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triaxial accelerometer data  (ActiGraph llc)

 
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    Structured Review

    ActiGraph llc triaxial accelerometer data
    Characteristics of Study Participants by Exposure to Improvements in Sidewalk Infrastructure at Baseline, Houston TRAIN Study, 2014–2016
    Triaxial Accelerometer Data, supplied by ActiGraph llc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/accelerometer+data/triaxial+accelerometer+data/pmc06543547-255-0-4
    Average 90 stars, based on 1 article reviews
    triaxial accelerometer data - by Bioz Stars, 2026-10
    90/100 stars

    Images

    1) Product Images from "Cost-Effectiveness of Improvements to the Built Environment Intended to Increase Physical Activity"

    Article Title: Cost-Effectiveness of Improvements to the Built Environment Intended to Increase Physical Activity

    Journal: Journal of physical activity & health

    doi: 10.1123/jpah.2018-0329

    Characteristics of Study Participants by Exposure to Improvements in Sidewalk Infrastructure at Baseline, Houston TRAIN Study, 2014–2016
    Figure Legend Snippet: Characteristics of Study Participants by Exposure to Improvements in Sidewalk Infrastructure at Baseline, Houston TRAIN Study, 2014–2016

    Techniques Used: Activity Assay

    Cost-Effectiveness Threshold Calculation by Reported and Calculated Estimates, Houston TRAIN Study, 2014–2017
    Figure Legend Snippet: Cost-Effectiveness Threshold Calculation by Reported and Calculated Estimates, Houston TRAIN Study, 2014–2017

    Techniques Used: Activity Assay

    Sensitivity Analysis of CER US$ and CEB US$ of Improvements to Sidewalk Infrastructure to Avert Health Care Costs Through Increases in Physical Activity, Houston TRAIN Study, 2014–2017
    Figure Legend Snippet: Sensitivity Analysis of CER US$ and CEB US$ of Improvements to Sidewalk Infrastructure to Avert Health Care Costs Through Increases in Physical Activity, Houston TRAIN Study, 2014–2017

    Techniques Used: Activity Assay

    Related Articles

    other:

    Article Title: Device-measured sedentary behavior in oldest old adults: A systematic review and meta-analysis
    Article Snippet: Studies that used activPAL, GENEActiv, and ActiGraph triaxial data reported sedentary estimates fairly similar to the uniaxial ActiGraph weighted mean (9.5–11.6 h/day) ( , , ).

    Article Title: The CNN Hip Accelerometer Posture (CHAP) Method for Classifying Sitting Patterns from Hip Accelerometers: A Validation Study
    Article Snippet: After partitioning both activPAL and triaxial ActiGraph data into nonoverlapping 10-s increments, features were extracted for each window.

    Article Title: Selecting a sleep tracker from EEG-based, iteratively improved, low-cost multisensor, and actigraphy-only devices.
    Article Snippet: Finally, for the Actigraph, triaxial accelerometer data were downloaded via a custom dock and aggregated into 1-minute epochs for sleep-wake scoring using the Actilife implementation of the Cole-Kripke algorithm.22,23 Oneminute epochs were scored from “Lights off” to “Lights on” and subsequently up-sampled to 30-second epochs to match the resolution of the PSG data.

    Article Title: CHAP-Adult: A Reliable and Valid Algorithm to Classify Sitting and Measure Sitting Patterns Using Data From Hip-Worn Accelerometers in Adults Aged 35+
    Article Snippet: Briefly, the input data (raw triaxial 30-Hz ActiGraph data) and the ground truth data (activPAL event-level data classified as sitting or not sitting using majority rules for each epoch) during awake wear time were split into 10-s nonoverlapping windows.

    Article Title: Detecting prolonged sitting bouts with the ActiGraph GT3X.
    Article Snippet: The aim of the present study was to develop an ActiGraph (waist-worn, 30Hz) posture classification to detect prolonged sitting bouts, and to compare the classification to proprietary ActiGraph data.

    Activity Assay:

    Article Title: Cost-Effectiveness of Improvements to the Built Environment Intended to Increase Physical Activity
    Article Snippet: .. Accelerometer-Derived Physical Activity Raw triaxial accelerometer data (Actigraph Corporation, Pensacola, FL) were sampled at 40 Hz and reintegrated and expressed as 60-second epochs prior to further processing. ..

    Sampling:

    Article Title: Impact of ActiGraph sampling rate on free-living physical activity measurement in youth.
    Article Snippet: ActiGraph sampling frequencies of more than 30Hzmay result in overestimation of activity counts in both children and adults, but research on free-living individuals has not included the range of sampling frequencies used by researchers.Objective.We compared countand rawacceleration-basedmetrics from free-living children and adolescents across a range of sampling frequencies.Approach.. Participants (n = 445; 10–15 years of age)wore an ActiGraph accelerometer for at least one 10 h day.. Vectormagnitude counts, mean amplitude deviation, monitor-independentmovement summary units, and activity intensity classified using sixmethods (four cut-points, two-regressionmodel, and artificial neural network)were compared between 30Hz and 60, 80, 90, and 100Hz sampling frequencies usingmean absolute differences, correlations, and equivalence testing.Main results.All outcomes were statistically equivalent, and correlation coefficients were 0.970.



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    Image Search Results


    Devices used in the study: A wearable camera was used to provide visual confirmation of human behavior performed in the wild. We utilized the smartwatch tri-axial accelerometer and gyroscope to estimate energy expenditure using our proposed algorithm and compared it against multiple algorithms , , , , – using the hip-worn ActiGraph.

    Journal: Scientific Reports

    Article Title: Developing and comparing a new BMI inclusive energy expenditure algorithm on wrist-worn wearables

    doi: 10.1038/s41598-025-99963-0

    Figure Lengend Snippet: Devices used in the study: A wearable camera was used to provide visual confirmation of human behavior performed in the wild. We utilized the smartwatch tri-axial accelerometer and gyroscope to estimate energy expenditure using our proposed algorithm and compared it against multiple algorithms , , , , – using the hip-worn ActiGraph.

    Article Snippet: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$K_m$$\end{document} motion estimates of accelerometer data segments are calculated by the Panasonic equation and aggregated based on the selected window size (Eq.).

    Techniques: