digital Health

Event

2nd IET International Conference on Technologies for Active and Assisted Living (TechAAL 2016)

Abbreviated titleTechAAL
Conference number2
Duration24 Oct 201625 Oct 2016
Location of eventSavoy Place
CityLondon
CountryUnited Kingdom
Web address (URL)
Degree of recognitionInternational event

Event: Conference

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Documents

Documents

  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via IEEE at http://ieeexplore.ieee.org/document/7801344/?arnumber=7801344. Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 935 KB, PDF document

DOI

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