research-article

Combining wearable accelerometer and physiological data for activity and energy expenditure estimation

Authors:

Julien Penders,

Oliver AmftAuthors Info & Claims

WH '13: Proceedings of the 4th Conference on Wireless Health

Article No.: 1, Pages 1 - 8

https://doi.org/10.1145/2534088.2534106

Published: 01 November 2013 Publication History

Abstract

Physical Activity (PA) is one of the most important determinants of health. Wearable sensors have great potential for accurate assessment of PA (activity type and Energy Expenditure (EE)) in daily life. In this paper we investigate the benefit of multiple physiological signals (Heart Rate (HR), respiration rate, Galvanic Skin Response (GSR), skin humidity) as well as accelerometer (ACC) data from two locations (wrist - combining ACC, GSR and skin humidity - and chest - combining ACC and HR) on PA type and EE estimation. We implemented single regression, activity recognition and activity-specific EE models on data collected from 16 subjects, while performing a set of PAs, grouped into six clusters (lying, sedentary, dynamic, walking, biking and running). Our results show that combining ACC and physiological signals improves performance for activity recognition (by 2 and 8% for the chest and wrist) and EE (by 36 - chest - and 35% - wrist - for single regression models, and by 18 - chest - and 46% - wrist - for activity-specific models). Physiological signals other than HR showed a coarser relation with level of physical exertion, resulting in being better predictors of activity cluster type and separation between inactivity and activity than EE, due to the weak correlation to EE within an activity cluster.

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https://doi.org/10.3390/s24103046
Gashi SMin CMontanari ASantini SKawsar F(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
https://doi.org/10.1038/s41597-022-01643-5
Sahoo RPrusty ARout ADas BSethi P(2022)Mental Stress Detection Using GSR Sensor Data with Filtering MethodsIntelligent Systems10.1007/978-981-19-0901-6_47(537-548)Online publication date: 4-May-2022
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Index Terms

Combining wearable accelerometer and physiological data for activity and energy expenditure estimation
1. Applied computing
  1. Life and medical sciences
    1. Consumer health
    2. Health informatics

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cover image ACM Other conferences

WH '13: Proceedings of the 4th Conference on Wireless Health

November 2013

91 pages

ISBN:9781450322904

DOI:10.1145/2534088

General Chairs:
Roozbeh Jafari
University of Texas at Dallas
,
Wendy Nilsen
National Institutes of Health

Copyright © 2013 ACM.

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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New York, NY, United States

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Published: 01 November 2013

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WH '13: Wireless Health 2013

November 1 - 3, 2013

Maryland, Baltimore

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WH '13 Paper Acceptance Rate 7 of 33 submissions, 21%;

Overall Acceptance Rate 35 of 139 submissions, 25%

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Cited By

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https://doi.org/10.3390/s24103046
Gashi SMin CMontanari ASantini SKawsar F(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
https://doi.org/10.1038/s41597-022-01643-5
Sahoo RPrusty ARout ADas BSethi P(2022)Mental Stress Detection Using GSR Sensor Data with Filtering MethodsIntelligent Systems10.1007/978-981-19-0901-6_47(537-548)Online publication date: 4-May-2022
https://doi.org/10.1007/978-981-19-0901-6_47
Poli AGabrielli VCiabattoni LSpinsante S(2021)Cross-Domain Classification of Physical Activity Intensity: An EDA-Based Approach Validated by Wrist-Measured Acceleration and Physiological DataElectronics10.3390/electronics1017215910:17(2159)Online publication date: 4-Sep-2021
https://doi.org/10.3390/electronics10172159
Sethi SSahoo R(2020)Design of WSN in Real Time Application of Health Monitoring SystemVirtual and Mobile Healthcare10.4018/978-1-5225-9863-3.ch032(643-658)Online publication date: 2020
https://doi.org/10.4018/978-1-5225-9863-3.ch032
Álvarez-García JCvetković BLuštrek M(2020)A Survey on Energy Expenditure Estimation Using Wearable DevicesACM Computing Surveys10.1145/340448253:5(1-35)Online publication date: 28-Sep-2020
https://dl.acm.org/doi/10.1145/3404482
Ingraham KRouse ERemy C(2020)Accelerating the Estimation of Metabolic Cost Using Signal Derivatives: Implications for Optimization and Evaluation of Wearable RobotsIEEE Robotics & Automation Magazine10.1109/MRA.2019.295410827:1(32-42)Online publication date: Mar-2020
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Chowdhury ATjondronegoro DChandran VZhang JTrost S(2019)Prediction of Relative Physical Activity Intensity Using Multimodal Sensing of Physiological DataSensors10.3390/s1920450919:20(4509)Online publication date: 17-Oct-2019
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Sankaran SLuyten KHansen DDendale PConinx K(2018)Have you met your METs?Proceedings of the 32nd International BCS Human Computer Interaction Conference10.14236/ewic/HCI2018.48(1-12)Online publication date: 4-Jul-2018
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Takeda MHirata YKatayama TMizuta YKoujina A(2018)State Estimation Using the CoG Candidates for Sit-to-Stand Support System UserIEEE Robotics and Automation Letters10.1109/LRA.2018.28495513:4(3011-3018)Online publication date: Oct-2018
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