Estimating Energy Expenditure with ActiGraph GT9X Inertial Measurement Unit

    loading  Checking for direct PDF access through Ovid

Abstract

Purpose

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.

    loading  Loading Related Articles