Assessing equivalence in raw accelerometer outputs across different brands using shaker table validation: a comparative analysis with filtering and linear regression techniques

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Authors: Hannah J. Coyle-Asbil, Bernadette Murphy, Lori Ann Vallis
Cited by: 0
References: 31

Abstract

This study had two major aims, first to determine whether the raw acceleration data from ActiGraph versus non-ActiGraph accelerometers were equivalent and, second to apply and compare different equivalency approaches: 1) linear regression, 2) lowpass 15 Hz, 3) lowpass 20 Hz, and 4) lowpass 30 Hz filters. ActiGraph GT3X+ (n = 8), ActiGraph wGT3X-BT (n = 10), ActiGraph GT9X (n = 8; primary and GT9X2), OPAL (n = 6) and GENEActiv (n = 5) accelerometers were affixed to a multi-axis shaker table and sinusoidal oscillations were introduced, spanning the entire dynamic range (±0.005 G to ± 8 G). The averages of the trials were compared according to the different techniques. Linear regression models were fitted to align the non-ActiGraph to the ActiGraph data, and lowpass filters were applied to the non-Actigraph data. Equivalency was assessed using two one sided t-tests of equivalence. The results indicated either statistically insignificant or negligible mean differences at the lower frequency oscillations, however at higher oscillations the recordings were significantly distinct and vastly varied across the different techniques. For example, the mean difference between the wGT3X-BT − GA at trial 28 unprocessed was −2788.09 m g , −394.93 m g for the linear regression, 1358.56 m g for the 15 Hz filter, and 182.94 m g for both the 20 Hz and 30 Hz filters. In conclusion, there are minor differences between ActiGraph and non-ActiGraph accelerometers at low frequencies; however, at high frequencies, 20 Hz low-pass filters are effective in improving equivalency. Our findings provide insight into device equivalency and important guidance for researchers to consider when harmonizing data across accelerometer devices.

Fields of Study & Subjects

Physical Activity and Health Context-Aware Activity Recognition Systems Balance Gait and Falls Prevention

Publication details

Journal / source
Biomedical Signal Processing and Control
Volume
119
Pages
109627–109627
Publication date
12 February 2026
Language
EN