8/15/2023 0 Comments Genea actimeter![]() ![]() Postures such as seat-perching, kneeling and crouching were misclassified when compared to video observation. An excellent level of detection of standardised postures was demonstrated by the activPA元. Inter-device reliability was either good (ICC(1,1)>0.75) or excellent (ICC(1,1)>0.90) for all outcomes. For ADLs, sensitivity to stepping was very low for adults (40.4%) but higher for young people (76.1%). Jogging step detection accuracy reduced with increasing cadence >150stepsmin(-1). Sedentary and upright times for standardised activities were within ±5% of video observation as was step count (excluding jogging) for both adults and young people. Inter-device reliability was calculated between 4 monitors. Agreement, specificity and positive predictive value were calculated between activPA元 outcomes and the gold-standard of video observation. Twenty adults (median 27.6y IQR22.6y) and 8 young people (12.0y IQR4.1y) performed standardised activities and activities of daily living (ADL) incorporating sedentary, upright and stepping activity. This study reports an evaluation of the validity and reliability of the activPA元 monitor for the detection of posture and stepping in both adults and young people. 2012 44:86–89.Characterisation of free-living physical activity requires the use of validated and reliable monitors. ActiGraph and Actical physical activity monitors: A peek under the hood. Calibrating a novel multi-sensor physical activity measurement system. John D., Liu S., Sasaki J.E., Howe C.A., Staudenmayer J., Gao R.X., Freedson P.S. Physical activity classification using the GENEA wrist-worn accelerometer. Zhang S., Rowlands A.V., Murray P., Hurst T.L. An artificial neural network to estimate physical activity energy expenditure and identify physical activity type from an accelerometer. Staudenmayer J., Pober D., Crouter S., Bassett D., Freedson P. Assessment of physical activity using wearable monitors: recommendations for monitor calibration and use in the field. 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.įreedson P., Bowles H.R., Troiano R., Haskell W. 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. 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. To compare raw acceleration output of the ActiGraph™ GT3X+ and GENEA activity monitors.Ī 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. ![]()
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