Wrist
Ahmadi, 2020- four machine learning models to predict energy expenditure of preschoolers from raw acceleration features from hip or non-dominant wrist-worn ActiGraph accelerometers
Bammann, 2021- cut-points for Euclidean norm minus one for monitors worn at each hip, wrist, and ankle in older adults
Bianchim, 2022- cut-points for Euclidean norm minus one and mean amplitude deviation for monitors worn at the right hip and each wrist in children and adolescents with and without cystic fibrosis
Choi, 2010- five prediction models for energy expenditure in adolescents using ActiGraph counts from the hip, wrist, or ankle
Crotti, 2020- cut-points for Euclidean norm minus one from ActiGraph worn on right hip and each wrist in children
Dibben, 2020- cut-points for two acceleration-based metrics (sum of vector magnitudes and mean amplitude deviation) for GENEActiv monitors worn at the hip and each wrist in patients with heart failure
Dillon, 2016- cut-points for signal vector magnitude from GENEActiv worn on each wrist in adults
Ellingson, 2017- modification of the Hildebrand et al. (2014) model to predict energy expenditure in adults using Euclidean norm minus one from an ActiGraph worn on the right wrist
Esliger, 2011- cut-points for signal magnitude vector from GENEA worn on right hip and each wrist in adults
Fraysse, 2021- cut-points for signal vector magnitude from GENEActiv worn on each wrist in older adults
Heil, 2006- single and 2-regression models for predicting activity energy expenditure in children and adults wearing an Actical accelerometer on the right hip or non-dominant ankle or wrist
Hibbing, 2018- four Sojourn models to predict energy expenditure of children using hip- or wrist-worn ActiGraph counts or Euclidean norm minus one
Hibbing, 2018 (2)- fifteen 2-regression models to predict energy expenditure in adults for an ActiGraph worn at the hip, each wrist and ankle using Euclidean norm minus one, gyroscope vector magnitude, and direction changes
Hildebrand, 2014- calculates Euclidean norm minus one from raw acceleration data of hip or non-dominant wrist-worn ActiGraph or GENEActivs with energy expenditure equations and activity intensity cut-points for children and adults
Kiuchi, 2014- twelve total models (three feature sets at four wear locations including both wrists and upper arms) were developed for predicting energy expenditure using acceleration and gyroscopic angular velocity in manual wheelchair users
Mackintosh, 2016- thirteen machine learning models to predict energy expenditure in children using count data from ActiGraphs on chest, both hips, wrists, ankles, and knees
Migueles, 2021- cut-points for activity intensity using Euclidean norm minus measured at the hip or either wrist in older adults
Montoye, 2015- sixteen acceleration-based machine learning models to predict energy expenditure of adults wearing ActiGraph on thigh or hip or a GENEActiv on either wrist
Montoye, 2016 (2)- four acceleration-based machine learning models to predict energy expenditure of adults wearing ActiGraph on thigh or hip or GENEActiv on either wrist
Montoye, 2016 (3)- six acceleration-based machine learning models to predict energy expenditure of adults wearing GENEActiv on either wrist
Montoye, 2017- four acceleration-based machine learning models to predict energy expenditure of adults wearing ActiGraph on ankle, hip, or either wrist
Montoye, 2018- four machine learning models to predict energy expenditure of adults wearing GENEActiv on left wrist
Montoye, 2019- six machine learning models using either count or raw data from ActiGraphs on right hip or left wrist to predict energy expenditure of children
Phillips, 2013- cut-points for signal magnitude vector from GENEA worn on right hip and each wrist in children
Roscoe, 2017- cut-points for signal magnitude vector from GENEActiv worn on non-dominant wrist in preschool-aged children
Sanders, 2019- cut-points for Euclidean norm minus one from ActiGraph worn on right hip and GENEActiv worn on non-dominant wrist in older adults
Schaefer, 2014- cut-points for signal magnitude vector divided by sampling frequency from GENEActiv worn on non-dominant wrist in children
Staudenmayer, 2015- linear regression and decision tree to predict energy expenditure or activity intensity, respectively, from dominant wrist-worn ActiGraphs in adults
Steenbock, 2019- twenty-four machine learning models to predict energy expenditure of preschoolers using acceleration metrics from an ActiGraph on either hip, GENEActiv right hip, GENEActiv on either wrist, and/or ActivPal right thigh
van Hees, 2011- prediction equations for energy expenditure for pregnant women and non-pregnant women wearing a GENEA on the wrist or non-dominant hip
van Hees, 2013- prediction equations for energy expenditure of women wearing a wrist-worn GENEA