Estimating Energy Expenditure with ActiGraph GT9X Inertial Measurement Unit
The purpose of this study was to explore whether gyroscope and magnetometer data from the ActiGraph GT9X improved accelerometer-based predictions of energy expenditure (EE).Methods
Thirty participants (mean ± SD: age, 23.0 ± 2.3 yr; body mass index, 25.2 ± 3.9 kg·m−2) volunteered to complete the study. Participants wore five GT9X monitors (right hip, both wrists, and both ankles) while performing 10 activities ranging from rest to running. A Cosmed K4b2 was worn during the trial, as a criterion measure of EE (30-s averages) expressed in METs. Triaxial accelerometer data (80 Hz) were converted to milli-G using Euclidean norm minus one (ENMO; 1-s epochs). Gyroscope data (100 Hz) were expressed as a vector magnitude (GVM) in degrees per second (1-s epochs) and magnetometer data (100 Hz) were expressed as direction changes per 5 s. Minutes 4–6 of each activity were used for analysis. Three two-regression algorithms were developed for each wear location: 1) ENMO, 2) ENMO and GVM, and 3) ENMO, GVM, and direction changes. Leave-one-participant-out cross-validation was used to evaluate the root mean square error (RMSE) and mean absolute percent error (MAPE) of each algorithm.Results
Adding gyroscope to accelerometer-only algorithms resulted in RMSE reductions between 0.0 METs (right wrist) and 0.17 METs (right ankle), and MAPE reductions between 0.1% (right wrist) and 6.0% (hip). When direction changes were added, RMSE changed by ≤0.03 METs and MAPE by ≤0.21%.Conclusions
The combined use of gyroscope and accelerometer at the hip and ankles improved individual-level prediction of EE compared with accelerometer only. For the wrists, adding gyroscope produced negligible changes. The magnetometer did not meaningfully improve estimates for any algorithms.