A random forest classifier for the prediction of energy expenditure and type of physical activity from wrist and hip accelerometers.
about
Accelerometer Data Collection and Processing Criteria to Assess Physical Activity and Other Outcomes: A Systematic Review and Practical Considerations.Prying into the intimate secrets of animal lives; software beyond hardware for comprehensive annotation in 'Daily Diary' tags.An Activity Index for Raw Accelerometry Data and Its Comparison with Other Activity Metrics.Refining Time-Activity Classification of Human Subjects Using the Global Positioning SystemMethods to estimate aspects of physical activity and sedentary behavior from high-frequency wrist accelerometer measurements.Decision Trees for Detection of Activity Intensity in Youth with Cerebral Palsy.Objective Assessment of Physical Activity: Classifiers for Public Health.How Accurately Can Your Wrist Device Recognize Daily Activities and Detect Falls?Developing and Evaluating Digital Interventions to Promote Behavior Change in Health and Health Care: Recommendations Resulting From an International Workshop.Usefulness of motion sensors to estimate energy expenditure in children and adults: a narrative review of studies using DLW.Performance Evaluation of State of the Art Systems for Physical Activity Classification of Older Subjects Using Inertial Sensors in a Real Life Scenario: A Benchmark Study.Comparison of children's free-living physical activity derived from wrist and hip raw accelerations during the segmented week.Comparison of Accelerometry Methods for Estimating Physical Activity.Actigraphy features for predicting mobility disability in older adults.Physical activity using wrist-worn accelerometers: comparison of dominant and non-dominant wrist.Performance of thigh-mounted triaxial accelerometer algorithms in objective quantification of sedentary behaviour and physical activity in older adults.Sitting Posture Monitoring System Based on a Low-Cost Load Cell Using Machine Learning.Improving Hip-Worn Accelerometer Estimates of Sitting Using Machine Learning Methods.An Activity Recognition Framework Deploying the Random Forest Classifier and A Single Optical Heart Rate Monitoring and Triaxial AccelerometerWrist-Band.Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants.Correlates of accelerometer-assessed physical activity in pregnancy-The 2015 Pelotas (Brazil) Birth Cohort StudyFusion of Heart Rate, Respiration and Motion Measurements from a Wearable Sensor System to Enhance Energy Expenditure EstimationApplication and Validation of Activity Monitors' Epoch Lengths and Placement Sites for Physical Activity Assessment in ExergamingTimeClassifier: a visual analytic system for the classification of multi-dimensional time series data
P2860
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P2860
A random forest classifier for the prediction of energy expenditure and type of physical activity from wrist and hip accelerometers.
description
2014 nî lūn-bûn
@nan
2014 թուականի Հոկտեմբերին հրատարակուած գիտական յօդուած
@hyw
2014 թվականի հոտեմբերին հրատարակված գիտական հոդված
@hy
2014年の論文
@ja
2014年論文
@yue
2014年論文
@zh-hant
2014年論文
@zh-hk
2014年論文
@zh-mo
2014年論文
@zh-tw
2014年论文
@wuu
name
A random forest classifier for ...... wrist and hip accelerometers.
@ast
A random forest classifier for ...... wrist and hip accelerometers.
@en
type
label
A random forest classifier for ...... wrist and hip accelerometers.
@ast
A random forest classifier for ...... wrist and hip accelerometers.
@en
prefLabel
A random forest classifier for ...... wrist and hip accelerometers.
@ast
A random forest classifier for ...... wrist and hip accelerometers.
@en
P2093
P2860
P356
P1476
A random forest classifier for ...... wrist and hip accelerometers.
@en
P2093
David Wing
Gert Lanckriet
Jacqueline Kerr
Katherine Ellis
Simon Marshall
Suneeta Godbole
P2860
P304
P356
10.1088/0967-3334/35/11/2191
P577
2014-10-23T00:00:00Z