An artificial neural network model of energy expenditure using nonintegrated acceleration signals.
about
Redefining the roles of sensors in objective physical activity monitoringEvaluation of artificial neural network algorithms for predicting METs and activity type from accelerometer data: validation on an independent sampleA method to estimate free-living active and sedentary behavior from an accelerometer.Physical activity monitoring in obese people in the real life environmentDistributed lag and spline modeling for predicting energy expenditure from accelerometry in youthComparing 3 dietary pattern methods--cluster analysis, factor analysis, and index analysis--With colorectal cancer risk: The NIH-AARP Diet and Health StudyComparison of the ActiGraph 7164 and the ActiGraph GT1M during self-paced locomotion.Issues in accelerometer methodology: the role of epoch length on estimates of physical activity and relationships with health outcomes in overweight, post-menopausal women.Validation of the ActiGraph two-regression model for predicting energy expenditure.Comparing metabolic energy expenditure estimation using wearable multi-sensor network and single accelerometer.Use of a Wireless Network of Accelerometers for Improved Measurement of Human Energy Expenditure.Determining energy expenditure from treadmill walking using hip-worn inertial sensors: an experimental studyValidation of five minimally obstructive methods to estimate physical activity energy expenditure in young adults in semi-standardized settingsAssessment of physical activity using wearable monitors: recommendations for monitor calibration and use in the field.Calibrating a novel multi-sensor physical activity measurement systemCalibration and validation of wearable monitors.Methods to estimate aspects of physical activity and sedentary behavior from high-frequency wrist accelerometer measurements.Incorporating an Exercise Detection, Grading, and Hormone Dosing Algorithm Into the Artificial Pancreas Using Accelerometry and Heart Rate.Best practices for using physical activity monitors in population-based research.Ngram time series model to predict activity type and energy cost from wrist, hip and ankle accelerometers: implications of age.Estimating activity and sedentary behavior from an accelerometer on the hip or wristAssessment of physical activity in youth.Support vector machines classifiers of physical activities in preschoolers.An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer.Hierarchical Linear Models for Energy Prediction using Inertial Sensors: A Comparative Study for Treadmill Walking.Advances in physical activity monitoring and lifestyle interventions in obesity: a review.Computational methods for estimating energy expenditure in human physical activities.Measuring physical activity in children and adolescents for dietary surveys: practicalities, problems and pitfalls.Multivariate adaptive regression splines models for the prediction of energy expenditure in children and adolescents.Usefulness of motion sensors to estimate energy expenditure in children and adults: a narrative review of studies using DLW.Actigraphy features for predicting mobility disability in older adults.Machine learning methods for classifying human physical activity from on-body accelerometers.Improving assessment of daily energy expenditure by identifying types of physical activity with a single accelerometer.Design of a wearable physical activity monitoring system using mobile phones and accelerometers.Validation of a wireless accelerometer network for energy expenditure measurement.Energy expenditure estimation from respiration variables.The utility of heart rate and minute ventilation as predictors of whole-body metabolic rate during occupational simulations involving load carriage.A systematic literature review of reviews on techniques for physical activity measurement in adults: a DEDIPAC study.Cardiorespiratory fitness estimation using wearable sensors: Laboratory and free-living analysis of context-specific submaximal heart rates.Improved estimation of energy expenditure by artificial neural network modeling.
P2860
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P2860
An artificial neural network model of energy expenditure using nonintegrated acceleration signals.
description
2007 nî lūn-bûn
@nan
2007年の論文
@ja
2007年学术文章
@wuu
2007年学术文章
@zh
2007年学术文章
@zh-cn
2007年学术文章
@zh-hans
2007年学术文章
@zh-my
2007年学术文章
@zh-sg
2007年學術文章
@yue
2007年學術文章
@zh-hant
name
An artificial neural network m ...... tegrated acceleration signals.
@en
An artificial neural network m ...... tegrated acceleration signals.
@nl
type
label
An artificial neural network m ...... tegrated acceleration signals.
@en
An artificial neural network m ...... tegrated acceleration signals.
@nl
prefLabel
An artificial neural network m ...... tegrated acceleration signals.
@en
An artificial neural network m ...... tegrated acceleration signals.
@nl
P2093
P2860
P1476
An artificial neural network m ...... tegrated acceleration signals.
@en
P2093
Ashley Béziat
Megan Neumann
Megan P Rothney
P2860
P304
P356
10.1152/JAPPLPHYSIOL.00429.2007
P407
P50
P577
2007-07-19T00:00:00Z