Physical activity measurement considerations: Development and validation of new algorithms for wrist worn accelerometers

Abstract: This investigation developed models to estimate aspects of physical activity and sedentary behavior from three-axis high frequency wrist worn accelerometer data. The models were developed and tested on twenty participants (n=10 males, n=10 females, mean age= 24.1, mean BMI = 23.9) who wore an ActiGraph GT3X+ accelerometer on their dominant wrist and an ActiGraph GT3X on the hip while performing a variety of scripted activities. Energy expenditure was concurrently measured by a portable indirect calorimetry system. Those calibration data were then used to develop and assess both machine learning and simpler models with fewer unknown parameters (linear regression and decision trees) to estimate METs and to classify activity intensity, sedentary time, and locomotion time. The wrist models, applied to 15-second windows, estimated METs (random forest: rMSE = 1.21 METs, hip: rMSE = 1.67 METs) and activity intensity (random forest: 75% correct, hip: 60% correct)better than a previously developed model that used counts per minute measured at the hip. In a separate set of comparisons, the simpler decision trees classified activity intensity (random forest: 75% correct, tree: 74% correct), sedentary time (random forest: 96% correct, decision tree: 97% correct), and locomotion time (random forest: 99% correct, decision tree: 96% correct), nearly as well or better than the machine learning approaches. Preliminary investigation of the models’ performance on two free-living people suggests that they may work well outside of controlled conditions.