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Using wearable activity type detection to improve physical activity energy expenditure estimation

Published: 26 September 2010 Publication History

Abstract

Accurate, real-time measurement of energy expended during everyday activities would enable development of novel health monitoring and wellness technologies. A technique using three miniature wearable accelerometers is presented that improves upon state-of-the-art energy expenditure (EE) estimation. On a dataset acquired from 24 subjects performing gym and household activities, we demonstrate how knowledge of activity type, which can be automatically inferred from the accelerometer data, can improve EE estimates by more than 15% when compared to the best estimates from other methods.

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    cover image ACM Conferences
    UbiComp '10: Proceedings of the 12th ACM international conference on Ubiquitous computing
    September 2010
    366 pages
    ISBN:9781605588438
    DOI:10.1145/1864349
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 26 September 2010

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    Author Tags

    1. accelerometer
    2. activity recognition
    3. energy expenditure
    4. health
    5. physical activity
    6. wearable
    7. wireless

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    Ubicomp '10
    Ubicomp '10: The 2010 ACM Conference on Ubiquitous Computing
    September 26 - 29, 2010
    Copenhagen, Denmark

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    UbiComp '10 Paper Acceptance Rate 39 of 202 submissions, 19%;
    Overall Acceptance Rate 764 of 2,912 submissions, 26%

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    • (2024)M3BAT: Unsupervised Domain Adaptation for Multimodal Mobile Sensing with Multi-Branch Adversarial TrainingProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36595918:2(1-30)Online publication date: 15-May-2024
    • (2022)An Intelligent Healthcare System for Residential Aged Care during the COVID-19 PandemicApplied Sciences10.3390/app12221184712:22(11847)Online publication date: 21-Nov-2022
    • (2022)Should I take a walk? Estimating Energy Expenditure from Video Data2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW56347.2022.00225(2074-2084)Online publication date: Jun-2022
    • (2022)A multidevice and multimodal dataset for human energy expenditure estimation using wearable devicesScientific Data10.1038/s41597-022-01643-59:1Online publication date: 1-Sep-2022
    • (2021)Research on Energy Cost of Human Body Exercise at Different Running SpeedProceedings of the 11th International Conference on Computer Engineering and Networks10.1007/978-981-16-6554-7_48(430-436)Online publication date: 12-Nov-2021
    • (2020)A Survey on Energy Expenditure Estimation Using Wearable DevicesACM Computing Surveys10.1145/340448253:5(1-35)Online publication date: 28-Sep-2020
    • (2020)Estimating Upper-Limb Impairment Level in Stroke Survivors Using Wearable Inertial Sensors and a Minimally-Burdensome Motor TaskIEEE Transactions on Neural Systems and Rehabilitation Engineering10.1109/TNSRE.2020.296695028:3(601-611)Online publication date: Mar-2020
    • (2020)Sensors Capabilities, Performance, and Use of Consumer Sleep TechnologySleep Medicine Clinics10.1016/j.jsmc.2019.11.00315:1(1-30)Online publication date: Mar-2020
    • (2019)Physical Workload Tracking Using Human Activity Recognition with Wearable DevicesSensors10.3390/s2001003920:1(39)Online publication date: 19-Dec-2019
    • (2019)Behavio2AuthProceedings of the ArabWIC 6th Annual International Conference Research Track10.1145/3333165.3333176(1-6)Online publication date: 7-Mar-2019
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