Wrist-independent energy expenditure prediction models from raw accelerometer data.

PURPOSES: (1) Develop artificial neural network (ANN) models for wrist accelerometer data which can predict energy expenditure (EE) using data collected from either wrist. (2) Develop ANNs for detecting the wrist on which the accelerometer was worn. Forty-four adults wore GENEActiv accelerometers on the left and right wrists and a portable metabolic analyzer while participating in a 90 min semi-structured activity protocol. Participants performed 14 sedentary, lifestyle, exercise, and ambulatory activities and were allowed to choose activity order, duration, and intensity. ANNs were created to predict EE and wrist detection using a leave-one-out cross-validation. In total, 12 combinations of feature sets (mean and variance of raw, vector magnitude, and absolute value data), training methods (left- and right- wrist), and testing methods (left- and right-wrist data) were used to develop EE prediction ANNs. Accuracy of the ANNs was evaluated using correlations, root mean square error (RMSE), and bias, using metabolic analyzer data as the criterion for EE. ANNs using raw data from the same wrist (e.g. EE predicted from right wrist ANNs using accelerometer data from right wrist) had the highest accuracy for EE prediction (r  =  0.84, RMSE  =  1.25-1.26 METs); conversely, opposite-wrist prediction accuracy (e.g. EE predicted from right wrist ANNs using accelerometer data from left wrist) was lower (r  =  0.60-0.64, RMSE  =  1.93-2.01 METs). Preprocessing into absolute values prior to ANN development allowed for, high EE prediction accuracy, with no difference in accuracy for same- versus opposite-wrist prediction (r  =  0.80-0.83, RMSE  =  1.30-1.49 METs). Wrist detection ANNs correctly determined wrist placement 100% of the time. Highly accurate, wrist-independent EE prediction ANNs were developed by computing absolute values of raw acceleration data prior to ANN development. This method provides a potential approach for advancing predictive accuracy of wrist-worn accelerometers.