Literature DB >> 21690001

Determining energy expenditure from treadmill walking using hip-worn inertial sensors: an experimental study.

Harshvardhan Vathsangam1, Adar Emken, E Todd Schroeder, Donna Spruijt-Metz, Gaurav S Sukhatme.   

Abstract

We describe an experimental study to estimate energy expenditure during treadmill walking using a single hip-mounted inertial sensor (triaxial accelerometer and triaxial gyroscope). Typical physical-activity characterization using commercial monitors use proprietary counts that do not have a physically interpretable meaning. This paper emphasizes the role of probabilistic techniques in conjunction with inertial data modeling to accurately predict energy expenditure for steady-state treadmill walking. We represent the cyclic nature of walking with a Fourier transform and show how to map this representation to energy expenditure (VO(2), mL/min) using three regression techniques. A comparative analysis of the accuracy of sensor streams in predicting energy expenditure reveals that using triaxial information leads to more accurate energy-expenditure prediction compared to only using one axis. Combining accelerometer and gyroscope information leads to improved accuracy compared to using either sensor alone. Nonlinear regression methods showed better prediction accuracy compared to linear methods but required an order of higher magnitude run time.

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Mesh:

Year:  2011        PMID: 21690001      PMCID: PMC3179538          DOI: 10.1109/TBME.2011.2159840

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  20 in total

1.  Compendium of physical activities: an update of activity codes and MET intensities.

Authors:  B E Ainsworth; W L Haskell; M C Whitt; M L Irwin; A M Swartz; S J Strath; W L O'Brien; D R Bassett; K H Schmitz; P O Emplaincourt; D R Jacobs; A S Leon
Journal:  Med Sci Sports Exerc       Date:  2000-09       Impact factor: 5.411

Review 2.  Accelerometry: providing an integrated, practical method for long-term, ambulatory monitoring of human movement.

Authors:  Merryn J Mathie; Adelle C F Coster; Nigel H Lovell; Branko G Celler
Journal:  Physiol Meas       Date:  2004-04       Impact factor: 2.833

3.  A novel method for using accelerometer data to predict energy expenditure.

Authors:  Scott E Crouter; Kurt G Clowers; David R Bassett
Journal:  J Appl Physiol (1985)       Date:  2005-12-01

4.  Translating accelerometer counts into energy expenditure: advancing the quest.

Authors:  Richard P Troiano
Journal:  J Appl Physiol (1985)       Date:  2006-04

Review 5.  Health benefits of physical activity: the evidence.

Authors:  Darren E R Warburton; Crystal Whitney Nicol; Shannon S D Bredin
Journal:  CMAJ       Date:  2006-03-14       Impact factor: 8.262

6.  An artificial neural network model of energy expenditure using nonintegrated acceleration signals.

Authors:  Megan P Rothney; Megan Neumann; Ashley Béziat; Kong Y Chen
Journal:  J Appl Physiol (1985)       Date:  2007-07-19

Review 7.  Accelerometer assessment of physical activity in children: an update.

Authors:  Ann V Rowlands
Journal:  Pediatr Exerc Sci       Date:  2007-08       Impact factor: 2.333

Review 8.  Physical activity assessment with accelerometers: an evaluation against doubly labeled water.

Authors:  Guy Plasqui; Klaas R Westerterp
Journal:  Obesity (Silver Spring)       Date:  2007-10       Impact factor: 5.002

9.  Predicting activity energy expenditure using the Actical activity monitor.

Authors:  Daniel P Heil
Journal:  Res Q Exerc Sport       Date:  2006-03       Impact factor: 2.500

10.  Validation of Actigraph accelerometer estimates of total energy expenditure in young children.

Authors:  John J Reilly; Louise A Kelly; Colette Montgomery; Diane M Jackson; Christine Slater; Stan Grant; James Y Paton
Journal:  Int J Pediatr Obes       Date:  2006
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  8 in total

1.  Hierarchical Linear Models for Energy Prediction using Inertial Sensors: A Comparative Study for Treadmill Walking.

Authors:  Harshvardhan Vathsangam; B Adar Emken; E Todd Schroeder; Donna Spruijt-Metz; Gaurav S Sukhatme
Journal:  J Ambient Intell Humaniz Comput       Date:  2013-12-01

2.  Using Smartphone Sensors for Improving Energy Expenditure Estimation.

Authors:  Amit Pande; Jindan Zhu; Aveek K Das; Yunze Zeng; Prasant Mohapatra; Jay J Han
Journal:  IEEE J Transl Eng Health Med       Date:  2015-09-18       Impact factor: 3.316

3.  The potential of artificial intelligence in enhancing adult weight loss: a scoping review.

Authors:  Han Shi Jocelyn Chew; Wei How Darryl Ang; Ying Lau
Journal:  Public Health Nutr       Date:  2021-02-17       Impact factor: 4.022

4.  Identifying typical physical activity on smartphone with varying positions and orientations.

Authors:  Fen Miao; Yi He; Jinlei Liu; Ye Li; Idowu Ayoola
Journal:  Biomed Eng Online       Date:  2015-04-13       Impact factor: 2.819

5.  Regression Model-Based Walking Speed Estimation Using Wrist-Worn Inertial Sensor.

Authors:  Shaghayegh Zihajehzadeh; Edward J Park
Journal:  PLoS One       Date:  2016-10-20       Impact factor: 3.240

6.  Quantifying Human Movement Using the Movn Smartphone App: Validation and Field Study.

Authors:  Ralph Maddison; Luke Gemming; Javier Monedero; Linda Bolger; Sarahjane Belton; Johann Issartel; Samantha Marsh; Artur Direito; Madeleine Solenhill; Jinfeng Zhao; Daniel John Exeter; Harshvardhan Vathsangam; Jonathan Charles Rawstorn
Journal:  JMIR Mhealth Uhealth       Date:  2017-08-17       Impact factor: 4.773

7.  Intelligent predictor of energy expenditure with the use of patch-type sensor module.

Authors:  Meina Li; Keun-Chang Kwak; Youn-Tae Kim
Journal:  Sensors (Basel)       Date:  2012-10-25       Impact factor: 3.576

8.  Estimation of Energy Expenditure Using a Patch-Type Sensor Module with an Incremental Radial Basis Function Neural Network.

Authors:  Meina Li; Keun-Chang Kwak; Youn Tae Kim
Journal:  Sensors (Basel)       Date:  2016-09-22       Impact factor: 3.576

  8 in total

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