Pages that link to "Q36173783"

The following pages link to Artificial neural networks to predict activity type and energy expenditure in youth (Q36173783):

Displaying 41 items.

• Movement prediction using accelerometers in a human population (Q27314795) (← links)
• Clinical Evaluation of the Measurement Performance of the Philips Health Watch: A Within-Person Comparative Study (Q29248007) (← links)
• Identifying physical activity type in manual wheelchair users with spinal cord injury by means of accelerometers (Q30567104) (← links)
• Comparative evaluation of features and techniques for identifying activity type and estimating energy cost from accelerometer data (Q31044072) (← links)
• Correlations of Complete Blood Count with Alanine and Aspartate Transaminase in Chinese Subjects and Prediction Based on Back-Propagation Artificial Neural Network (BP-ANN). (Q33843014) (← links)
• Predicting human movement with multiple accelerometers using movelets (Q34063972) (← links)
• Bipart: Learning Block Structure for Activity Detection (Q34349384) (← links)
• A systematic review of intervention effects on potential mediators of children's physical activity (Q34596371) (← links)
• Light-intensity physical activity and cardiometabolic biomarkers in US adolescents (Q34949509) (← links)
• Accelerometer-derived sedentary and physical activity time in overweight/obese adults with type 2 diabetes: cross-sectional associations with cardiometabolic biomarkers (Q35182107) (← links)
• Artificial neural network modeling using clinical and knowledge independent variables predicts salt intake reduction behavior (Q35671801) (← links)
• Decision Trees for Detection of Activity Intensity in Youth with Cerebral Palsy (Q36803598) (← links)
• Validity of ActiGraph child-specific equations during various physical activities (Q36941197) (← links)
• A comparison of energy expenditure estimation of several physical activity monitors (Q37239416) (← links)
• Using accelerometers to measure physical activity in large-scale epidemiological studies: issues and challenges (Q37625907) (← links)
• Establishing and evaluating wrist cutpoints for the GENEActiv accelerometer in youth. (Q37649802) (← links)
• Assessment of triglyceride and cholesterol in overweight people based on multiple linear regression and artificial intelligence model (Q37655358) (← links)
• Wrist Accelerometer Cut Points for Classifying Sedentary Behavior in Children. (Q37674483) (← links)
• Measurement of Physical Activity and Energy Expenditure in Wheelchair Users: Methods, Considerations and Future Directions. (Q38751630) (← links)
• A Review of Emerging Analytical Techniques for Objective Physical Activity Measurement in Humans. (Q38893368) (← links)
• The Influence of Epoch Length on Physical Activity Patterns Varies by Child's Activity Level (Q38906367) (← links)
• Activity Recognition in Youth Using Single Accelerometer Placed at Wrist or Ankle. (Q39208580) (← links)
• Neural network versus activity-specific prediction equations for energy expenditure estimation in children. (Q39358883) (← links)
• Application of a tri-axial accelerometer to estimate jump frequency in volleyball (Q41028278) (← links)
• Validation of a wireless accelerometer network for energy expenditure measurement. (Q45952021) (← links)
• Measurement of physical activity in children and adolescents with cerebral palsy: the way forward (Q46683744) (← links)
• Wrist Acceleration Cut-points for Moderate-to-Vigorous Physical Activity in Youth. (Q47653758) (← links)
• Estimating physical activity in youth using an ankle accelerometer (Q57142840) (← links)
• Deep learning-based classification with improved time resolution for physical activities of children (Q58589814) (← links)
• Machine learning algorithms for activity recognition in ambulant children and adolescents with cerebral palsy (Q59340238) (← links)
• A Machine Learning Approach to Measure and Monitor Physical Activity in Children to Help Fight Overweight and Obesity (Q59589493) (← links)
• Identifying Free-Living Physical Activities Using Lab-Based Models with Wearable Accelerometers (Q59811063) (← links)
• Predicting ambulatory energy expenditure in lower limb amputees using multi-sensor methods (Q61805515) (← links)
• Advances and Controversies in Diet and Physical Activity Measurement in Youth (Q62494494) (← links)
• Measuring Caloric Intake at the Population Level (NOTION): Protocol for an Experimental Study (Q62716822) (← links)
• Population-level physical activity surveillance in young people: are accelerometer-based measures ready for prime time? (Q90403303) (← links)
• Calibration and Validation of the Youth Activity Profile as a Physical Activity and Sedentary Behaviour Surveillance Tool for English Youth (Q90461130) (← links)
• Compositional analyses of the associations between sedentary time, different intensities of physical activity, and cardiometabolic biomarkers among children and youth from the United States (Q92072991) (← links)
• Bidirectional, Daily Temporal Associations between Sleep and Physical Activity in Adolescents (Q92226786) (← links)
• Neural Network Ensembles for Sensor-Based Human Activity Recognition Within Smart Environments (Q92443975) (← links)
• Prediction of 30-Day Readmission for COPD Patients Using Accelerometer-Based Activity Monitoring (Q92444004) (← links)