Other

  • Brage, 2003- branched and not branched models to predict energy expenditure from hip-worn CSA accelerometer counts/min and/or heart rate in adult males

  • Brandes, 2012- activity-specific linear regression equations for predicting energy expenditure during walking, cycling, and stair walking in children and adults using acceleration measured by a Dynaport on the lower back

  • Chuang, 2013- single regression, activity-specific regression, and a mono-exponential equation to predict energy expenditure in adults wearing accelerometers on the wrist and ankle

  • Curone, 2010- cut-points for signal magnitude area using ADXL330 on upper part of trunk

  • Duclos, 2015- equation to predict total energy expenditure from smartphone accelerometers in adults

  • Hiremath, 2012- a general equation and a set of four activity-specific regression equations for predicting energy expenditure of manual wheelchair users using a Sensewear armband

  • Horner, 2012- models for predicting total energy expenditure and physical activity energy expenditure for males and females during free-living using a 3dnx on the lower back

  • Jang, 2006- combination of 7 accelerometers (15 total axes) is used to predict energy expenditure in adults using a simple calculation converting acceleration into velocity and work

  • Kim, 2008- three linear regression models to predict energy expenditure in adults using waist, ankle, and wrist accelerometers

  • 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

  • Mehta- cut-points for activity intensity from raw acceleration

  • Nguyen, 2013- data from three monitors - a Lifecorder accelerometer at the waist, Step Watch 3 at the ankle, and a GPS unit - are combined to predict energy expenditure in adults

  • Nolan, 2014- acceleration measured by an iPod touch worn on the lower back is used to determine speed and grade of walking or running which are used to calculate energy expenditure based on pre-existing equations in adults

  • Tanaka, 2007- linear and non-linear equations were developed to predict energy expenditure in young children wearing an ActivTracer on the left hip

  • Vaha-Ypya, 2015- equations to predict energy expenditure and activity intensity cut-points for mean amplitude deviation were determined from adults wearing a hip-worn Hookie monitor

  • Vaha-Ypya, 2015 (2)- cut-points for mean amplitude deviation (MAD) that can be used for any accelerometer brand were determined from adults wearing three monitors at the hip

  • Weippert, 2013- four models to predict energy expenditure in adults wearing a chest-worn accelerometer

  • Yamazaki, 2009- model to predict energy expenditure during walking in adults wearing a waist-worn accelerometer