These monitors have a dynamic range of ☖ g and allow users to collect raw acceleration at various sampling frequencies ranging from 10 to 160 Hz at 10 Hz increments. Two activity monitors used in physical activity research are the ActiGraph™ GT3X+ and GENEA (Unilever Discover, Colworth, UK). While raw accelerometry is a possible solution for inter-monitor output equivalency, several sensor and digital signal processing specifications need to be similar between monitors to ensure equivalency. For example, activity counts from ActiGraph™ (ActiGraph™ Inc., Pensacola, FL, USA) monitors are not the same as those from the Actical (Phillips Respironics, Andover, MA, USA) monitor due to manufacturer specific signal processing. Another potential advantage of using raw acceleration is increased inter-monitor output equivalency through elimination of proprietary signal processing specifications used to derive activity counts. These techniques are becoming increasingly popular, as they provide improved estimates as compared to the traditional activity count cut-points. Availability of numerous signal features greatly enhances the potential of using complex machine learning techniques to accurately estimate physical activity and sedentary behavior. For example, an acceleration signal has time-domain (TD) and frequency-domain (FD) features that are used as prediction variables to estimate attributes of physical activity and sedentary behavior. The raw acceleration signal is a complex time-series waveform characterized by various features in multiple domains. There are several advantages in using raw acceleration to estimate physical activity. A key conclusion of the conference was to limit the use of proprietary activity counts and utilize raw acceleration to estimate physical activity. In 2009, the American College of Sports Medicine and the National Institutes of Health co-sponsored the ‘Objective Measurement of Physical Activity: Best Practices and Future Directions’ conference to update best practice recommendations for using wearable monitors to assess physical activity. Conclusions: It may be inappropriate to apply a model developed on the GENEA to predict activity type using GT3X+ data when input features are TD attributes of raw acceleration. Prediction accuracy was not compromised when interchangeably using FD models between monitors. Training the model using TD input features on the GENEA and applied to GT3X+ data yielded significantly lower (p < 0.05) prediction accuracy. Results: GENEA produced significantly higher (p < 0.05, 3.5 to 6.2%) mean VM than GT3X+ at all frequencies during shaker testing. Z-statistics were used to compare the proportion of accurate predictions from the GT3X+ and GENEA for each model. We compared activity type recognition accuracy between the GT3X+ and GENEA when the prediction model was fit using one monitor and then applied to the other. For the human testing protocol, random forest machine-learning technique was used to develop two models using frequency domain (FD) and time domain (TD) features for each monitor. A linear mixed model was used to compare the mean triaxial vector magnitude (VM) from the GT3X+ and GENEA at each oscillation frequency. Additionally, 10 participants (age = 23.8 ± 5.4 years) wore the GT3X+ and GENEA on the dominant wrist and performed treadmill walking (2.0 and 3.5 mph) and running (5.5 and 7.5 mph) and simulated free-living activities (computer work, cleaning a room, vacuuming and throwing a ball) for 2-min each. Methods: A GT3X+ and GENEA were oscillated in an orbital shaker at frequencies ranging from 0.7 to 4.0 Hz (ten 2-min trials/frequency) on a fixed radius of 5.08 cm. Purpose: To compare raw acceleration output of the ActiGraph™ GT3X+ and GENEA activity monitors.
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