Artificial neural networks to predict activity type and energy expenditure in youth.
about
Movement prediction using accelerometers in a human population.Clinical Evaluation of the Measurement Performance of the Philips Health Watch: A Within-Person Comparative StudyIdentifying physical activity type in manual wheelchair users with spinal cord injury by means of accelerometers.Comparative evaluation of features and techniques for identifying activity type and estimating energy cost from accelerometer data.Correlations of Complete Blood Count with Alanine and Aspartate Transaminase in Chinese Subjects and Prediction Based on Back-Propagation Artificial Neural Network (BP-ANN).Predicting human movement with multiple accelerometers using moveletsBipart: Learning Block Structure for Activity DetectionA systematic review of intervention effects on potential mediators of children's physical activity.Light-intensity physical activity and cardiometabolic biomarkers in US adolescents.Accelerometer-derived sedentary and physical activity time in overweight/obese adults with type 2 diabetes: cross-sectional associations with cardiometabolic biomarkers.Artificial neural network modeling using clinical and knowledge independent variables predicts salt intake reduction behavior.Decision Trees for Detection of Activity Intensity in Youth with Cerebral Palsy.Validity of ActiGraph child-specific equations during various physical activities.A comparison of energy expenditure estimation of several physical activity monitors.Using accelerometers to measure physical activity in large-scale epidemiological studies: issues and challenges.Establishing and evaluating wrist cutpoints for the GENEActiv accelerometer in youth.Assessment of triglyceride and cholesterol in overweight people based on multiple linear regression and artificial intelligence model.Wrist Accelerometer Cut Points for Classifying Sedentary Behavior in Children.Measurement of Physical Activity and Energy Expenditure in Wheelchair Users: Methods, Considerations and Future Directions.A Review of Emerging Analytical Techniques for Objective Physical Activity Measurement in Humans.The Influence of Epoch Length on Physical Activity Patterns Varies by Child's Activity Level.Activity Recognition in Youth Using Single Accelerometer Placed at Wrist or Ankle.Neural network versus activity-specific prediction equations for energy expenditure estimation in children.Application of a tri-axial accelerometer to estimate jump frequency in volleyball.Validation of a wireless accelerometer network for energy expenditure measurement.Measurement of physical activity in children and adolescents with cerebral palsy: the way forward.Deep learning-based classification with improved time resolution for physical activities of children
P2860
Q27314795-A42F5D01-ED86-43A7-ADEE-A00A25A3A4CEQ29248007-275EBCF9-7FE4-42EF-8139-B362056729E1Q30567104-BDA117AD-FE44-4801-B795-DC840FB65AD6Q31044072-3E48AC74-11A5-49B4-8EC3-976122DEDE7BQ33843014-C03D0487-38F5-4F39-AB29-7B01672EBA51Q34063972-EE2C39F4-2111-4309-AE51-C42CE5267439Q34349384-DFCF09C4-AD21-46C0-B91D-851FAD8D51D3Q34596371-8F2573BC-B844-4D64-8C21-46AA13E7304CQ34949509-DA46F40C-7854-48E7-A66C-B292B1A71D17Q35182107-F073FF78-B033-45A1-8D50-55381204F477Q35671801-F36998AB-9838-43B4-B89C-8B503C618C06Q36803598-A6944E78-F5DB-41DB-A69A-9D067875FD81Q36941197-23CA03B0-71CB-4E5C-8D9E-75DB0D4FCE8EQ37239416-FE31953E-B1A3-4E92-AF26-605070252B82Q37625907-204E1FA0-27AB-4624-AC1B-E7C1F979FF3BQ37649802-2F646D4E-1E30-46C1-96C6-0B6971615CDEQ37655358-3C22C586-4CEE-4BCA-B171-BBF2E2AF5E38Q37674483-66207D49-B0FD-493B-B720-2FB3950B618EQ38751630-C8B1898B-D3E1-4661-8242-3A71F138DBC0Q38893368-A95D65B0-25DA-44DA-A86C-B43C2C354907Q38906367-E3ECD1AD-9351-437E-BB37-AEE62CB09914Q39208580-3EA1D950-F999-4A20-9EDE-8C4307D1FA82Q39358883-E77989CD-4C34-4B62-B205-B4955BAF8536Q41028278-04123312-B037-4879-8A8D-F452E9768659Q45952021-8377DD4F-EC8D-43AF-97FF-78070F942903Q46683744-1FF7AB75-5881-403A-9C39-E56F9ABB041EQ58589814-EC8BEB51-0CEF-4EA0-827B-BCE19FF37ACA
P2860
Artificial neural networks to predict activity type and energy expenditure in youth.
description
2012 nî lūn-bûn
@nan
2012年の論文
@ja
2012年論文
@yue
2012年論文
@zh-hant
2012年論文
@zh-hk
2012年論文
@zh-mo
2012年論文
@zh-tw
2012年论文
@wuu
2012年论文
@zh
2012年论文
@zh-cn
name
Artificial neural networks to predict activity type and energy expenditure in youth.
@ast
Artificial neural networks to predict activity type and energy expenditure in youth.
@en
type
label
Artificial neural networks to predict activity type and energy expenditure in youth.
@ast
Artificial neural networks to predict activity type and energy expenditure in youth.
@en
prefLabel
Artificial neural networks to predict activity type and energy expenditure in youth.
@ast
Artificial neural networks to predict activity type and energy expenditure in youth.
@en
P2860
P1476
Artificial neural networks to predict activity type and energy expenditure in youth.
@en
P2093
Karen A Pfeiffer
Yonglei Zheng
P2860
P304
P356
10.1249/MSS.0B013E318258AC11
P577
2012-09-01T00:00:00Z